hypothesis of generation

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In This Article Expand or collapse the "in this article" section The Concept of Generations

Introduction.

• Reference Works
• The Life Course and Generations
• Family Relations, Transmission, and Transfers
• Childhood and Generations
• Youth and Generations
• Generational Ambivalence
• Generations and Social Policy

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The Concept of Generations by Carolina Gutierrez Muñoz , Julia Brannen LAST MODIFIED: 23 June 2021 DOI: 10.1093/obo/9780199791231-0237

“Generation” is a polysynthetic concept with several meanings that captures the relation between the individual and the collective in both societal and kinship relations, the concept of the life course as individuals age, and collective existence as lived out in the company of time-based cohorts of contemporaries. Karl Mannheim, who presented himself as a sociologist of knowledge rather than history, developed the theory of generations in 1927, during a period of rapid modernization. The ascendancy of the concept today also reflects the rapidity of social change. A key example is the growing imbalance between older and younger populations in Western societies that raises issues of generational justice, especially at times when many governments have cut back public expenditures and welfare benefits. The concept of generations as originally proposed by Mannheim in his germinal essay, Mannheim 1952 (cited under General Overviews , [first translated into English in 1952]), has inspired generations of sociologists. However, the concept has been applied relatively little in empirical research. Mannheim conceived of generations as a problem for historical sociology. In his view, historical context had strong experiential effects on the formative years of a birth cohort (those who were born and grew up in the same period), effects which persist over the life course. In addition, Mannheim proposed the idea of generations as units, by which he meant the ways in which a birth or age cohort responds collectively to a set of social conditions and the ways in which each generations develops its own consciousness and sense of belonging and identity. At the same time, Mannheim was clear that generations were not subjected to the same experiences and that divisions of class and gender were significant. The concept of generations has also been the central territory of anthropology through its study of kinship relations. It is also a focus of demography in its study of populations; of psychology in its focus on the life span and child development; and of sociology in its focus on parenthood, household, and childhood. Within these disciplines and fields, there is considerable variation in the use of the term. Some researchers use it as a bridging concept; some, for example, contextualize family and kinship relations in historical context, while others focus exclusively on intergenerational relationships and processes of intergenerational transmission within families, taking little account of history. In this bibliography we will make certain broad distinctions. First, we consider the literature on the concept of historical generations and linked concepts of the life course and age. We then go on to consider the literature on families and kinship relations as they relate to intergenerational transmission in families and the sociologies of childhood and youth. We end with a section on intergenerational solidarity, fairness, and social policy. The literature includes papers with a conceptual slant, empirical research, textbooks, and works by organizations that produce relevant research.

General Overviews

The Mannheim’s 1927/28 (published in English in 1952) essay on “the problem of generations,” is the key reference point on the subject. The most celebrated exemplar of research in the field is Elder 1999 , particularly Elder’s study of those growing up during the Great Depression. Popescu 2019 starts from this work to offer an overview of how different disciplines have developed the concept. Pilcher 1994 shows how Mannheim’s work is not only relevant for the sociological studies of generations, but also for understanding the link between biography and history and between personal and social change, for which Mannheim does not receive sufficient credit. Marshall 1983 and Kertzer 1983 develop an in-depth sociological analysis of the concept, including a methodological discussion. In its focus on generational consciousness, Eyerman and Turner 1998 elucidates the concept’s importance to cultural sociology. Caballero and Baigorri 2019 show how the generations concept affords a conceptual tool that cuts across different disciplines. Purhonen 2016 draws on Bourdieu to critique “generationalism” that tends to caricature generations.

Caballero, Manuela, and Artemio Baigorri. “Glocalising the Theory of Generations: The Case of Spain.” Time and Society 28.1 (2019): 333–357.

DOI: 10.1177/0961463X18783374

Inspired particularly by the philosopher Ortega y Gasset and by Mannheim, this article shows that generations conceptually crosscuts different disciplines. Drawing on sociology, philosophy, and history, it applies the concept to the identification of six generations in Spain with different collective identities. The authors argue that these generations reflect the social changes in the history of Spain during the 20th century.

Elder, Glen H. Children of the Great Depression: Social Change in Life Experience . 25th anniversary ed. Boulder, CO: Westview Press, 1999.

The work of Glen Elder is both conceptual and empirical in connecting individuals to historical and socioeconomic contexts. In his most famous study, first published in 1974, 167 individuals born in 1920–1921 in Oakland, California, were followed through the 1960s. Here Elder assesses the influence of the Great Depression on the life course of his subjects over two generations.

Eyerman, Ron, and Bryan S. Turner. “Outline of a Theory of Generations.” European Journal of Social Theory 1.1 (1998): 91–106.

DOI: 10.1177/136843198001001007

This article applies Mannheim’s concept of generations to cultural sociology, bringing to bear Bourdieu’s concept of habitus—the emotions, attitudes, preferences, and dispositions that bind members of a cohort, and how generational cohorts exercise control, as in excluding other cohorts from scarce resources, material, and cultural. It is also concerned with the development of generational consciousness: how generational cohorts embody collective identities in response to formative events.

Kertzer, David. “Generation as a Sociological Problem.” Annual Review of Sociology 9.1 (1983): 125–149.

DOI: 10.1146/annurev.so.09.080183.001013

This paper examines the multiple meanings of the concept that arise from Mannheim’s legacy. It points to some major conceptual and methodological problems in its application: the vagueness of the concept’s operationalization, a failure to link different (family) generations in studying transmission between generations, and a lack of attention to life course phase.

Mannheim, Karl. “The Problem of Generations.” In Karl Mannheim: Essays . Edited by Paul Kecskemeti, 276–322. London: Routledge, 1952.

In this seminal work, first published in 1927/1928 and also republished in 1972, Mannheim defines generations as those born in the same year who share “a common location in the historical dimension of the social process” (p. 290). Mannheim also distinguishes between generations as actuality and unit; the latter is formed when peers are exposed to the same phenomenon and respond collectively in the same way. Cohort membership is subject therefore to historical context that shapes cultures and subjectivities.

Marshall, Victor W. “Generations, Age Groups and Cohorts: Conceputal Distinctions.” Canadian Journal on Aging/La Revue Canadienne Du Vieillissement 2.2 (1983): 51–62.

DOI: 10.1017/S0714980800015701

Drawing on Mannheim’s concept, Marshall defines related concepts for the sociological analysis of age relations. In this text he distinguishes between methodological and theoretical approaches to the phenomena of generations, cohorts, and age groups. The paper offers a good starting point for the understanding of relevant concepts related to the subject of generations.

Pilcher, Jane. “Mannheim’s Sociology of Generations: An Undervalued Legacy.” British Journal of Sociology 45.3 (1994): 481–495.

DOI: 10.2307/591659

Pilcher offers a close analysis of Mannheim’s seminal concept, suggesting that sociology has paid insufficient attention to it. She suggests that Mannheim’s work is not only relevant to understanding generations, but that it also contributes to several other sociological issues, such as concepts of time and the relationship between the biological and the social.

Popescu, Alexandra. “ The Brief History of Generation—Defining the Concept of Generation: An Analysis of Literature Review .” Journal of Comparative Research in Anthropology and Sociology 10.2 (2019): 15–30.

This paper gives an overview of the development of the concept. Starting with Mannheim’s work, it reviews philosophical, demographic, sociological, and psychological theorizations showing how the concept of generations has recently attracted the interest of researchers. It discusses empirical and theoretical papers that demonstrate a failure to reach a consensus in its definition. It offers a good synopsis of the state of the art.

Purhonen, Semi. “Generations on Paper: Bourdieu and the Critique of ‘Generationalism.’” Social Science Information 55.1 (2016): 94–114.

DOI: 10.1177/0539018415608967

This article provides an antidote to so-called generationalism. Drawing on the work of Bourdieu, who rarely used the concept, the paper offers a critique of the naïve and simplistic use of the concept; that is, that generations are somehow “ready-made.” It points to the importance that Bourdieu places on the articulation of a generational experience and the engagement of individuals within social space.

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Are Generational Categories Meaningful Distinctions for Workforce Management? (2020)

Chapter: 3 origin and use of generational theories, 3 origin and use of generational theories.

This chapter reviews the history and use of generational theories, as well as the creation of such generational labels as “baby boomers” and “millennials” that are used to describe people born in a certain time period or of a certain age. While the notion of generations has a long history of scholarly consideration, attention to generational differences among individuals has become increasingly prevalent over the past 20 years. An implicit assumption of generational thinking is that people who were born around the same time have similar values and attributes that differ from those of people born at a different time. This chapter provides background on how the concept of generation is used and has evolved in both the scientific and popular literature. The next chapter reviews the existing scientific literature related to generational differences in the workforce.

EARLY SOCIOLOGICAL THEORIES OF GENERATIONS

In the mid-1800s, Auguste Comte described social progress as the product of generational change ( Cours de Philosophie Positive , 1830–1840). He posited that just as individuals mature and change throughout their lives, societies progress through stages, which he termed “theological,” “metaphysical,” and “positive.” According to Comte, progress through these stages is driven by generational turnover, with each successive generation bringing new and innovative ideas and practices to replace those of older generations.

The modern scientific usage and understanding of the term “generations” can be traced back to sociologist Karl Mannheim’s The Problem of Generations (1952). Mannheim theorized that generations provide a basis for understanding social movements—how social change is possible while cultural traditions and identity are preserved. He identified five processes through which generations facilitate social change: (1) new participants in the cultural process emerge, (2) former participants in the cultural process disappear, (3) members of any generation can participate only for a limited time, (4) cultural heritage is transmitted from generation to generation, and (5) generational transitions are continuous. Subsequent sociological theories similarly highlighted the importance of generations in facilitating social change. Ryder (1965) , for instance, described the succession of birth cohorts (a construct similar to Mannheim’s formulation of generations) as a process of lending flexibility and providing new perspectives to address social problems.

According to Mannheim (1952) , generations are formed through two important elements: a common location in historical time, such that there are shared events and experiences, and an awareness of that historical location. Mannheim clarified that a generation is not a “concrete group” of people who share physical and social proximity and are aware of the existence of the other members. Thus, a generation is similar not to a club, in which one could identify who is in and out, but to a person’s social class. Notably, Mannheim stressed that birth year alone was insufficient to place a person in a specific generation; rather, the person needed to experience and participate in the defining events of the generation. He also noted that the same historical events will not affect people from different cultural backgrounds and social classes in the same way. To use a modern example, the destruction of the World Trade Center Towers in New York City on September 11, 2001, might be expected to have significantly affected the thinking and attitudes of Americans who were alive to witness these events. But such an event might not be a defining moment for people living in countries in which terrorism is more frequent than is the case in the United States, and it would be expected to affect the thinking and attitudes of New Yorkers differently from those in other parts of the country. Even within the United States, people of different education levels, wealth, and culture may have experienced or interpreted these events differently.

Both Mannheim (1952) and Ryder (1965) rejected the idea that generations emerge at regularly spaced intervals, noting that the rhythm of generations depends on the timing of historical, social, and cultural events that affect people’s experiences. Ryder further noted that historical events occurring during young adulthood are particularly influential, as young people are “old enough to participate directly in the movements impelled by change, but not old enough to have become committed to an occupation, a

residence, a family of procreation or a way of life” ( Ryder, 1965 , p. 848). Later generational theories in sociology highlighted the importance of not only historical events that happen during especially salient developmental stages, but also significant culturally bound life stages (e.g., education, marriage, building family, working years) that influence goals and values ( Riley, 1973 , 1987 ). These sociological theories of generations did not focus on understanding individual behavior, but on an aggregate concept of generations as facilitating social change ( Rudolph and Zacher, 2017 ).

Another major figure in the sociological tradition is Glen Elder. Building on his large-scale longitudinal studies of child and adult development produced during the early to mid-1900s ( Elder, 1974 , 1985 ), Elder formulated a life course perspective ( Elder, 1998 ; also see Elder, Kirkpatrick-Johnson, and Crosnoe, 2003 ) positing the process through which social and historical contexts, particularly during childhood and adolescence, affect the trajectory of an individual’s development through the life span. Specifically, he argued that “historical events and individual experience are connected through the family and the ‘linked’ fates of its members” ( Elder, 1998 , p. 3). That is, an individual’s childhood and adolescent experiences are critically important in setting the stage for the subsequent developmental adult trajectory. In contrast to sociological traditions emphasizing the impact of events on social change, Elder focused on the mechanisms and consequences of social and historical context with respect to an individual’s values and transition into adult roles, most notably those related to work.

Elder argued that an individual’s family resources, values, and strategies for adapting to the broader external context exert a stronger effect on that individual than the historical context per se. Thus, within a generation or cohort defined by historical period, one could expect great heterogeneity within that population segment as a function of both more proximal familial and social interdependencies. In a similar vein, MacLean and Elder’s (2007) review of the literature shows that the effects of different historical periods on military service are moderated by person-related attributes (e.g., family and friend resources).

Elder’s life course perspective extended the sociological approach to generations in two ways. First, by focusing on the individual and lifespan development, his ideas helped shift attention from impacts on social change to impacts on individual behavior and adult development. Second, along with the work of Riley (1987) and others, Elder’s concept of “linked lives” emphasized a possible process or mechanisms by which unique events that often characterize a generation come to affect an individual’s values and behavior ( Elder, Kirkpatrick-Johnson, and Crosnoe, 2003 ). However, Elder consistently noted that the variability associated with different “linked lives” in turn yields nontrivial variability in how individuals who live through a similar time period develop different values, interests, and occupational trajectories.

INFLUENTIAL POPULAR THEORY OF GENERATIONS

Like the researchers of the sociological theories described above, Strauss and Howe (1991) focus on generations in the aggregate in their popular book Generations: The History of America’s Future 1584 to 2069 . Their approach, however, departs from scientific theories in two important ways. First, they delineate a specific span of time—about 20 years—associated with the emergence of a generation. Second, they posit that four generational personalities (idealist, reactive, civic, and adaptive) emerge every 20 years or so in a cyclical pattern that repeats roughly every 80 years, driven by a generational reaction to the prior generation. According to Strauss and Howe, for example, idealists are an indulged and narcissistic generation of adults who raise a generation of underprotected and alienated reactives; who then raise team-oriented, overprotected but society-minded civics; who then raise an adaptive generation that comes of age in a time of crisis with an ethos of personal sacrifice. Although this pattern supposedly repeats every 80 years or so, the authors allow for significant historical events, such as the Civil War, to disrupt the cycle.

Although their work is thought-provoking, Strauss and Howe (1991) present essentially no empirical evidence for their theoretical perspective. Rather, they highlight individual case studies in making their claims regarding prototypical representatives of each generation’s personality type throughout history. Although these case studies are compelling, they were selected specifically to provide examples of prototypical members of a generation (i.e., selection bias). One might also easily provide counter examples of people within a cohort who do not exhibit the prototypical traits associated with a generation or who exhibit traits belonging to a different generation (e.g., people who should be adaptive given their birth year but who exhibit the traits of an idealist). Nonetheless, the work of Strauss and Howe has been highly influential with respect to both their thinking about the timing of the emergence of generations (i.e., every 20 years or so) and their labels for generations, which have influenced popular ideas about generational differences ( Brooks, 2000 ).

GENERATIONAL LABELS

In the modern era, generations are often described by labels and defined as a group born between specific years—for example, the “millennial” generation, born roughly in the 1980s and 1990s. Generations tend to be assigned these labels through a somewhat messy process led by journalists, magazine editors, advertising executives, and the general public ( Raphelson, 2014 ). Usually, a variety of labels are used until one sticks in the common vernacular, because of either a seminal book or article, a historical event,

or simply general consensus. As noted above, Strauss and Howe’s (1991) work was highly influential with respect to how it described and labeled the various generations in America. The labels they used for each of the generations have become—with one exception—the common vernacular in discussions about generations. Although they did not create the terms, their labels of the “silent,” “boomer,” and “millennial” generations have stuck.

The term “silent generation” notably appeared in a 1951 article in Time magazine, but it is unclear when the term originated. 1 The labels “baby boomers” and “millennials” are linked to historical events, but it is also not entirely clear who created these terms or how they came to be the “official” names for those generations. “Baby boomer” was given to the generation of individuals born between mid-1946 and mid-1964, after World War II ( Hogan, Perez, and Bell, 2008 ). The term denotes the baby boom in the United States following the war, when the birth rate rose significantly and then fell. The label was notably used in a 1963 newspaper article about the new wave of college applicants. 2 The term “millennials,” referring to the turning of the millennium, appears to have first been coined by Strauss and Howe.

“Generation X” (called the “13ers” by Strauss and Howe) is a striking example of how generational labels are largely the product of popular culture. Photographer Robert Capa first used the title Generation X in the 1950s for a photo series of young people after World War II. In 1964, a collection of interviews with teenagers was published in a book titled Generation X ( BBC News, 2014 ). The phrase was again used by musician Billy Idol in the early 1970s for his punk rock band. The label finally achieved its modern meaning after being popularized in a 1991 novel by Douglas Coupland, a Canadian author and artist. Interestingly, Coupland’s choice of the title Generation X was meant to signify that this generation did not want to be defined ( Raphelson, 2014 ).

Finally, the popular label “generation Z” is used by many writers for the youngest named generation. It recently appears to have won out over other contenders ( Dimock, 2019 ), such as “postmillennials” ( Fry and Parker, 2018 ), “iGen” ( Twenge, 2018 ), and “homelanders” ( Howe and Strauss, 2007 ).

___________________

1 See the “People: The Younger Generation” piece in Time (November 5, 1951) at http://content.time.com/time/subscriber/article/0,33009,856950,00.html .

2 See clip from Daily Press (Newport News, VA, January 28, 1963) at https://www.newspapers.com/clip/19690752/daily_press .

WIDESPREAD USE OF GENERATIONAL TERMINOLOGY

The topic of generations and generational differences is discussed in a wide variety of contexts, including the popular press, business and human resources advice, and research, 3 both in the United States and internationally. 4 In these contexts, authors and consultants make use of the generational labels and associated birth years as an easy way to categorize groups of people, primarily by age (see Table 3-1 for examples). That is, generational categories are used commonly as a heuristic to reference a group of people around a certain age.

The use of generational categories in discussions about workforce management has become particularly prominent in the past 20 years in the popular press and in businesses and human resources advice. This growth in the use of these categories with respect to workforce management suggests anecdotally that employers are taking a serious look at generational differences. While the committee could find no evidence of enacted employment policies and practices directly tied to generational issues, we did find opinion pieces, commissioned reports, and training aimed at addressing personnel concerns from a generational perspective (see Box 3-1 and the discussion below).

Popular Press

It was beyond the scope of the committee’s charge to review comprehensively the coverage of generational issues in the popular press. In conducting this study, however, we could not help but notice the vast amount and array of advice on generational issues in the workforce that is available to the public. Here, we offer our observations after reading many of these articles.

3 Government agencies that collect population-level data, often used by researchers and the public, sometimes report these data by age groups using generational categories. Notable agencies include both the Department of Labor ( BLS, 2019c ) and the Census Bureau ( U.S. Census Bureau, 2015 ).

4 Given the claim that generations are formed through shared experiences of events that occur during a developmentally significant period (i.e., late adolescence/early adulthood), it is curious that labels for generations that were generated in the United States have also been used to describe and explain behavior for people and cultures outside of the United States, who arguably do not share the same cultural experiences. For example, Pew Research draws the line between millennials and generation Z as 1996 based on the timing of a few events: the terrorist attacks of September 11, 2001, U.S. involvement in the wars in Iraq and Afghanistan, the 2008 election and economic recession, and the adoption of such technologies as the smartphone ( Dimock, 2019 ). However, these events, which mark the lines between generations in the United States, would have been experienced differently or not at all in other countries around the world.

TABLE 3-1 Illustration of Different Labels for Generational Categories and Associated Birth Years from Various Sources

* See FAQs What are birth year cutoffs? at http://www.jeantwenge.com/faqs .

Most articles in the popular press, as well as television news stories, that refer to generations report on the likes, dislikes, habits, and attributes of various generations. For example, a Google news search of the word “millennial” yields more than 50 million results, with articles and stories from the Los Angeles Times , Forbes , NBC News, The Washington Post , and many other sources. The topics of these articles and stories range from a generation’s thoughts about religion and pets to their values and behaviors in the workplace. Much of the discussion of these topics in the popular press is descriptive in nature and reports demographic statistics, such as the percentage of millennials in the workforce.

Most of the research and data referenced, if any, in these pieces is cross-sectional, drawn from either published cross-sectional research or some informal survey conducted by the reporting outlet. Findings from these surveys can be misleading because the findings are often reported as generational characteristics but may just be reflecting age differences at the time, and because of the small convenience samples on which the findings are based are not representative of the generations the surveys seek to characterize. Issues of cross-sectional designs and representativeness are discussed further in Chapter 4 .

As the group of individuals known as the millennials have aged (now in their 20s and 30s) and become a dominant proportion of the global population, an entire industry on generational differences has developed in an effort to understand the expectations of this target group and capitalize on those expectations economically. In many cases, generational labels are presented as heuristics with which to better understand differences in work-related values and other attitudes of different age groups. Many of these articles use headlines to highlight large differences among generations, but the further one reads, the more generational differences are described as somewhat trivial. Moreover, many authors include caveats that highlight either heterogeneity within generations (e.g., not all millennials are always on their cellphones) or evidence showing similarities between generations (e.g., millennials and generation Xers use their cellphones equally). The committee observed trends in the popular press of “myth busting” some generational claims, reporting discord among individuals who feel they do not belong to or identify with common stereotypes of a given generation, as well as growing instability in the concept of easily generalizable groups based on either birth year or shared historical events (e.g., Casey, 2016 ; Wall Street Journal , 2017 ).

Business and Human Resources Advice

A plethora of discussion and advice concerning generations in the workplace is available in books, magazine and newspaper articles, blogs, and surveys and from a growing number of consultants who provide training and perspective on these issues. For example, Deloitte, a large international consulting firm, conducts an annual “Millennial Global Survey” to look at attitudes, perceptions, and characteristics of young people around the world. 5 This survey is administered to around 10,000 people from dozens of countries, all of whom were born between 1983 and 1994, and includes questions related to work, the economy, technology, and similar issues. Business-centered organizations and publications have numerous articles and courses on managing different generations. For example, there are hundreds of articles about generations in the workplace on the website of the Society for Human Resource Management (SHRM), and the American Management Association offers articles and several courses about managing

5 The Deloitte Global Millennial Survey uses the same birth cohort (1983–1994) to examine “millennials” from 42 countries, including Nigeria, Australia, Malaysia, China, and South Africa. Deloitte is not alone in applying U.S.-based generational categories to the global population (despite the definitions linking generations to significant social events and lack of relevance to international populations). Numerous popular articles report on work-related characteristics of people around the world using such U.S. categories as generation X, millennial, and generation Z ( Bresman and Rao, 2017 ; Miller and Lu, 2018 ).

generations. There are even a handful of business school courses centered around issues related to generations.

Much of this advice focuses on the challenges of managing workers of multiple ages in the workplace and often includes broad descriptions of each generation, with little reference to evidence supporting these descriptions. For example, an article on the American Management Association website describes the silent generation as loyal and dedicated, baby boomers as distrusting authority and having a sense of entitlement in the workplace, generation X’ers as independent and placing a lower priority on work, and millennials as resilient and team-centric ( Jenkins, 2019 ). While the article does not explicitly acknowledge potential heterogeneity within generational groups, it goes on to say that employers should seek to “create a respectful, open and inclusive environment where workers of all ages and cultural backgrounds can share who they are without fear of being judged, ‘fixed,’ or changed.” Other articles use headlines that appear to claim large differences among generations, but the articles themselves often state that workers of all ages generally want the same things out of work. For example, the main message of an SHRM article titled “Employers Say Accommodating Millennials Is a Business Imperative” is that workplaces in which flexibility, work–life balance, and wellness are emphasized are able to attract and retain workers of all ages ( Wright, 2018 ). Taken as a whole, this advice often is self-contradicting, identifies similar values among workers (e.g., seeking respect on the job and personal growth), or boils down to the assertion that workers should be assessed individually and not by generational group.

As the idea of generational differences has grown in popularity, so, too, has the number of studies in this area by think tanks, scientific organizations, and researchers. The Pew Research Center, a nonpartisan think tank, collects and analyzes data on such issues as work attitudes, use of technology, and economics; the Pew website lists hundreds of articles relating to age and generation, some of which look at data using age categories and some of which use generational categories. 6 The American Psychological Association (2017) conducts an annual survey on Work and Well-Being, which compares generational groups on a number of work outcomes, including work stress, job satisfaction, and plans to change jobs. The Society for Industrial and Organizational Psychology (SIOP) has published white papers on the millennial culture in the workplace (e.g., Graen and Grace,

6 See resources from the Pew Research Center at https://www.pewresearch.org/topics/generations-and-age .

2015 ) and has featured such talks as “What Millennials Want from Work” at its annual conferences. The information provided by these scientific organizations, however, also tends to include perspectives that examine the quality of evidence behind generational stereotypes (see, e.g., the September 2015 special issue of Industrial and Organizational Psychology: Perspectives on Science and Practice , Volume 8, Issue 3).

Psychological research on generations typically attempts to link such individual outcomes as work-related values, attitudes, and behaviors to generation membership. Notably, this research tends to use existing generational categories (see Table 3-1 ) to define groups in its samples. The committee was tasked to review the body of literature on generational attitudes and behaviors in workforce management and employment practices, and we identified more than 500 research articles on the topic. Appendix A details the committee’s literature review, while Chapter 4 presents the findings and conclusions that resulted from the review.

Research has explored the concept of generations for decades as a way to understand social change. New approaches have taken ideas from the sociological literature and applied them to understanding attitudes and behaviors of individuals. The idea of categorizing people by their generation became popular, and generational terminology has now taken hold in the common vernacular. Numerous articles and discussions and a growing industry of consultants and management resources focus on generational differences and the management of generations in the workplace, and employers and managers are being urged to make decisions and develop policies based on generational differences. However, careful examination of the empirical support for generational differences is essential before significant, costly decisions are made. Findings from existing generational research on work-related outcomes are examined in the next chapter.

Conclusion 3-1: As popular use of generational terminology expanded, the concept of generations developed decades ago in sociology to understand social change has taken a new research trajectory in an effort to classify individual differences in values, attitudes, and behaviors, notably those in relation to work. This new trajectory has been fueled in the past 20 years by greater attention to changing workforce demographics and the potential utility of understanding generational differences with respect to work.

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Headlines frequently appear that purport to highlight the differences among workers of different generations and explain how employers can manage the wants and needs of each generation. But is each new generation really that different from previous ones? Are there fundamental differences among generations that impact how they act and interact in the workplace? Or are the perceived differences among generations simply an indicator of age-related differences between older and younger workers or a reflection of all people adapting to a changing workplace?

Are Generational Categories Meaningful Distinctions for Workforce Management? reviews the state and rigor of the empirical work related to generations and assesses whether generational categories are meaningful in tackling workforce management problems. This report makes recommendations for directions for future research and improvements to employment practices.

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1 Imagining Generations and Social Change

• Published: October 2018
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This chapter explains the generational theory of Karl Mannheim and enumerates five challenges that scholars face when studying generational change. It shows how these changes have impeded social scientific research on generations and why research on Millennials, Generation X, and other broad cohorts is flawed from its first premises. To counteract the existing generational mythology, this chapter outlines what is required to produce social scientific studies of generational change. Recent scholarship on the “social generation” provides a theoretical and methodological opening for finally solving Mannheim’s “problem of generations.” Building on cross-disciplinary scholarship on the cognitive and cultural dimensions of the process of imagination, this chapter argues that the “social imagination” is the key concept that helps explain how public opinion about gay marriage changed.

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The Histories and Theories Generational Cycles: The Fourth Turning Reviewed

This paper provides an overview the historical precedence and theoretical underpinnings of the generational cycle theory of William Strauss and Neil Howe, best known as the Fourth Turning. It also addresses its relevance to the history of the United States and its living generations.

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3.1 Spontaneous Generation

Learning objectives.

By the end of this section, you will be able to:

• Explain the theory of spontaneous generation and why people once accepted it as an explanation for the existence of certain types of organisms
• Explain how certain individuals (van Helmont, Redi, Needham, Spallanzani, and Pasteur) tried to prove or disprove spontaneous generation

Clinical Focus

Barbara is a 19-year-old college student living in the dormitory. In January, she came down with a sore throat, headache, mild fever, chills, and a violent but unproductive (i.e., no mucus) cough. To treat these symptoms, Barbara began taking an over-the-counter cold medication, which did not seem to work. In fact, over the next few days, while some of Barbara’s symptoms began to resolve, her cough and fever persisted, and she felt very tired and weak.

• What types of respiratory disease may be responsible?

Jump to the next Clinical Focus box

Humans have been asking for millennia: Where does new life come from? Religion, philosophy, and science have all wrestled with this question. One of the oldest explanations was the theory of spontaneous generation, which can be traced back to the ancient Greeks and was widely accepted through the Middle Ages.

The Theory of Spontaneous Generation

The Greek philosopher Aristotle (384–322 BC) was one of the earliest recorded scholars to articulate the theory of spontaneous generation , the notion that life can arise from nonliving matter. Aristotle proposed that life arose from nonliving material if the material contained pneuma (“spirit” or “breath”). As evidence, he noted several instances of the appearance of animals from environments previously devoid of such animals, such as the seemingly sudden appearance of fish in a new puddle of water. 1

This theory persisted into the 17th century, when scientists undertook additional experimentation to support or disprove it. By this time, the proponents of the theory cited how frogs simply seem to appear along the muddy banks of the Nile River in Egypt during the annual flooding. Others observed that mice simply appeared among grain stored in barns with thatched roofs. When the roof leaked and the grain molded, mice appeared. Jan Baptista van Helmont , a 17th century Flemish scientist, proposed that mice could arise from rags and wheat kernels left in an open container for 3 weeks. In reality, such habitats provided ideal food sources and shelter for mouse populations to flourish.

However, one of van Helmont’s contemporaries, Italian physician Francesco Redi (1626–1697), performed an experiment in 1668 that was one of the first to refute the idea that maggots (the larvae of flies) spontaneously generate on meat left out in the open air. He predicted that preventing flies from having direct contact with the meat would also prevent the appearance of maggots. Redi left meat in each of six containers ( Figure 3.2 ). Two were open to the air, two were covered with gauze, and two were tightly sealed. His hypothesis was supported when maggots developed in the uncovered jars, but no maggots appeared in either the gauze-covered or the tightly sealed jars. He concluded that maggots could only form when flies were allowed to lay eggs in the meat, and that the maggots were the offspring of flies, not the product of spontaneous generation.

In 1745, John Needham (1713–1781) published a report of his own experiments, in which he briefly boiled broth infused with plant or animal matter, hoping to kill all preexisting microbes. 2 He then sealed the flasks. After a few days, Needham observed that the broth had become cloudy and a single drop contained numerous microscopic creatures. He argued that the new microbes must have arisen spontaneously. In reality, however, he likely did not boil the broth enough to kill all preexisting microbes.

Lazzaro Spallanzani (1729–1799) did not agree with Needham’s conclusions, however, and performed hundreds of carefully executed experiments using heated broth. 3 As in Needham’s experiment, broth in sealed jars and unsealed jars was infused with plant and animal matter. Spallanzani’s results contradicted the findings of Needham: Heated but sealed flasks remained clear, without any signs of spontaneous growth, unless the flasks were subsequently opened to the air. This suggested that microbes were introduced into these flasks from the air. In response to Spallanzani’s findings, Needham argued that life originates from a “life force” that was destroyed during Spallanzani’s extended boiling. Any subsequent sealing of the flasks then prevented new life force from entering and causing spontaneous generation ( Figure 3.3 ).

Check Your Understanding

• Describe the theory of spontaneous generation and some of the arguments used to support it.
• Explain how the experiments of Redi and Spallanzani challenged the theory of spontaneous generation.

Disproving Spontaneous Generation

The debate over spontaneous generation continued well into the 19th century, with scientists serving as proponents of both sides. To settle the debate, the Paris Academy of Sciences offered a prize for resolution of the problem. Louis Pasteur , a prominent French chemist who had been studying microbial fermentation and the causes of wine spoilage, accepted the challenge. In 1858, Pasteur filtered air through a gun-cotton filter and, upon microscopic examination of the cotton, found it full of microorganisms, suggesting that the exposure of a broth to air was not introducing a “life force” to the broth but rather airborne microorganisms.

Later, Pasteur made a series of flasks with long, twisted necks (“swan-neck” flasks), in which he boiled broth to sterilize it ( Figure 3.4 ). His design allowed air inside the flasks to be exchanged with air from the outside, but prevented the introduction of any airborne microorganisms, which would get caught in the twists and bends of the flasks’ necks. If a life force besides the airborne microorganisms were responsible for microbial growth within the sterilized flasks, it would have access to the broth, whereas the microorganisms would not. He correctly predicted that sterilized broth in his swan-neck flasks would remain sterile as long as the swan necks remained intact. However, should the necks be broken, microorganisms would be introduced, contaminating the flasks and allowing microbial growth within the broth.

Pasteur’s set of experiments irrefutably disproved the theory of spontaneous generation and earned him the prestigious Alhumbert Prize from the Paris Academy of Sciences in 1862. In a subsequent lecture in 1864, Pasteur articulated “ Omne vivum ex vivo ” (“Life only comes from life”). In this lecture, Pasteur recounted his famous swan-neck flask experiment, stating that “…life is a germ and a germ is life. Never will the doctrine of spontaneous generation recover from the mortal blow of this simple experiment.” 4 To Pasteur’s credit, it never has.

• How did Pasteur’s experimental design allow air, but not microbes, to enter, and why was this important?
• What was the control group in Pasteur’s experiment and what did it show?
• 1 K. Zwier. “Aristotle on Spontaneous Generation.” http://www.sju.edu/int/academics/cas/resources/gppc/pdf/Karen%20R.%20Zwier.pdf
• 2 E. Capanna. “Lazzaro Spallanzani: At the Roots of Modern Biology.” Journal of Experimental Zoology 285 no. 3 (1999):178–196.
• 3 R. Mancini, M. Nigro, G. Ippolito. “Lazzaro Spallanzani and His Refutation of the Theory of Spontaneous Generation.” Le Infezioni in Medicina 15 no. 3 (2007):199–206.
• 4 R. Vallery-Radot. The Life of Pasteur , trans. R.L. Devonshire. New York: McClure, Phillips and Co, 1902, 1:142.

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• Authors: Nina Parker, Mark Schneegurt, Anh-Hue Thi Tu, Philip Lister, Brian M. Forster
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Generational Theory

Generational Change Theory Generational Demographics

Universal Design for Learning argues that difference and disability are systemic phenomena that arise in the interaction of agent and context. Consequently, educational contexts must be designed to be flexible in order to emphasize ability – by providing multiple means of engagement, representation, action, and expression.

Learning Design encompasses all activities associated with the development of practices, tasks, resources, and technologies intended to support meaningful and engaged learning in a particular situation. Learning Design discourses are the teaching-focused complement of Emergent Complexity Discourses, which are more focused on the adaptive dynamics of learning systems than on the pragmatics of influencing learning.

Principal Metaphors

• Knowledge is … temporally and culturally distinct mode of being
• Knowing is … embodying generationally specific traits
• Learner is … a generational representative
• Learning is … acculturating
• Teaching is … n/a (largely irrelevant)

Academic Treatments of Generational Theory

• Strauss–Howe Generational Theory (Fourth Turning Theory) (William Strauss & Neil Howe, 1990s) – an argument that history unfolds according to recurring generational archetypes, based on analyses of American and global history
• Theory of Generations (Sociology of Generations) (Karl Mannheim, 1920s) – defining a “generation” in terms of individuals who have all experienced a historical event that is associated with significant social and/or cultural transformation

Popular Treatments of Generational Theory

• Lost Generation (coined by Ernest Hemingway, 1920s) – born early 1880s–1900 , entering adulthood during World War I, followed by the Spanish Flu Epidemic, and then by the “Roaring Twenties.” The name is a reference to the disorientation of persons and peoples across these tumultuous times in global politics and national culture.
• Greatest Generation (G.I. Generation) (term popularized by Tom Brokaw, 1990s) – born 1901–1927 , and defined according to the age of most soldiers in World War II
• Silent Generation (Lucky Few Generation) – born 1928–1945 , this cohort came of age during a relatively peaceful era (compared to previous generations) and was is associated with rapid growth in prosperity in the western world
• Baby Boomers (Me Generation) – born 1946–1964 , in a significant baby boom following World War II, noted for taking advantage of post-world-war prosperity, for intense self-interest, and for mounting opposition to oppressive and biased policies and structures, came to age prior to the digital revolution
• Generation Jones (coined by Jonathan Pontell, 2000s) – microgeneration born 1956–1965 , during a period of continuing economic boom and high birth rates. Owing to a prosperous childhood in the 1960s and an austere adolescence in the 1970s, members are characterized as pragmatic, hardworking, adaptable, and inventive. The term is derived from a phrase popular in the 1970s, “Keeping up with the Joneses,” which is suggested to be reflective of Generation Jones desires for grander possessions and experiences.
• Generation X (Gen X; Latchkey Generation; MTV Generation; Baby Bust Generation) – born 1965–1980 , associated with a drop in birth rates after the post-war baby boom and with formative years when digital technologies were proliferating, noted for comfort with and savvy around technology
• Xennials (Oregon Trail Generation, which is named after an educational video game ) –microgeneration born mid-1970s–mid-1980s , experiencing an “analog childhood” and a “digital young adulthood” – thus situated differently in relation to digital technologies than both Generation X and Millennials , yet sharing qualities with both.
• Geriatric Millennials  (Erica Dhawan, 2020s) –  Millennials  born in the early 1980s, who were the first born into homes with personal computers. The suggestion is that they are uniquely comfortable with both analog and digital technologies.
• TikTok Generation – a term sometimes associated with  Generation Z , prompted by claims in the early 2020s that over half this group used the app and, conversely, more than half the app users came from the group
• Zombie Generation (P. Chevyetski, 2010s) ­– a play on the “Z” of Generation Z , intended to characterize a large portion of this generation as technology-addicted zombies
• Generation Alpha (Gen Alpha) – born from the early-2010s through the mid-to-late 2020s , and so not yet associated with any essentialized markers
• Next-Gens – a relative term, referring to the “next generation” – i.e., the one that succeeded or will succeed the generation under discussion

Subdiscourses and Constructs Associated with Generational Theory

• Digital Divide (1990s) – a descriptive device invoked in comparisons of groups who have access to and benefit to digital technologies and those who do not
• Digital Immigrants (Marc Prensky, 2000s) – descriptor applied to one whosesignificant encounters with digital technologies began after the formative years (contrast: Digital Natives )
• Digital Natives (Marc Prensky, 2000s) – descriptor applied to one who has been immersed in digital technologies from birth (contrast: Digital Immigrants )
• Generation Gap (Generational Gap) – a catch-all construct, originally applied to in the 1960s to label perceived differences between Baby Boomers and the Silent Generation , currently generalized to refer to any perceived differences in beliefs, values, preferences, lifestyles, and imagined necessities between two age groups
• Generationism – the conviction that one generation is better than another
• Intergenerationality (Inter-Generational Contract) – the interactions between generations, typically examined through lenses of conflict, privilege, and power
• Transgenerational Design (James Pirkl, 1980s) – consistent with Learning Design , and highly resonant with Universal Design for Learning , this discourse focuses on products and contexts that can accommodate the widest possible range of consumers, especially across age groups

Authors and/or Prominent Influences

Status as a theory of learning, status as a theory of teaching, status as a scientific theory, subdiscourses:.

• Baby Boomers (Me Generation)
• Digital Divide
• Digital Immigrants
• Digital Natives
• Generation Alpha (Gen Alpha)
• Generation Gap (Generational Gap)
• Generation Jones
• Generation X (Gen X; Latchkey Generation; MTV Generation; Baby Bust Generation)
• Generation Z (Gen Z, iGeneration, Post-Millennials, Homeland Generation; Zoomers)
• Generationism
• Geriatric Millennials
• Greatest Generation (G.I. Generation)
• Impulse Hypothesis (Hans Jaeger, 1980s)
• Intergenerationality (Inter-Generational Contract)
• Lost Generation
• Millennials (Generation Y; Gen Y; Gen Me; Gen We; Echo Boomers)
• Pulse-Rate Hypothesis
• Silent Generation (Lucky Few Generation)
• Strauss–Howe Generational Theory (Fourth Turning Theory)
• Theory of Generations (Sociology of Generations)
• TikTok Generation
• Transgenerational Design
• Xennials (Oregon Trail Generation)
• Zombie Generation

Map Location

Please cite this article as: Davis, B., & Francis, K. (2023). “Generational Theory” in Discourses on Learning in Education . https://learningdiscourses.com.

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3 1.2 Spontaneous Generation

Learning objectives.

• Explain the theory of spontaneous generation and why people once accepted it as an explanation for the existence of certain types of organisms
• Explain how certain individuals (van Helmont, Redi, Needham, Spallanzani, and Pasteur) tried to prove or disprove spontaneous generation

CLINICAL FOCUS: Part 1

Barbara is a 19-year-old college student living in the dormitory. In January, she came down with a sore throat, headache, mild fever, chills, and a violent but unproductive (i.e., no mucus) cough. To treat these symptoms, Barbara began taking an over-the-counter cold medication, which did not seem to work. In fact, over the next few days, while some of Barbara’s symptoms began to resolve, her cough and fever persisted, and she felt very tired and weak.

• What types of respiratory disease may be responsible?

Jump to the next Clinical Focus box

Humans have been asking for millennia: Where does new life come from? Religion, philosophy, and science have all wrestled with this question. One of the oldest explanations was the theory of spontaneous generation, which can be traced back to the ancient Greeks and was widely accepted through the Middle Ages.

The Theory of Spontaneous Generation

The Greek philosopher Aristotle (384–322 BC) was one of the earliest recorded scholars to articulate the theory of spontaneous generation, the notion that life can arise from nonliving matter. Aristotle proposed that life arose from nonliving material if the material contained pneuma (“vital heat”). As evidence, he noted several instances of the appearance of animals from environments previously devoid of such animals, such as the seemingly sudden appearance of fish in a new puddle of water. [1]

This theory persisted into the 17th century, when scientists undertook additional experimentation to support or disprove it. By this time, the proponents of the theory cited how frogs simply seem to appear along the muddy banks of the Nile River in Egypt during the annual flooding. Others observed that mice simply appeared among grain stored in barns with thatched roofs. When the roof leaked and the grain moulded, mice appeared. Jan Baptista van Helmont , a 17th century Flemish scientist, proposed that mice could arise from rags and wheat kernels left in an open container for 3 weeks. In reality, such habitats provided ideal food sources and shelter for mouse populations to flourish.

However, one of van Helmont’s contemporaries, Italian physician Francesco Redi (1626–1697), performed an experiment in 1668 that was one of the first to refute the idea that maggots (the larvae of flies) spontaneously generate on meat left out in the open air. He predicted that preventing flies from having direct contact with the meat would also prevent the appearance of maggots. Redi left meat in each of six containers ( Figure 1.10 ). Two were open to the air, two were covered with gauze, and two were tightly sealed. His hypothesis was supported when maggots developed in the uncovered jars, but no maggots appeared in either the gauze-covered or the tightly sealed jars. He concluded that maggots could only form when flies were allowed to lay eggs in the meat, and that the maggots were the offspring of flies, not the product of spontaneous generation.

In 1745, John Needham (1713–1781) published a report of his own experiments, in which he briefly boiled broth infused with plant or animal matter, hoping to kill all preexisting microbes. [2] He then sealed the flasks. After a few days, Needham observed that the broth had become cloudy and a single drop contained numerous microscopic creatures. He argued that the new microbes must have arisen spontaneously. In reality, however, he likely did not boil the broth enough to kill all preexisting microbes.

Lazzaro Spallanzani (1729–1799) did not agree with Needham’s conclusions, however, and performed hundreds of carefully executed experiments using heated broth. [3] As in Needham’s experiment, broth in sealed jars and unsealed jars was infused with plant and animal matter. Spallanzani’s results contradicted the findings of Needham: Heated but sealed flasks remained clear, without any signs of spontaneous growth, unless the flasks were subsequently opened to the air. This suggested that microbes were introduced into these flasks from the air. In response to Spallanzani’s findings, Needham argued that life originates from a “life force” that was destroyed during Spallanzani’s extended boiling. Any subsequent sealing of the flasks then prevented new life force from entering and causing spontaneous generation ( Figure 1.11 ).

• Describe the theory of spontaneous generation and some of the arguments used to support it.
• Explain how the experiments of Redi and Spallanzani challenged the theory of spontaneous generation.

Disproving Spontaneous Generation

The debate over spontaneous generation continued well into the 19th century, with scientists serving as proponents of both sides. To settle the debate, the Paris Academy of Sciences offered a prize for resolution of the problem. Louis Pasteur , a prominent French chemist who had been studying microbial fermentation and the causes of wine spoilage, accepted the challenge. In 1858, Pasteur filtered air through a gun-cotton filter and, upon microscopic examination of the cotton, found it full of microorganisms, suggesting that the exposure of a broth to air was not introducing a “life force” to the broth but rather airborne microorganisms.

Later, Pasteur made a series of flasks with long, twisted necks (“swan-neck” flasks), in which he boiled broth to sterilize it ( Figure 1.12 ). His design allowed air inside the flasks to be exchanged with air from the outside, but prevented the introduction of any airborne microorganisms, which would get caught in the twists and bends of the flasks’ necks. If a life force besides the airborne microorganisms were responsible for microbial growth within the sterilized flasks, it would have access to the broth, whereas the microorganisms would not. He correctly predicted that sterilized broth in his swan-neck flasks would remain sterile as long as the swan necks remained intact. However, should the necks be broken, microorganisms would be introduced, contaminating the flasks and allowing microbial growth within the broth.

Pasteur’s set of experiments irrefutably disproved the theory of spontaneous generation and earned him the prestigious Alhumbert Prize from the Paris Academy of Sciences in 1862. In a subsequent lecture in 1864, Pasteur articulated “ Omne vivum ex vivo ” (“Life only comes from life”). In this lecture, Pasteur recounted his famous swan-neck flask experiment, stating that “…life is a germ and a germ is life. Never will the doctrine of spontaneous generation recover from the mortal blow of this simple experiment.” [4] To Pasteur’s credit, it never has.

• How did Pasteur’s experimental design allow air, but not microbes, to enter, and why was this important?
• What was the control group in Pasteur’s experiment and what did it show?

Key Takeaways

• The theory of spontaneous generation states that life arose from nonliving matter. It was a long-held belief dating back to Aristotle and the ancient Greeks.
• Experimentation by Francesco Redi in the 17th century presented the first significant evidence refuting spontaneous generation by showing that flies must have access to meat for maggots to develop on the meat. Prominent scientists designed experiments and argued both in support of (John Needham) and against (Lazzaro Spallanzani) spontaneous generation.
• Louis Pasteur is credited with conclusively disproving the theory of spontaneous generation with his famous swan-neck flask experiment. He subsequently proposed that “life only comes from life.”

Multiple Choice

Fill in the blank, short answer.

• Explain in your own words Pasteur’s swan-neck flask experiment.
• Explain why the experiments of Needham and Spallanzani yielded in different results even though they used similar methodologies.

Critical Thinking

• What would the results of Pasteur’s swan-neck flask experiment have looked like if they supported the theory of spontaneous generation?
• https://link.springer.com/content/pdf/10.1007%2Fs10739-017-9494-7.pdf ↵
• E. Capanna. “Lazzaro Spallanzani: At the Roots of Modern Biology.” Journal of Experimental Zoology 285 no. 3 (1999):178–196. ↵
• R. Mancini, M. Nigro, G. Ippolito. “Lazzaro Spallanzani and His Refutation of the Theory of Spontaneous Generation.” Le Infezioni in Medicina 15 no. 3 (2007):199–206. ↵
• R. Vallery-Radot. The Life of Pasteur , trans. R.L. Devonshire. New York: McClure, Phillips and Co, 1902, 1:142. ↵

DeSales Microbiology Copyright © 2022 by DeSales University & Dr. Dia Beachboard is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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Data-driven hypothesis generation in clinical research: what we learned from a human subject study, article sidebar, main article content.

Hypothesis generation is an early and critical step in any hypothesis-driven clinical research project. Because it is not yet a well-understood cognitive process, the need to improve the process goes unrecognized. Without an impactful hypothesis, the significance of any research project can be questionable, regardless of the rigor or diligence applied in other steps of the study, e.g., study design, data collection, and result analysis. In this perspective article, the authors provide a literature review on the following topics first: scientific thinking, reasoning, medical reasoning, literature-based discovery, and a field study to explore scientific thinking and discovery. Over the years, scientific thinking has shown excellent progress in cognitive science and its applied areas: education, medicine, and biomedical research. However, a review of the literature reveals the lack of original studies on hypothesis generation in clinical research. The authors then summarize their first human participant study exploring data-driven hypothesis generation by clinical researchers in a simulated setting. The results indicate that a secondary data analytical tool, VIADS—a visual interactive analytic tool for filtering, summarizing, and visualizing large health data sets coded with hierarchical terminologies, can shorten the time participants need, on average, to generate a hypothesis and also requires fewer cognitive events to generate each hypothesis. As a counterpoint, this exploration also indicates that the quality ratings of the hypotheses thus generated carry significantly lower ratings for feasibility when applying VIADS. Despite its small scale, the study confirmed the feasibility of conducting a human participant study directly to explore the hypothesis generation process in clinical research. This study provides supporting evidence to conduct a larger-scale study with a specifically designed tool to facilitate the hypothesis-generation process among inexperienced clinical researchers. A larger study could provide generalizable evidence, which in turn can potentially improve clinical research productivity and overall clinical research enterprise.

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Hypothesis-generating research and predictive medicine

Leslie g. biesecker.

National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA

Genomics has profoundly changed biology by scaling data acquisition, which has provided researchers with the opportunity to interrogate biology in novel and creative ways. No longer constrained by low-throughput assays, researchers have developed hypothesis-generating approaches to understand the molecular basis of nature—both normal and pathological. The paradigm of hypothesis-generating research does not replace or undermine hypothesis-testing modes of research; instead, it complements them and has facilitated discoveries that may not have been possible with hypothesis-testing research. The hypothesis-generating mode of research has been primarily practiced in basic science but has recently been extended to clinical-translational work as well. Just as in basic science, this approach to research can facilitate insights into human health and disease mechanisms and provide the crucially needed data set of the full spectrum of genotype–phenotype correlations. Finally, the paradigm of hypothesis-generating research is conceptually similar to the underpinning of predictive genomic medicine, which has the potential to shift medicine from a primarily population- or cohort-based activity to one that instead uses individual susceptibility, prognostic, and pharmacogenetic profiles to maximize the efficacy and minimize the iatrogenic effects of medical interventions.

The goal of this article is to describe how recent technological changes provide opportunities to undertake novel approaches to biomedical research and to practice genomic preventive medicine. Massively parallel sequencing is the primary technology that will be addressed here ( Mardis 2008 ), but the principles apply to many other technologies, such as proteomics, metabolomics, transcriptomics, etc. Readers of this journal are well aware of the precipitous fall of sequencing costs over the last several decades. The consequence of this fall is that we are no longer in a scientific and medical world where the throughput (and the costs) of testing is the key limiting factor around which these enterprises are organized. Once one is released from this limiting factor, one may ask whether these enterprises should be reorganized. Here I outline the principles of how these enterprises are organized, show how high-throughput biology can allow alternative organizations of these enterprises to be considered, and show how biology and medicine are in many ways similar. The discussion includes three categories of enterprises: basic research, clinical research, and medical practice.

The basic science hypothesis-testing paradigm

The classical paradigm for basic biological research has been to develop a specific hypothesis that can be tested by the application of a prospectively defined experiment (see Box 1 ). I suggest that one of the major (although not the only) factors that led to the development of this paradigm is that experimental design was limited by the throughput of available assays. This low throughput mandated that the scientific question had to be focused narrowly to make the question tractable. However, the paradigm can be questioned if the scientist has the ability to assay every potential attribute of a given type (e.g., all genes). If the hypothesis is only needed to select the assay, one does not need a hypothesis to apply a technology that assays all attributes. In the case of sequencing, the radical increase in throughput can release scientists from the constraint of the specific hypothesis because it has allowed them to interrogate essentially all genotypes in a genome in a single assay. This capability facilitates fundamental biological discoveries that were impossible or impractical with a hypothesis-testing mode of scientific inquiry. Examples of this approach are well demonstrated by several discoveries that followed the sequencing of a number of genomes. An example was the discovery that the human gene count was just over 20,000 ( International Human Genome Sequencing Consortium 2004 ), much lower than prior estimates. This result, although it was much debated and anticipated, was not a hypothesis that drove the human genome project, but nonetheless was surprising and led to insights into the nuances of gene regulation and transcriptional isoforms to explain the complexity of the human organism. The availability of whole genome sequence data from multiple species facilitated analyses of conservation. While it was expected that protein-coding regions, and to a lesser extent promoters and 5′- and 3′-untranslated regions of genes, would exhibit recognizable sequence conservation, it was unexpected that an even larger fraction of the genomes outside of genes are highly conserved ( Mouse Genome Sequencing Consortium 2002 ). This surprising and unanticipated discovery has spawned a novel field of scientific inquiry to determine the functional roles of these elements, which are undoubtedly important in physiology and pathophysiology. These discoveries demonstrate the power of hypothesis-generating basic research to illuminate important biological principles.

Basic science hypothesis-testing and hypothesis-generating paradigms

Clinical and translational research

The approach to clinical research grew out of the basic science paradigm as described above. The first few steps of selecting a scientific problem and developing a hypothesis are similar, with the additional step ( Box 2 ) of rigorously defining a phenotype and then carefully selecting research participants with and without that trait. As in the basic science paradigm, the hypothesis is tested by the application of a specific assay to the cases and controls. Again, this paradigm has been incredibly fruitful and should not be abandoned, but the hypothesis-generating approach can be used here as well. In this approach, a cohort of participants is consented, basic information is gathered on their health, and then a high-throughput assay, such as genome or exome sequencing, is applied to all of the participants. Again, because the assay tests all such attributes, the research design does not necessitate a priori selections of phenotypes and genes to be interrogated. Then, the researcher can examine the sequence data set for patterns and perturbations, form hypotheses about how such perturbations might affect the phenotype of the participants, and test that hypothesis with a clinical research evaluation. This approach has been used with data from genome-wide copy number assessments (array CGH and SNP arrays), but sequencing takes it to a higher level of interrogation and provides innumerable variants with which to work.

Clinical research paradigms

An example of this type of sequence-based hypothesis-generating clinical research started with a collaborative project in which we showed that mutations in the gene ACSF3 caused the biochemical phenotype of combined malonic and methylmalonic acidemia ( Sloan et al. 2011 ). At that time, the disorder was believed to be a classic pediatric, autosomal-recessive severe metabolic disorder with decompensation and sometimes death. We then queried the ClinSeq cohort ( Biesecker et al. 2009 ) to assess the carrier frequency, to estimate the population frequency of this rare disorder. Because ClinSeq is a cohort of adults with a range of atherosclerosis severity, we reasoned that this would serve as a control population for an unbiased estimate of ACSF3 heterozygote mutant alleles. Surprisingly, we identified a ClinSeq participant who was homozygous for one of the mutations identified in the children with the typical phenotype. Indeed, one potential interpretation of the data would be that the variant is, in fact, benign and was erroneously concluded to be pathogenic, based on finding it in a child with the typical phenotype. It has been shown that this error is common, with up to 20% of variants listed in databases as pathogenic actually being benign ( Bell et al. 2011 ). Further clinical research on this participant led to the surprising result that she had severely abnormal blood and urine levels of malonic and methylmalonic acid ( Sloan et al. 2011 ). This novel approach to translational research was a powerful confirmation that the mutation was indeed pathogenic, but there was another, even more important conclusion. We had conceptualized the disease completely incorrectly. Instead of being only a severe, pediatric metabolic disorder, it was instead a disorder with a wide phenotypic spectrum in which one component of the disease is a metabolic perturbation and another component is a susceptibility to severe decompensation and strokes. This research indeed raises many questions about the natural history of the disorder, whether the pediatric decompensation phenotype is attributable to modifiers, what the appropriate management of such an adult would be, etc.

Irrespective of these limitations, the understanding of the disease has markedly advanced, and the key to understanding the broader spectrum of this disease was the hypothesis-generating approach enabled by the massively parallel sequence data and the ability to phenotype patients iteratively from ClinSeq. The iterative phenotyping was essential because we could not have anticipated when the patients were originally ascertained that we would need to assay malonic and methylmalonic acid. Nor did we recognize prospectively that we should be evaluating apparently healthy patients in their seventh decade for this phenotype. Indeed, it is impossible to evaluate patients for all potential phenotypes prospectively, and it is essential to minimize ascertainment bias for patient recruitment in order to allow the discovery of the full spectrum of phenotypes associated with genomic variations. This latter issue has become a critical challenge for implementing predictive medicine, as described below.

Predictive genomic medicine in practice

The principles of scientific inquiry are parallel to the processes of clinical diagnosis ( Box 3 ). In the classic, hypothesis-testing paradigm, clinicians gather background information including chief complaint, 2 medical and family history, and physical examination, and use these data to formulate the differential diagnosis, which is a set of potential medical diagnoses that could explain the patient's signs and symptoms. Then, the clinician selects, among the myriad of tests (imaging, biochemical, genetic, physiologic, etc.), a few tests, the results of which should distinguish among (or possibly exclude entirely) the disorders on the differential diagnosis. Like the scientist, the physician must act as a test selector, because each of the tests is low throughput, time consuming, and expensive.

Clinical practice paradigms—hypothesis testing and hypothesis generating

As in the basic and translational research discussion above, the question could be raised as to whether the differential diagnostic paradigm is necessary for genetic disorders. Indeed, the availability of clinical genome and exome sequencing heralds an era when the test could be ordered relatively early in the diagnostic process, with the clinician serving in a more interpretative role, rather than as a test selector ( Hennekam and Biesecker 2012 ). This approach has already been adopted for copy number variation, because whole genome array CGH- or SNP-based approaches have mostly displaced more specific single-gene or single-locus assays and standard chromosome analyses ( Miller et al. 2010 ). But the paradigm can be taken beyond hypothesis-generating clinical diagnosis into predictive medicine. One can now begin to envision how whole genome approaches could be used to assess risks prospectively for susceptibility to late-onset disorders or occult or subclinical disorders. The heritable cancer susceptibility syndromes are a good example of this. The current clinical approach is to order a specific gene test if a patient presents with a personal history of an atypical or early-onset form of a specific cancer syndrome, or has a compelling family history of the disease. As in the prior examples, this is because individual cancer gene testing is expensive and low throughput. One can ask the question whether this is the ideal approach or if we could be screening for these disorders from genome or exome data. Again, we applied sequencing analysis for these genes to the ClinSeq cohort because they were not ascertained for that phenotype. In a published study of 572 exomes ( Johnston et al. 2012 ), updated here to include 850 exomes, we have identified 10 patients with seven distinct cancer susceptibility syndrome mutations. These were mostly familial breast and ovarian cancer ( BRCA1 and BRCA2 ), with one patient each with paraganglioma and pheochromocytoma ( SDHC ) and one with Lynch syndrome ( MSH6 ). What is remarkable about these diagnoses is that only about half of them had a convincing personal or family history of the disease, and thus most would have not been offered testing using the current, hypothesis-testing clinical paradigm. These data suggest that screening for these disorders using genome or exome sequencing could markedly improve our ability to identify such families before they develop or die from these diseases—the ideal of predictive genomic medicine.

Despite these optimistic scenarios and examples, it remains true that our ability to perform true predictive medicine is limited. These limitations include technical factors such as incomplete sequence coverage, imperfect sequence quality, inadequate knowledge regarding the penetrance and expressivity of most variants, uncertain medical approaches and utility of pursuing variants from genomic sequencing, and the poor preparation of most clinicians for addressing genomic concerns in the clinic ( Biesecker 2013 ). Recognizing all of these limitations, it is clear that we are not prepared to launch broad-scale implementation of predictive genomic medicine, nor should all research be structured using the hypothesis-generating approach.

Hypothesis-testing approaches to science and medicine have served us well and should continue. However, the advent of massively parallel sequencing and other high-throughput technologies provides opportunities to undertake hypothesis-generating approaches to science and medicine, which in turn provide unprecedented opportunities for discovery in the research realm. This can allow the discovery of results that were not anticipated or intended by the research design, yet provide critical insights into biology and pathophysiology. Similarly, hypothesis-generating clinical research has the potential to provide these same insights and, in addition, has the potential to provide us with data that will illuminate the full spectrum of genotype–phenotype correlations, eliminating the biases that have limited this understanding in the past. Finally, applying these principles to clinical medicine can provide new pathways to diagnosis and provide the theoretical basis for predictive medicine that can detect disease susceptibility and allow health to be maintained, instead of solely focusing on the treatment of evident disease.

Article is online at http://www.genome.org/cgi/doi/10.1101/gr.157826.113 .

2 The chief complaint is a brief description of the problem that led the patient to the clinician, such as “I have a cough and fever.”

• Bell CJ, Dinwiddie DL, Miller NA, Hateley SL, Ganusova EE, Mudge J, Langley RJ, Zhang L, Lee CC, Schilkey FD, et al. 2011. Carrier testing for severe childhood recessive diseases by next-generation sequencing . Sci Transl Med 3 : 65ra64 [ PMC free article ] [ PubMed ] [ Google Scholar ]
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A behind-the-scenes blog about research methods at Pew Research Center.

For our latest research findings, visit  pewresearch.org .

Assessing the effects of generation using age-period-cohort analysis

(Related posts:  5 things to keep in mind when you hear about Gen Z, Millennials, Boomers and other generations  and  How Pew Research Center will report on generations moving forward )

Opinions often differ by generation in the United States. For example, Gen Zers and Millennials are more likely than older generations to want the government to do more to solve problems , according to a January 2020 Pew Research Center survey. But will Gen Zers and Millennials always feel that way, or might their views on government become more conservative as they age? In other words, are their attitudes an enduring trait specific to their generation, or do they simply reflect a stage in life?

That question cannot be answered with a single survey. Instead, researchers need two things: 1) survey data collected over many years – ideally at least 50 years , or long enough for multiple generations to advance through the same life stages; and 2) a statistical tool called age-period-cohort (APC) analysis.

In this piece, we’ll demonstrate how to conduct age-period-cohort analysis to determine the effects of generation, using nearly 60 years of data from the U.S. Census Bureau’s Current Population Survey. Specifically, we’ll revisit two previous Center analyses that looked at generational differences in marriage rates and the likelihood of having moved residences in the past year to see how they hold up when we use APC analysis.

What is age-period-cohort analysis?

In a typical survey wave, respondents’ generation and age are perfectly correlated with each other. The two cannot be disentangled. Separating the influence of generation and life cycle requires us to have many years of data.

If we have data not just from 2020, but also from 2000, 1980 and earlier decades, we can compare the attitudes of different generations while they were passing through the same stages in the life cycle. For example, we can contrast a 25-year-old respondent in 2020 (who would be a Millennial) with a 25-year-old in 2000 (a Gen Xer) and a 25-year-old in 1980 (a Baby Boomer).

A dataset compiled over many years allows age and generation to be examined separately using age-period-cohort analysis. In this context, “age” denotes a person’s stage in the life cycle, “period” refers to when the data was collected, and “cohort” refers to a group of people who were born within the same time period. For this analysis, the cohorts we are interested in are generations – Generation Z, Millennials, Generation X and so on .

APC analysis seeks to parse the effects of age, period and cohort on a phenomenon. In a strictly mathematical sense, this is a problem because any one of those things can be exactly calculated from the other two (e.g., if someone was 50 years old in 2016, then we also know they were born circa 1966 because 2016 – 50 = 1966). If we only have data collected at one point in time, it’s not possible to identify conclusively whether apparent differences between generations are not just due to how old the respondents were when the data was collected. For example, we don’t know whether Millennials’ lower marriage rate in 2016 is a generational difference or simply a result of the fact that people ages 23 to 38 are less likely to be married, regardless of when they were born. Even if we have data from many years, any apparent trends could also be due to other factors that apply to all generations and age groups equally. We might mistakenly attribute a finding to age or cohort when it really should be attributed to period. This is called the “identification problem.” Approaches to APC analysis are all about getting around the identification problem in some way.

Using multilevel modeling for age-period-cohort analysis

A common approach for APC analysis is multilevel modeling. A multilevel model is a kind of regression model that can be fit to data that is structured in “groups,” which themselves can be units of analysis.

A classic example of multilevel modeling is in education research, where a researcher might have data on students from many schools. In this example, the two levels in the data are students and the schools they attend. Traits on both the student level (e.g., grade-point average, test scores) and the school level (e.g., funding, class size) could be important to understanding outcomes.

In APC analysis, things are a little different. In a dataset collected over many years, we can think of each respondent as belonging to two different but overlapping groups. The first is their generation, as determined by the year in which they were born. The second is the year in which the data was collected.

Fitting a multilevel model with groups for generation and year lets us isolate differences between cohorts (generations) and periods (years) while holding individual characteristics like age, sex and race constant. Placing period and cohort on a different level from age addresses the identification problem by allowing us to model all of these variables simultaneously.

Conducting age-period-cohort analysis with the Current Population Survey

One excellent resource that can support APC analysis is the Current Population Survey’s Annual Social and Economic Supplement (ASEC), conducted almost every year from 1962 to 2021. We’ll use data from the ASEC to address two questions:

• Does the lower marriage rate among today’s young adults reflect a generational effect, or is it explained by other factors?
• Does the relatively low rate of moving among today’s young adults reflect a generational effect, or is it explained by other factors?

In previous Center analyses, we held only age constant. This time, we want to separate generation not just from age, but also from period, race, gender and education.

Getting started

The data we’ll use in this analysis can be accessed through the Integrated Public Use Microdata Series (IPUMS) . After selecting the datasets and variables you need, download the data (as a dat.gz file) and the XML file that describes the data and put them in the same folder. Then, use the package ipumsr to read the data in as follows:

Next, process and clean the data. First, filter the data so it only includes adults (people ages 18 and older), and then create clean versions of the variables that will be in the model.

Here’s a look at the cleaning and filtering code we used. We coded anyone older than 80 as being 80 because the ASEC already coded them that way for some years. We applied that rule to every year in order to ensure consistency. We only created White, Black and Other categories for race because categories such as Hispanic and Asian weren’t measured until later. Finally, there were several years when the ASEC did not measure whether people moved residences in the past year, so we excluded those years from our analysis.

The full ASEC dataset has more than 6.7 million observations, with around 60,000 to 100,000 cases from each year. Without the computing power to fit a model to the entire dataset in any reasonable amount of time, one option is to sample a smaller number of cases per year, such as 2,000. To ensure that the sampled cases are still representative, sample them proportionally to their survey weight.

Model fitting

Now that the data has been processed, it’s ready for model fitting. This example uses the rstanarm package to fit the model using Bayesian inference.

Below, we fit a multilevel logistic regression model with marriage as the outcome variable; with age, number of adults in the household, sex, race and education as individual-level explanatory variables; and with period and generation as normally distributed random effects that shift the intercept depending on which groups an individual is in.

Regression models are largely made up of two components: the outcome variable and some explanatory variables. Characteristics that are measured on each individual in the data and that could be related to the outcome variable are potentially good explanatory variables. Every model also contains residual error, which captures anything that influences the outcome variable other than the explanatory variables; this can be thought of as encompassing unique qualities that make all individuals in the data different from one another. A multilevel regression model will also capture unique qualities that make each group in the data different from one another. The model may optionally include group-level explanatory variables as well.

The groups don’t need to be neatly nested within one another, allowing for flexibility in the kinds of situations to which multilevel modeling can apply. In our example, we model age as a continuous, individual-level predictor while modeling generation (cohort) and period as groups that each have different, discrete effects on the outcome variable. Separating age from period and cohort by placing them on different levels allows us to model all of them without running head-on into the identification problem. However, this is premised on a number of important assumptions that may not always hold up in practice.

By modeling the data like this, we are treating the relationship between period or generation and marriage rates as discrete, where each period or generation has its own distinct relationship. If there is a smooth trend over time, the model does not estimate the trend itself, instead looking at each period or generation in isolation. We are, however, modeling age as a continuous trend.

Separating generation from other factors

Our research questions above concern whether the differences by generation that show up in the data can still be attributed to generation after controlling for age, cohort and other explanatory variables.

In a report on Millennials and family life , Pew Research Center looked at ASEC’s data on people who were 23 to 38 years old in four specific years – 2019, 2003, 1987 and 1968. Each of these groups carried a generation label – Millennial, Gen X, Boomer and Silent – and the report noted that younger generations were less likely to be married than older ones .

Now that we have a model, we can reexamine this conclusion, decoupling generation from age and period. The model can return predicted probabilities of being married for any combination of variables passed to it, including combinations that didn’t previously exist in the data – and combinations that are, by definition, impossible. The model can, for example, predict how likely it is that a Millennial who was between ages 23 and 38 in 1968 would be married, even if no such person can exist. That’s useful not for what it represents in and of itself, but for what it can explain about the influence of generation on getting married.

In order to create predicted probabilities, the first requirement is to create a dataset on which the model will predict probabilities. This dataset should have every variable used in the model.

Here, we create a function that takes five inputs: the model, the original data, a generation category (cohorts), a range of years (periods) and a range of ages. This function will take everybody in the ASEC data for the given years and ages (regardless of whether they were in the subset used to fit the model) and set all their generations to be the same while keeping everything else unchanged, whether the resulting input makes sense in real life or not. This simulated ASEC data is then passed to the posterior_predict() function from rstanarm , which gives us a 2,000-by-n matrix, where n is the number of observations in the new data passed to it. We then compute the weighted mean of the predicted probabilities across the observations, giving us 2,000 weighted means. These 2,000 weighted means represent draws from a distribution estimating the predicted probability. We summarize this distribution by taking its median, as well as the 2.5th and 97.5th percentiles to create a 95% interval to express uncertainty.

We then run this function four times, once for each generation, on everyone in the ASEC data who was ages 23 to 38 during each of the four years we studied in the report. First, the function estimates the marriage rate among those people if, hypothetically, they were all Millennials, regardless of what year they appear in the ASEC data. Next, it estimates the marriage rate among those same people if, hypothetically, they were all Gen Xers. Then the function estimates the marriage rate if they were all Boomers or members of the Silent Generation, respectively.

In statistical terms, we are drawing from the “posterior predictive distribution.” This allows us to generate estimates for hypothetical scenarios in which we manipulate generation while holding age, period and all other individual characteristics constant. While this is not anything that could ever happen in the real world, it’s a convenient and interpretable way to visualize how changing one predictor would affect the outcome (marriage rate).

Determining the role of generation

To answer the first of our research questions, let’s plot our predicted probabilities of being married. If we took all these people at each of these points in time and magically imbued them with everything that is unique to being a Millennial, the model estimates that 38% of them would be married. In contrast, if you imbued everyone with the essence of the Silent Generation, that estimate would be 68%. The numbers themselves aren’t important, as they don’t describe an actual population. Instead, we’re mainly interested in how different the numbers are from one another. In this case, the intervals do not overlap at all between the generations and there is a clear downward trend in the marriage rate. This is what we would expect to see if the lower marriage rate among Millennials reflects generational change that is not explained by the life cycle (age) or by other variables in the model (gender, race, education).

What about our second question about generational differences in the likelihood of having moved residences in the past year? The Center previously reported that Millennials were less likely to move than prior generations of young adults. That analysis was based on a similar approach that considered people who were ages 25 to 35 in 2016, 2000, 1990, 1981 and 1963, and identified them as Millennials, Gen Xers, “Late Boomers,” “Early Boomers” and Silents.

Using the ASEC dataset described above, we reexamined this pattern using age-period-cohort analysis. We fit the same kind of multilevel model to this outcome and plotted the predicted probabilities in the same way. In this case, there are no clear differences or trend across the generations, with overlapping intervals for the estimated share who moved in the past year. This suggests that the apparent differences between the generations are better explained by other factors in the model, not generation.

These twin analyses of marriage rates and moving rates illustrates several key points in generational research.

In some cases (e.g., moving), what looks like a generation effect is actually explained by other factors, such as race or education. In other cases (e.g., marriage), there is evidence of an enduring effect associated with one’s generation.

APC analysis requires an extended time series of data; a theory for why generation may matter; a careful statistical approach; and an understanding of the underlying assumptions being made.

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• Published: 04 April 2024

The impact of perceived environmental corporate social responsibility on idea generation and idea implementation

• Li Yu   ORCID: orcid.org/0000-0002-6635-5598 1 &
• Weiwei Wu 2  

Humanities and Social Sciences Communications volume  11 , Article number:  486 ( 2024 ) Cite this article

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• Business and management

Scholars have already made a few outcomes regarding the effect of environmental corporate social responsibility (ECSR) on employees’ workplace behaviors. However, research on how perceived ECSR influences employee innovation remains largely unexplored. Drawing from the social identity theory (SIT) and stakeholder theory, this research fills this gap by examining: (a) the influence of perceived ECSR on idea generation (IG). (b) the influence of perceived ECSR on idea implementation (II). (c) the moderating effects of psychological capital (PsyCap) on these relationships. Using data on 348 employees from Chinese firms, the results demonstrate that perceived ECSR effectively fosters IG and II, yet when PsyCap is high, the positive influence of perceived ECSR is stronger. Our findings offer new insights for understanding the role of ECSR perception in the employee innovation domain by revealing that perceived ECSR can generate IG and promote II, and delimiting their boundaries from a psychological perspective. In addition, our findings make several practical implications for firms to cultivate their employees’ ECSR perception and improve employees’ IG and II to achieve sustainable development.

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Introduction

In recent years, improving energy efficiency and increasing waste recycling in China have become the core requirements (Zhou et al. 2022 ; Wu and Yu 2023 ). For example, the Chinese government (National Bureau of Statistics) reported that the total energy consumption reached 5410 billion kilograms of standard coal in 2022, a growth of 2.9% compared to the previous year. Moreover, firms’ activities are often related to environmental degradation (Li et al. 2023 ). In such a context, firms are starting to achieve success by affecting employees involved in innovative behaviors while mitigating environmental damage (Latif et al. 2022a ; Tuan 2023 ). Thus, it is critical for firms to develop employees’ innovative behavior (EIB). EIB is a workplace in which employees generate new ideas and implement them during the work process (Scott and Bruce 1994 ), including idea generation (IG) and idea implementation (II) (Birdi et al. 2016 ). To date, from a non-environmental protection perspective, scholars have investigated the predictors of IG and II, such as knowledge sharing (Fatemi et al. 2022 ), environmental dynamism (Huang et al. 2023 ), personal identification (Bracht et al. 2023 ), however, a theoretical exploration from an environmental corporate social responsibility (ECSR) perception perspective is still not comprehensive enough, especially in the contextualization of environmental protection (Ahmad et al. 2022a ; Tsameti et al. 2023 ). Thus, a meaningful and valuable question should be answered - does perceived ECSR foster IG and II?

Under the background of environmental protection, ECSR, as a topic of common concern, has been introduced into the environmental activities of organizations to improve employees’ workplace behaviors (Orazalin and Nurlan 2020 ; Hsu and Chen 2023 ). As a subjective perception of firms’ efforts to protect the environment, perceived ECSR is defined as “a sense that employees perceive the degree to which their organization’s ECSR is fulfilled and evaluate their organization” (Wu et al. 2021 ). Some scholars have suggested that ECSR perception of employees can promote them to identify with their organization, and further generate creative ideas and conduct innovative missions in their organization (Ahmad et al. 2022a ). Thus, increasing ECSR perceptions is beneficial to employees in terms of innovation performance (Hur et al. 2018 ). For example, when firms engage in ECSR activities, they may shape a good image and establish close relationships with their external stakeholders (Flammer, 2013 ) to access tangible and intangible resources (Abdi et al. 2022b ). These resources facilitate effectively generating and implementing innovative ideas from employees (Forcadell et al. 2021 ). Hence, perceived ECSR may become a predictive role that positively influences the IG and II of employees. However, previous studies still lacked the predictors of IG and II in the contextualization of environmental protection (Bracht et al. 2023 ; Tsameti et al. 2023 ), resulting in a limited understanding of the relationship between the perceived ECSR and two dimensions of EIB. Therefore, it is worth exploring the question - how and under which circumstance does perceived ECSR impact IG and II under the context of environmental protection?

However, despite the consensus on the impact of ECSR, little research has investigated under which circumstances perceived ECSR impacts employee innovation from a psychological perspective (Ahmad et al. 2022b ). SIT contends that the impacting process of employees’ organizational perception involved in their workplace behavior is driven by individuals’ psychological state of development (Ashforth and Mael, 1989 ; Raja et al. 2020 ). Firms need to improve employees’ psychological state of development during innovation management activities (Kraus et al. 2023 ; Latif et al. 2023 ). In this case, the transformation process of perceived ECSR to generation and implementation of ideas relies on employees’ psychological state from a psychological perspective. Psychological capital (PsyCap) represents the degree to which employees can display a positive psychological state (Qin et al. 2022 ; Luthans et al. 2007 ). Some research also has indicated that employees who have a higher PsyCap, are more likely to perceive firms’ activities, and their ECSR perceptions will be enhanced (Mao et al. 2021 ; Kraus et al. 2023 ), which then will elicit positive responses, such as generating creative ideas and implementing these new ideas. Hence, from a psychological perspective, the impact of PsyCap needs to be introduced into this research model to fully examine the relationships among perceived ECSR, IG, and II. According to the theoretical argumentation and gaps in the literature, this study raised the third research question: Does PsyCap as the moderating role influence the relationships between perceived ECSR and IG and II in the contextualization of environmental protection?

Accordingly, to address these gaps, drawing from the SIT, we set up to explore the psychological mechanism that perceived ECSR influences IG (either by oneself or taken from others) and II (Zhou 2003 ), as well as the moderating role of PsyCap in these relationships. SIT is widely used in the organizational behavior literature (Song et al. 2018 ) and is used to analyze how organizational factors influence individuals’ identification with the organization, and then impact their behaviors (Ashforth and Mael 1989 ). The stakeholder theory is applied to analyze how the resources from stakeholders influence individual innovation (Gambeta et al. 2019 ). By analyzing data from 348 employees of Chinese firms, our research tests the relationships between perceived ECSR and IG and II. Chinese firms of the construction industry, are closely related to environmental pollution and energy consumption (Chuai et al. 2021 ). In such a context, China regards ECSR as a prominent concern of firms trying to reduce pollutant emissions (Li et al. 2020 ). In addition, by focusing on the construction industry, our study can avoid interference from other industries. In other words, to avoid the variation of the different industries, it is necessary to control the samples within the construction industry. China is now implementing the “Innovation-Driven Development Strategy”, vigorously promoting firms’ innovation and encouraging employees to conduct innovation activities (Chen et al. 2021 ). Therefore, China has a suitable environment in which to explore these relationships among IG, II, PsyCap, and perceived ECSR.

To answer the above research questions, our research makes several contributions to the literature. First, previous studies on ECSR focused on its economic effect at the macro level (Li et al. 2020 ), however, the influence of perceived ECSR on employee innovation has not been fully studied from a micro-level perspective. Based on SIT and stakeholder theory, we found out the predicted role of ECSR perception, thereby providing a better understanding of the impact of ECSR. Second, while prior research has suggested that there is an indirect link between perceived CSR and employee innovativeness (Forcadell et al. 2021 ), little research identifies the predictors of the generation and implementation of ideas from an environmental perspective (Caniëls et al. 2022 ). This study clearly explains how perceived ECSR affects IG and II from an environmental protection perspective, thus breaking through this research limitation. Meanwhile, this study echoes prior studies (e.g., Wu et al. 2021 ) to spotlight the antecedents of IG and II are rooted in employees’ perceptions of their firms’ ECSR activities. Third, prior research ignored employees’ psychological states that affect the effectiveness of perceived ECSR, and therefore lack recognition of the circumstances under which perceived ECSR can effectively foster two dimensions of EIB. The findings contribute to the ECSR literature by determining the boundary by which perceived ECSR impacts IG and II from the PsyCap perspective.

Research background

Ecsr perception.

ECSR pays close attention to firm-specific activities and plays a key part in the interactive connection between firms and the natural environment (Chuang et al., 2018; Josiah and Akpuh, 2022 ). In our study, we defined ECSR as a series of environmentally friendly behaviors, in which a firm integrates environmental issues in interactions with stakeholders. ECSR perception is a subjective perception of an organization’s environmental responsibility (Turker 2009 ; De Roeck and Delobbe 2012 ), which can elicit positive behaviors from employees. Wu et al. ( 2021 ) define perceived ECSR as “a sense that employees perceive the degree to which their organization’s ECSR is fulfilled and evaluate their organization”. Our research, thus, pays attention to employees’ perception of ECSR and adopts Wu et al. ( 2021 ) definition of perceived ECSR.

Research on ECSR has mainly been going at two different levels. Most research has focused on the effects of ECSR from the macro level. For example, macro-level research has proposed that ECSR could impact legitimacy (Wei et al. 2017 ), firm value (Li et al. 2020 ), environmental performance (Hsu and Chen, 2023 ), and green intellectual capital (Li et al. 2023 ). Recently, other research has begun to analyze the influence of ECSR from a micro-level perspective. With this literature, some scholars have begun to explore internal stakeholders’ responses to ECSR and argued a close relationship between ECSR perceptions and internal stakeholders’ behaviors (Fatima and Elbanna 2023 ; Zhao et al. 2023 ). For example, they have examined that ECSR perception could develop customer loyalty (Ali et al. 2021 ), foster green purchase intention (Vu et al. 2022 ), promote pro-environment behavior (Latif et al. 2022b ) and environmental citizenship behavior (Soopramanien et al. 2023 ), and improve organizational attractiveness (Turker et al. 2023 ) by matching for various stakeholders’ expectations and interests. Although the ECSR literature has gradually started to study the effect of ECSR perception at the micro level, research on the antecedents of employee innovation from the ECSR perception perspective is still in its infancy.

Employees’ innovative behavior

EIB as a workplace behavior is a spontaneous activity, which is generally not included in the job responsibilities expected by the roles of the organization (Boiral, 2009 ). Examples of this behavior include generating novel ideas and acquiring various resources to implement such novel ideas (Mascareo et al. 2021 ). EIB includes IG and II (Birdi et al. 2016 ).

Research on the antecedents of IG and II has mainly been going on two levels. Some studies have given attention to the impact of organizational-level antecedents on IG and II (Huang et al. 2023 ). Organizational-level research has examined the effects of the hierarchy of authority (Keum, and See 2017 ), social network (Perry-Smith and Mannucci 2017 ), social network service addiction (Khan et al. 2022 ), virtual communication curbs (Brucks and Levav 2022 ), AI-based innovation management (Füller et al. 2022 ), HR systems (Batistič et al. 2022 ), environmental dynamism (Huang et al. 2023 ) and contextual input (Paay et al. 2023 ) on generation and implementation of the idea. Additionally, previous studies also argued that different organizational-level factors can impact the implementation of ideas compared to ideas generation (Hoang et al. 2022 ). For example, Birdi et al. ( 2016 ) found that compared with the generation of ideas, job control has a greater impact on the implementation of ideas. Other research has focused on the impact of individual-level antecedents on IG and II (Liu et al. 2020 ). Some of them proposed that generation and implementation of ideas are driven by knowledge sourcing (Che et al. 2018 ), knowledge sharing (Fatemi et al. 2022 ), employees’ creative self-efficacy (Newman et al. 2018 ), network size (Mannucci and Perry-Smith, 2022 ), employees’ perceptions of digital leadership (Erhan et al. 2022 ), personal identification (Bracht et al. 2023 ), and employee voice (Tsameti et al. 2023 ). Meanwhile, some scholars also stated that individual factors (e.g., creativity-relevant skills, Birdi et al. 2016 ) are significantly correlated with IG, but not with II. Additionally, we found that some scholars have investigated how environmental strategy perceptions influence innovative behavior (Paruzel et al. 2023 ). Afridi et al. ( 2020 ) and Paruzel et al. ( 2023 ) have indicated a direct relationship, while Wu et al. ( 2021 ) found an indirect relationship via organizational identification. This research suggests that perceived ECSR affects EIB. Hence, we intend to explore whether and when perceived ECSR influences IG and II, separately.

Psychological capital

PsyCap has received continuous attention from scholars in the positive organizational behavior (POB) domain (Newman et al. 2014 ; Kim et al. 2019 ). By adopting positive psychology and POB as the foundation and point of departure, Luthans et al. ( 2007 ) proposed the higher-order construct of PsyCap. PsyCap reflects individuals’ psychological state of development including the four dimensions of self-efficacy, hope, optimism, and resilience (Luthans and Youssef-Morgan, 2017 ), in which individuals show perceptions and workplace behaviors (Darvishmotevali and Ali 2020 ). Breaking down the four dimensions, self-efficacy is defined as a personal subjective perception that individuals utilize cognitive and motivational resources to complete a task and achieve goals in a specific context (Luthans et al. 2007 ). This personal subjective perception stems from social perceptions, as well as is rooted in individuals’ experiences. Second, hope refers to a positive motivation built up from individuals’ cognition, which consists of the willingness to succeed, and the path to achieving the ability to pursue the goal (Guo et al. 2018 ). By adjusting the goal and the way to achieve it, hope can confer the ability to guide people to work and motivate them to find pathways to desired goals. Third, optimism represents the expectation of positive results and the positive attribution of events (Seligman et al. 1998 ). Hence, optimistic workers are more likely to positively face stressful situations and positively find new methods to remove obstacles and seek opportunities (Rego et al. 2012 ). Finally, resilience is the ability that individuals positively handle troubles, and can even capture positive changes and overwhelming revolution in serious situations or during uncertainty (Luthans et al. 2007 ).

Taken together, PsyCap has become a crucial role in POB research (Bouckenooghe et al. 2015 ). Compared with rigid personal traits, Luthans and Church ( 2002 ) pointed out that PsyCap can flexibly enable individuals to exhibit behaviors that are beneficial to the organization, thereby improving personal performance. POB researchers have indicated that PsyCap plays a key role in improving employees’ behaviors and employee performance (Bouckenooghe et al. 2015 ), such as citizenship behaviors (Parent-Rocheleau et al. 2020 ), job performance (Ozturk, and Karatepe 2019 ; Darvishmotevali and Ali, 2020 ), engagement in knowledge-intensive work (Toth et al. 2022 ), job engagement (Kraus et al. 2023 ), and employee well-being (Babu et al. 2023 ). Recently, it has been growing evidence that PsyCap is related to innovative behavior (including employees’ IG and II) that supports their organizations (Kumar et al. 2022 ; Ghafoor and Haar, 2022 ). As Kumar et al. ( 2022 ) noted, PsyCap positively impacts EIB through mastery orientation. While it increasingly appears that PsyCap has benefits in generating and implementing new ideas (Ghafoor and Haar, 2022 ), the moderating effects of PsyCap on the ECSR’s perception-innovative behavior relationship is still infancy. As employees develop higher levels of PsyCap, their positive psychological state is enhanced (Guo et al. 2018 ). High PsyCap also helps employees to better perceive firms’ ECSR at work. Therefore, we further introduce PsyCap as the moderating factor of the relationships between ECSR’s perception and IG, and II in the current business environment.

Social identity theory and stakeholder theory

Derived from early research in social psychology (Tajfel and Turner 1989 ), the SIT contends that an employee’s identity is viewed as a psychological process in which employees divide themselves into different social reference groups (Ashforth and Mael 1989 ). SIT has been used extensively to explain the factors involved in behaviors in a specific context (Afsar et al. 2018 ). Furthermore, the concept of employee identity is well-built within the ECSR literature (Fatima and Elbanna 2023 ). According to the SIT, employees who positively perceive their organization with oneness or sameness towards the organization tend to be attracted by their organization’s image, which in turn affects employees’ behavior (Ahmad et al. 2022b ). In other words, when employees work for a firm with a good image, employees’ group affiliations will enhance their identity, thus changing their workplace behaviors (Glavas and Godwin 2013 ). ECSR activities reflect the extent to which firms in environmental protection and help firms establish a good organizational image that improves employees’ pride and fulfills their self-enhancement needs to be related to such a reputable organization (Tian and Robertson 2019 ; Yasin et al. 2023 ). Perceived ECSR is particularly crucial in impacting how competent employees measure their firms’ efforts in the environment (Turker et al. 2023 ). Meanwhile, IG and II are regarded as workplace behaviors related to employees’ perceptions in the environmental protection context (Ahmad et al. 2022b ). From a SIT perspective, perceived ECSR as a vital role may drive IG and II in the environmental protection context.

Stakeholder theory contends that the development of firms’ innovation requires the exchange and transaction of resources with stakeholders (Abdi et al. 2022a ). Stakeholder theory also points out that firms tend to acquire resources by meeting the expectations and interests of stakeholders (Flammer 2013 ; Abdi et al. 2022b ). Stakeholders originally covered “shareowners, employees, customers, suppliers, lenders, and society” (Freeman 2010 ). As critical stakeholders, employees perceive firms’ environmentally friendly behaviors and provide positive responses, which are meaningful and vital for their innovation (Gambeta et al. 2019 ). From this perspective, ECSR perception as a subjective assessment that pays attention to employees’ evaluation and understanding of an organization’s environmental responsibility (Gavin and Maynard 1975 ), panders to stakeholders’ expectations, which helps organizations acquire the resources from stakeholders and elicit IG and II of employees. The resources include positive psychological resources of employees, knowledge, and skills mastered by stakeholders. As such, perceived ECSR is likely to affect IG and II from a stakeholder theory perspective. To better explore the underlying psychological processes that perceived ECSR enhances innovative behavior, we further empirically investigate under which circumstances perceived ECSR influences employees’ IG and II by using the SIT and stakeholder theory. Additionally, from the psychological perspective, SIT suggests that psychological processes depend on individuals’ psychological state (Raja et al. 2020 ). PsyCap, as a positive psychological state, may impact the psychological processes that perceived ECSR affects employees’ IG and implementation.

Hypothesis development

Based on the SIT and stakeholder theory, we construct the conceptual model (see Fig. 1 ). We expect that perceived ECSR positively impacts IG and II. Furthermore, we also examine how PsyCap moderates these relationships among perceived ECSR, IG, and II.

We divided employees’ innovative behavior into idea generation and idea implementation. H1a shows that perceived ECSR is positively related to idea generation. H1b shows that perceived ECSR is positively related to idea implementation. H2a indicates that psychological capital positively moderates the influence of perceived ECSR on idea generation such that the relationship will be stronger for employees with high levels of psychological capital. H2b indicates that psychological capital positively moderates the influence of perceived ECSR on idea implementation such that the relationship will be stronger for employees with high levels of psychological capital.

Perceived ECSR and IG and II

Drawing on Scott and Bruce ( 1994 ), we define EIB as a workplace behavior that recognizes problems generates new ideas, and is likely to carry out ideas into implementation. Consistent with the research of Zhou ( 2003 ), our study divides innovative behavior into two dimensions: IG (either by oneself or taken from others) and II. IG is conceptualized as the generation of novel ideas, including new products, new services, new manufacturing methods, and new management processes (Woodman et al., 1993), which can promote the survival, innovation, and growth of firms in market competition. Also, we define II as the transformation of creative ideas into corresponding products. IG is the conceptual basis of EIB, which is the cognitive activity of employees (Yuan and Woodman 2010 ). II needs secure resources to implement these new ideas generated by employees, which is a social process (Mascareo et al. 2020 ).

SIT states that subjective perception may influence individuals’ identification, thereby impacting the behavior that supports their organization. When employees perceive that their organizations put an effort into ECSR activities for the sake of the improvement environment, they have a sense of pride (Mahmud et al. 2022 ; Yasin et al. 2023 ). In this context, increasing ECSR perception can help firms to develop employees’ identification with their firms, thus encouraging employees to generate good ideas and share new ideas (Ahmad et al. 2022a ). Moreover, previous studies have found that novel ideas generated by employees are regarded as a type of workplace behavior related to employees’ perceptions in the environmental protection context (Alhmoudi et al. 2022 ). Afridi et al. ( 2020 ) and Ahmad et al. ( 2022b ) found that firms that actively participate in ECSR activities can build an atmosphere of trust where employees can generate multiple ideas in their work roles. Since the more perceptions of a psychologically safe and trustworthy environment help to come up with original ideas (Glavas and Piderit, 2009 ; Bracht et al. 2023 ), the more likelihood that new ideas will be produced (Ahmad et al. 2022b ). From a SIT perspective, perceived ECSR may drive employees’ IG in the environmental protection context. In addition, stakeholder theory holds that employees with higher ECSR perception tend to make positive responses, which are important for their innovation activities (Wu et al. 2021 ). As such, when designing and manufacturing new products linked to the environment, employees with high ECSR perception are more willing to provide various new ideas for firms (Abdi et al. 2022b ). Therefore, we hypothesize:

Hypothesis1a . Perceived ECSR is positively related to IG.

Looking at the second stage of EIB, II is often presented as an effortful, resource-consuming process (Mascareo et al. 2020 ). In such a context, many obstacles may need to be overcome to encourage employees to put new ideas into practice. As the SIT proposed, higher ECSR perception can foster a sense of organizational identification among employees because employees desire to keep the firm’s good social reputation (Fatima and Elbanna 2023 ). The above organizational identification process can be reflected in the degree of employees’ support for innovation in providing a person’s technical knowledge resources and skills for their organizations to develop innovative ideas and apply new ideas (Ahmad et al. 2022a ). In this context, this increases the possibility of II. Wu et al. ( 2021 ) and Zhao et al. ( 2022 ) found that increasing ECSR perception is beneficial for firms to build a good reputation, thus attracting employees to invest more resources to implement new ideas and conduct creative solutions. According to Mascareo et al. ( 2021 ), higher ECSR perception can help firms enhance the recognition of their employees, thereby motivating them to complete innovative tasks and carry out their new ideas into implementation. Additionally, stakeholder theory also points out that the implementation of new ideas depends on the resources of stakeholders (Freeman, 2010 ; Xu et al. 2018 ). From this perspective, perceived ECSR as a subjective assessment of firms’ environmental responsibility (Chen et al. 2022 ), caters to the expectations and interests of stakeholders (Wu et al. 2020 ). In such a context, firms help employees build close connections with other stakeholders (Flammer 2013 ) and acquire the resources from the network of relationships (Abdi et al. 2022b ). The resources can help employees implement their new ideas and new solutions. Meanwhile, Wu et al. ( 2021 ), and Yang et al. ( 2019 ) stated that when firms engage in ECSR activities, they could establish a good organizational reputation to meet employees’ expectations, and in turn obtain the knowledge and technical resources needed for employees’ innovation. In light of the above theoretical arguments, we hypothesize:

Hypothesis1b . Perceived ECSR is positively related to II.

The moderating role of psychological capital

Following Luthans et al. ( 2007 ), PsyCap is conceptualized as a positive psychological state of individuals’ development. PsyCap can enhance the positive effects of employees’ organizational perceptions (Abbas et al. 2014 ). Because perceived ECSR can be viewed as a subjective perception of organizations’ ECSR activities (Rahman and Post 2012 ), we argue that high PsyCap employees tend to improve the influence of perceived ECSR on the generation and implementation of ideas.

SIT suggests the influence process of perceived ECSR involved in IG is a psychological process of employee identity (De Roeck and Delobbe 2012 ). This psychological process is impacted by individuals’ psychological states (e.g., hope and optimism, Luthans et al. 2007 ). As a positive psychological state, PsyCap covers employees’ feelings of confidence to complete challenging tasks (Fuchs et al. 2019 ; Kraus et al. 2023 ), the hoping feeling for desirable outcomes (Darvishmotevali and Ali, 2020 ), the optimistic sensations of individuals’ ability to overcome obstacles and achieve success (Cai et al. 2019 ), and the sense of resilience to the uncertain work and adversity (Hartmann et al. 2020 ). Thus, perceived ECSR could motivate employees to bring up more new ideas due to the positive psychological resources (i.e., PsyCap) of employees. That is, high PsyCap employees tend to generate novel ideas as a result of ECSR perceptions. Moreover, previous research has found that an employee with higher PsyCap tends to make positive attitudes and behaviors as a result of their environmental responsibility perceptions (Randolph et al. 2022 ; Latif et al. 2023 ), and as a consequence, the value of perceived ECSR to stimulate innovative ideas is magnified. As such, when employees perceive their firm as one that develops cleaner products and actively invests in environmental good (i.e., ECSR; Glavas and Godwin, 2013 ), high PsyCap employees are more likely to explore and develop innovative ideas without fear of outcomes (Kumar et al. 2022 ). Therefore, we hypothesize:

Hypothesis2a . PsyCap moderates the influence of perceived ECSR on IG.

High PsyCap employees who can exploit resources to help firms obtain certain goals, make a variety of behavioral responses as a result of employees’ organizational perceptions (Cai et al. 2019 ). Recently, it has been argued that high PsyCap helps employees perceive their firm’s ECSR projects in a positive direction and better cope with negative emotions, thereby creatively overcoming challenges and completing various innovative tasks in their immediate work environment (Raja et al. 2020 ). This suggests that high PsyCap improves the influence of perceived ECSR on II. In other words, PsyCap is a moderator that encourages employees to direct their ECSR perception toward II. Ghafoor and Haar ( 2022 ) and Kumar et al. ( 2022 ) pointed out that PsyCap has an impact on the connections between employees’ perception of firms’ activities and their behavioral responses. Specifically, when they are more sensitive to how their firms conduct the ECSR program, high PsyCap employees have a propensity to positively exploit and integrate resources to put new ideas into implementation (Hsu and Chen 2017 ; Kumar et al. 2022 ). Thus, high PsyCap employees tend to improve the influence of perceived ECSR on II. We hypothesize:

Hypothesis2b . PsyCap moderates the influence of perceived ECSR on II.

Sample and data collection

We surveyed employees working at different firms in the construction industry to examine the posited hypotheses. Firms in the construction industry need to get a lot of construction resources from the natural environment (e.g., wood, gravel, and soil) and are facing environmental pressure. In this context, innovation has been a core requirement for firms in this industry. Thus, the construction industry is suitable for the objectives of our study. We collected the data on ECSR perception, PsyCap, IG, and II through questionnaires. Before distributing the questionnaires, all items are confirmed to be understandable and clear for employees.

While 500 employees participated in our survey, we received 348 usable questionnaires with a response rate of 69.60%. This high response rate means that there is no non-response bias in our research. In Table 1 , 87.93% of our study participants were male. 78.45% were more than 30 years old. Moreover, 75.29% have already obtained bachelor’s degrees or above. Of the 348 responding participants, 19.83% held board professorate senior engineer and senior engineer, and 66.09% were engineers and assistant engineers. 27.87% of participants have been working for 11 to 20 years and 7.18% have been working for more than 20 years with their present firms.

This study measured perceived ECSR, PsyCap, IG, and II. Following the back-translation technique, items in this questionnaire are translated from English to Chinese (Brislin, 1980 ). Employees who we questioned provided their perceptual evaluations of all items (see Table S1 ) on a 5-point Likert scale ranging from 1“strongly disagree” to 5“strongly agree”.

Perceived ECSR

Following Turker ( 2009 ) perceived ECSR was measured by adopting three items to describe the perception that employees perceived their organization’s activities to protect the environment (De Roeck and Delobbe 2012 ). A sample item is: “Our firm takes part in activities to protect and improve the natural environment”.

Based on the study of Luthans et al. ( 2007 ), we adopt a measure of PsyCap following a twenty-four-item from the PsyCap questionnaire (PCQ). A sample item is: “I am confident to find a solution by analyzing long-term problems”. Keeping with the study of Guo et al. ( 2018 ), we adapted the overall PsyCap score.

We measured IG and II based on an eight-item scale from Scott and Bruce ( 1994 ) and Zhou and George ( 2001 ). We measured employees’ IG based on a four-item scale. A sample item is: “I can find new technologies, processes and services in the workplace”. II was measured using four items (e.g., “I can use new methods or techniques to reduce costs and improve efficiency in my work”).

Control variables

Prior studies (e.g., Newman et al. 2018 ; Tian and Robertson, 2019 ) have suggested that demographic characteristics (e.g., age and gender) influence EIB, which may affect the links between perceived ECSR and IG and II. Therefore, we include employees’ gender, age, education, position, and tenure as key control variables in this study. We coded gender as “1” for male and “2” for female. Similarly, we coded employees’ age (i.e., “1” - 18 to 30 years old, “2” - 31 to 40 years old, “3” - 41 to 50 years old, and “4” - 51 years old or above). Employees’ education is measured by their highest degree. Position was coded as “5” for professorate senior engineer, “4” for senior engineer, “3” for engineer, “2” for assistant engineer, and “1” for others. We calculated tenure as the number of years that employees have worked in the firm. Moreover, we controlled for firm size and team size because they may affect employees’ (a) IG and (b) II. The number of employees represents firm size (Wu and Yu 2022 ). We coded firm size as “1” - 100 employees or below, “2” - 101 to 300 employees, “3” - 301 to 500 employees, “4” - 501 to 1000 employees, and “5” - 1001 employees or above. We coded team size as “1” - 3 employees or below, “2” - 4 to 10 employees, “3” - 11 to 20 employees, “4” - 20 to 50 employees, and “5” - 51 employees or above. As Kang et al. ( 2021 ) and Zhang et al. ( 2022 ) noted, marital status could potentially influence employees’ innovative behavior. Therefore, we controlled it. Following previous literature (e.g., Niu 2014 ; Shahsavar et al. 2020 ), we coded marital status as “1” for single, “2” for living together, “3” for married without children, “4” for married with children and “5” for others. Finally, we also controlled for ownership. Ownership was coded as a dummy variable. The state-owned firm is recorded as 1, otherwise, it is recorded as 0.

Reliability and validity analysis

First, this study tested the reliability of perceived ECSR, PsyCap, IG, and II via SPSS 21. As Table 2 shows, the results indicate that Cronbach’s alpha values of all variables are more than 0.70, demonstrating adequate reliability.

Second, we conducted the principal component analysis for the scales and our results show that the cumulative variance contribution rate of all items is 69.456%, more than 60%. The total KMO value is 0.963. Meanwhile, the statistics of Bartlett’s test are significant at the level of 0.001, revealing that it is suitable for factor analysis. Moreover, four factors with eigenvalues were identified, more than 1. All standardized factor loadings are more than 0.5. Hence, the four-factor structure was identified in this study.

Third, we examined the validity of four variables by adopting confirmatory factor analysis (CFA). This model is well-matched with the data ( \(\,{x}^{2}/{df}\)  = 2.546, RMSEA = 0.067, CFI = 0.916, TFI = 0.910, IFI = 0.916). Moreover, the AVE of each structure is in the range of 0.622 to 0.711, and the comprehensive reliability is in the range of 0.868 to 0.983, demonstrating a high convergent validity. Additionally, the discrimination validity among constructs is verified by the AVE value in our study. The square root of the AVE of each structure is between 0.789 and 0.843 in Table 5 , which is greater than the off-diagonal coefficients. Therefore, these variables have good discrimination validity (Fornell and Larcker 1981 ).

Common method variance

To decrease the Common Method Variance (CMV), this study adopted some procedural techniques following Podsakoff et al. ( 2003 ). First, our questionnaire is anonymous. Moreover, employees’ personal information and answers to the questionnaire will not be disclosed publicly. Second, this study reordered the questions listed in the questionnaire. Third, consider that there are no uniform and clear answers to the questions on this questionnaire, employees can answer all questions according to their own opinions. Statistically, following Harman’s single-factor test, our results indicated that principal component analysis extracted four factors and the first factor accounted for 45.865%, well below the cutoff of 50%, demonstrating that the single-factor model is not suitable for this study (Harman 1976 ). In addition, this study confirmed that the four-factor model ( \(\,{x}^{2}/{df}\)  = 2.546, RMSEA = 0.067, CFI = 0.916, TFI = 0.910, IFI = 0.916) is superior to one factor model ( \(\,{x}^{2}/{df}\)  = 6.417, RMSEA = 0.125, CFI = 0.703, TFI = 0.683, IFI = 0.704) through CFA. Thus, common method bias does not deserve attention.

Empirical results

Statistical analysis.

Table 3 shows the descriptive statistics of variables. The mean and standard deviation of perceived ECSR are 3.518 and 1.106, respectively. The mean and standard deviation (SD) of PsyCap are 3.857 and 0.824. The mean of IG is 4.046, which is slightly higher than II (3.992). The SD of IG is 0.676, which is slightly lower than that of product innovation (0.747). As we expected, measured dispersion for our data is not a concern in this study. Moreover, the medians of perceived ECSR (3.667), PsyCap (4.000), IG (4.000), and II (4.250) are close to the maximums, suggesting that the levels of these variables are slightly high. Additionally, we used one sample T-test to compare the means in our sample with normative values. From Table 4 , results from these analyses revealed that levels of perceived ECSR (t = 8.743, p < 0.01; difference in means=0.518), PsyCap (t = 19.399, p < 0.01; difference in means=0.857), IG (t = 28.847, p < 0.01; difference in means=1.046), and II (t = 24.765, p < 0.01; difference in means=0.992) are higher than in the normative sample.

Correlation matrix

The correlations of variables are depicted in Table 5 . These analyses confirm that perceived ECSR correlates significantly with IG ( \(r\) =0.512; p  < 0.01) and II ( \(r\) =0.568; p  < 0.01). Furthermore, PsyCap is significantly associated with IG ( \(r\) =0.318; p  < 0.01) and II ( \(r\) =0.316; p  < 0.01). In addition, age ( \(r\) =0.172; p  < 0.01), firm size ( \(r\) =0.146; p  < 0.01), and tenure ( \(r\)  = 0.185; p < 0.01) are positively related to IG, but other control variables in our study are not. Gender ( \(r\) =0.113; p  < 0.05), age ( \(r\) =0.144; p  < 0.01), firm size ( \(r\) =0.215; p  < 0.01), tenure ( \(r\)  = 0.276; p < 0.01), marital status ( \(r\) =0.112; p  < 0.05) and ownership ( r  = −0.155; p  < 0.01) are significantly correlated with II, but education, team size, and position are not significant.

Test of hypotheses

We employed the OLS analysis to test the direct effects and moderating effects (see Table 7 ). The variance inflation factors (VIF) for all regression models are less than 5.0 in Table 6 , which reveals that there is no multicollinearity in this research.

Next, we test all of our hypotheses in Table 7 . Our results revealed that perceived ECSR positively influences IG (Model 3, \(\beta\) =0.309; p  < 0.01), and the influence of perceived ECSR on II in Model 4 ( \(\beta\) =0.371; p  < 0.01) is positive, and noticeable after controlling for gender, education, age, firm size, team size, tenure, position, marital status, and ownership. Thus, Hypothesis1a and Hypothesis1b are supported. Additionally, Model 5 shows team size ( \(\beta\) =−0.063; p  < 0.05) and marital status ( \(\beta\) =0.047; p  < 0.1) are associated with IG, but other control variables are not significant. Model 6 shows that gender, age, education, firm size, team size, and ownership do not have any impact on II. We found only tenure ( \(\beta\) =0.125; p  < 0.01), position ( \(\beta\) =0.068; p  < 0.05), and marital status ( \(\beta\) =0.071; p  < 0.01) to be significant in Model 6.

In Model 5, the interaction term between perceived ECSR and PsyCap on IG is represented in Table 6 . This coefficient is positive and significant ( \(\beta\) =0.106, p  < 0.05), thereby indicating that PsyCap moderates the positive impact of perceived ECSR on IG (Hypothesis 2a). Similarly, the interaction term between perceived ECSR and PsyCap on II also was contained in Model 6. This coefficient is positive and significant ( \(\beta\) =0.085, p  < 0.1), thus indicating that PsyCap enhances the impact of perceived ECSR on II (Hypothesis 2b). Figure 2 shows that at a high level of PsyCap, the influence of perceived ECSR on IG is greater than at a low level of PsyCap. Meanwhile, Fig. 3 describes that at a higher PsyCap, the influence of perceived ECSR on II is stronger. This may be because when PsyCap is high, employees are more likely to recognize firms’ ECSR efforts, thus increasing their positive behaviors toward ECSR activities. In this context, perceived ECSR will influence IG and II when PsyCap is high. Therefore, Hypothesis 2a and Hypothesis 2b are supported.

As Figure shows, idea generation increases more rapidly when the level of PsyCap shifts from low to high. This indicates that PsyCap enhances the positive influence of perceived ECSR on idea generation (H2a). PECSR perceived ECSR, PsyCap psychological capital.

As Figure shows, idea implementation increases more rapidly when the level of PsyCap shifts from low to high. This indicates that PsyCap enhances the positive influence of perceived ECSR on implementation (H2b). PECSR perceived ECSR, PsyCap psychological capital.

Additional analyses

To test the robustness of the results, we performed sensitivity analyses. First, we estimated a new model of psychological capital as the independent variable, perceived ECSR as the moderating variable, IG and II as the dependent variables. The results are included in Table 8 . From Table 8 , the R 2 values (Model 1, R 2  = 0.184; Model 2, R 2  = 0.228) also are smaller than the current model in Table 7 (Model 3, R 2  = 0.289; Model 4, R 2  = 0.373). As such, the current model is appropriate. Second, we also estimated an additional new model of the interaction between perceived ECSR and PsyCap as the independent variable, IG and II as the dependent variables. For Model 3 in Table 8 , we found that the R 2 value (0.121) still is smaller than the current model in Table 7 (Model 3, R 2  = 0.289). Similarly, the results of the interaction term between perceived ECSR and PsyCap on II are contained in Model 4. The R 2 value (0.158) in Model 4 is smaller than the current model in Table 7 (Model 4, R 2  = 0.373). Therefore, our model provides a better fit. Third, we constructed a new framework, under which we examine how IG and II impact PsyCap. In Table 7 , the details are as follows: the R 2 values in Table 8 (Model 5, R 2  = 0.158; Model 6, R 2  = 0.157) also are smaller than the current model in Table 7 (Model 3, R 2  = 0.289; Model 4, R 2  = 0.373), thus indicating that our model fits the data well. These results suggest that our model adequately captures the underlying relationships among the main variables in our research model.

Additionally, we also employed some other tests. Table 9 represents the results of additional robustness tests. First, we added a quadratic term of perceived ECSR in Model 1 and Model 2 to test if perceived ECSR has this curvilinear influence on the two dimensions of EIB. In Table 9 , the squared term of perceived ECSR is insignificant in Model 2, suggesting that the inverted U-shaped relationship between perceived ECSR and II is not supported by our data. Second, many prior studies on EIB do not include ownership (e.g., Füller et al. 2022 ; Bracht et al. 2023 ). We removed the ownership dummies and tested Hypothesis 2a and Hypothesis 2b. The results represent that the moderating effects of PsyCap are still positive ( β  = 0.103, p  < 0.05; β  = 0.083, p  < 0.1) in Model 3 and Model 4, providing support for Hypothesis 2a and Hypothesis 2b, again. Our findings remain unaltered. Accordingly, these additional tests indicate that our findings are overall robust.

Discussion and conclusion

Under the increasing pressure of environmental protection, EIB is extremely important for firms’ survival and success (Hur et al. 2018 ). Drawing the SIT and stakeholder theory, we construct a general model of employees’ ECSR perception and innovative behavior with which to explore the influences of perceived ECSR on the two dimensions of EIB and identify the boundary, which is PsyCap. Our empirical results show that perceived ECSR as a predictive role is significantly and positively linked with IG and II, respectively. The findings enhance the understanding of the link between ECSR perceptions and employee innovation, as well as provide practical guidelines for firms in the turbulent business environment. Meanwhile, the findings also further highlight the connection between employee roles and sustainable development. Additionally, our empirical results show that the effects of perceived ECSR in improving IG and II are significantly stronger when the PsyCap of employees is high.

Theoretical contributions

Our findings offer important contributions to the innovative behavior literature. First, we contribute to the literature by extending the influence of perceived ECSR into the innovation domain and clarifying the potential mechanism of how perceived ECSR influences the two dimensions of EIB. Prior studies on ECSR are mainly from its economic impact at the macro level (Li et al. 2020 ; Wu and Yu 2023 ), but the role of perceived ECSR in driving employee innovation has been underexamined. In our model, by testing the predictive role of ECSR perceptions, our study highlights how some drivers of IG and II arise from employees’ perceptions of their firms’ engaging their companies’ participation in specific initiatives that are environmentally responsible and promote the sustainable development of the organization. Moreover, we introduce stakeholder theory and SIT to innovative behavior literature to seek the potential mechanism for triggering positive behavioral responses among employees, through which perceived ECSR positively affects the generation and implementation of ideas. Compared with a single theory, our multi-theoretical model could examine the phenomenon more comprehensively. Therefore, our finding helps to establish the theoretical connection between ECSR and employee innovation. In addition, the finding also advances ECSR literature from the perspective of stakeholders.

Second, our results further reveal that perceived ECSR positively impacts the two dimensions of EIB, including the generation and implementation of ideas. By combining the existing literature and our knowledge in hand, our finding is one of the few studies to investigate the antecedents of IG and II in the environmental protection context (Wu and Yu, 2022 ; Bracht et al. 2023 ). The finding echoes the call for an investigation of the antecedents of IG and II (Birdi et al. 2016 ) and implies that perceived ECSR influences the generation and implementation of ideas from the ECSR perspective. While previous research has proved that an indirect connection exists between perceived CSR and employee innovativeness (Forcadell et al. 2021 ), little research has demonstrated that perceived CSR directly affects IG and II in the environmental protection context (Caniëls et al. 2022 ). Therefore, our findings also offer insightful explanations for the relationships between ECSR and employee innovation.

Third, our study contributes to innovative behavior by determining the boundary condition (i.e., PsyCap) when perceived ECSR might positively impact the workplace behaviors of employees (e.g., Tian and Robertson, 2019 ). Previous studies ignored individuals’ psychological states that condition the effectiveness of ECSR perception (Wu et al. 2021 ). In this case, the understanding of under what circumstances ECSR perception improves IG and II is limited. Our findings demonstrate that PsyCap can strengthen these relationships among perceived ECSR, IG, and II in the workplace. Therefore, our research fills this gap by deepening the understanding of the important role of PsyCap in perceived ECSR - IG and II relationships from the psychological perspective. The finding also emphasizes the role of PsyCap in affecting employees’ workplace behaviors.

Finally, our work contributes to the nascent literature on individual-level ECSR by extending initial research on perceived ECSR in management research (e.g., Vlachos et al. 2014 ). Our research explores an overlooked dimension of CSR, namely ECSR at the individual level (Turker 2009 ; Rahman and Post 2012 ). In doing so, this study has linked perceived ECSR with employees’ workplace behaviors by confirming that perceived ECSR plays a key part in influencing IG and II from a micro-level perspective. Furthermore, our findings that these relationships between perceived ECSR and IG and II are moderated by PsyCap from the individual perspective. As such, the findings expand the existing studies (e.g., Graafland 2020 ; Wu and Yu 2023 ) that have begun to examine the boundary condition of the ECSR perception - employees’ innovation relationship. In addition, by identifying PsyCap as a boundary condition that affects the ECSR perception - IG and II relationships, our study offers a strong theoretical interpretation of this relationship in the micro-level ECSR literature.

Practical implications

Our findings have also provided some vital practical implications for practitioners operating in the construction industry. First, perceived ECSR is a key driver of IG and II under the background of environmental protection. Therefore, we suggest that firms’ practitioners (e.g., CEOs) aiming to improve the generation and implementation of employees’ ideas should establish a good organizational atmosphere that fosters the employees’ perceptions toward firms’ ECSR activities. An organizational atmosphere can be created by regularly training and disclosing ECSR topics to employees. The more perceptions of ECSR activities help to bring up original ideas and take initiatives (Glavas and Piderit 2009 ), the more likelihood that employees’ new ideas will be motivated. In addition, to obtain more technical knowledge resources for developing innovative ideas and implementing new ideas, we suggest that practitioners should effectively share the ECSR strategy information with employees through annual reports, quarterly reports, and awareness programs. The information on the ECSR strategy covers all efforts made by firms to fulfill their ECSR (Rahman and Post 2012 ). Furthermore, practitioners should regularly communicate ECSR strategies to employees through training.

Second, our findings have demonstrated that PsyCap can strengthen the direct links between perceived ECSR and the generation and implementation of ideas. Because employees’ PsyCap is a positive psychological state, it can be fostered and shaped gradually over time (Caniëls et al. 2022 ). Therefore, we suggest that practitioners should invest more energy and time in improving PsyCap to enhance employees’ ability to deal with the ECSR-innovation relationship. Specifically, practitioners can set up a psychological counseling department for employees to help employees shape a good psychological state and process negative emotions timely. For example, when practitioners discover employees with low PsyCap, organizations can quickly address their problems by providing them with personalized services to improve employees’ feelings of confidence and hope to complete innovative tasks. In addition, to increase employees’ PsyCay, we suggest that practitioners should regularly hold a variety of organizational cultural activities. Moreover, practitioners should enhance employees’ PsyCap in the training to increase the potential return of perceived ECSR.

Finally, we suggest that practitioners should actively invest in sustainable development activities to promote the integration of ECSR into all aspects of employees’ work environment, which will improve IG and II in ECSR activities by increasing employee. In addition, our findings help regulators recognize the importance of firms investing in ECSR activities to promote employees’ innovative behavior. Regulators, such as the Social Responsibility Bureau of the State-owned Assets Supervision and Administration Commission of the State Council of China, should increase the firm’s ECSR investment by providing encouraging policies, such as government subsidies and preferential taxation policy for high-level ECSR firms.

Limitations and future research

Apart from its implications, our research has some limitations. First, participants are only from the construction industry in China, which means our results may not fully prove their applicability to other industries because employees in different industries have different reactions to perceived ECSR. In this context, the low R 2 value may derive from our empirical contexts. Future research should further pay attention to employees of other industries (e.g., non-ferrous metals, textiles, petrochemical and chemical, manufacturing, power, coal, mining, and pharmaceutical) in different countries to confirm our findings. Second, the low R 2 value also may arise from a limitation of our research related to the cross-sectional design of the current study. That’s because the cross-sectional data used in our study do not permit causal interpretations of its empirical findings. Future research could adopt experimental or longitudinal designs to improve the R 2 value and test our proposed hypotheses in the current study. Third, we only considered the moderating role of employees’ PsyCap in the links between perceived ECSR and the two dimensions of EIB from the psychological perspective. These relationships are highly complex. In such a context, it may be influenced by additional moderating factors from other perspectives, which may lead to the low R 2 value in our model. Future research should make an effort to investigate other moderating factors (e.g., organizational culture and work atmosphere) within a certain environment. Finally, our research only considers IG and II affected by perceived ECSR, we encourage other scholars to test the link between organizational climates and innovative behavior rather than merely individual factors.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (72072047), the Fundamental Research Funds for the Central Universities (HIT.HSS.ESD202310), the Research Project on Graduates’ Education and Teaching Reform (23MS011), the Research Project on Higher Education of Heilongjiang Higher Education Association (23GJYBC011), and the China Scholarship Council (CSC NO. 202106120213). We also appreciate Maree Shirota (associate editor) and reviewers.

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How One Tech Skeptic Decided A.I. Might Benefit the Middle Class

David Autor, an M.I.T. economist and tech contrarian, argues that A.I. is fundamentally different from past waves of computerization.

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By Steve Lohr

David Autor seems an unlikely A.I. optimist. The labor economist at the Massachusetts Institute of Technology is best known for his in-depth studies showing how much technology and trade have eroded the incomes of millions of American workers over the years.

But Mr. Autor is now making the case that the new wave of technology — generative artificial intelligence, which can produce hyper-realistic images and video and convincingly imitate humans’ voices and writing — could reverse that trend.

“A.I., if used well, can assist with restoring the middle-skill, middle-class heart of the U.S. labor market that has been hollowed out by automation and globalization,” Mr. Autor wrote in a paper that Noema Magazine published in February.

Mr. Autor’s stance on A.I. looks like a stunning conversion for a longtime expert on technology’s work force casualties. But he said the facts had changed and so had his thinking.

Modern A.I., Mr. Autor said, is a fundamentally different technology, opening the door to new possibilities. It can, he continued, change the economics of high-stakes decision-making so more people can take on some of the work that is now the province of elite, and expensive, experts like doctors, lawyers, software engineers and college professors. And if more people, including those without college degrees, can do more valuable work, they should be paid more, lifting more workers into the middle class.

The researcher, whom The Economist once called “the academic voice of the American worker,” started his career as a software developer and a leader of a computer-education nonprofit before switching to economics — and spending decades examining the impact of technology and globalization on workers and wages.

Mr. Autor, 59, was an author of an influential study in 2003 that concluded that 60 percent of the shift in demand favoring college-educated workers over the previous three decades was attributable to computerization. Later research examined the role of technology in wage polarization and in skewing employment growth toward low-wage service jobs .

Other economists view Mr. Autor’s latest treatise as a stimulating, though speculative, thought exercise.

“I’m a great admirer of David Autor’s work, but his hypothesis is only one possible scenario,” said Laura Tyson, a professor at the Haas School of Business at the University of California, Berkeley, who was chair of the Council of Economic Advisers during the Clinton administration. “There is broad agreement that A.I. will produce a productivity benefit, but how that translates into wages and employment is very uncertain.”

That uncertainty usually veers toward pessimism. Not just Silicon Valley doomsayers, but mainstream economists predict that many jobs, from call center workers to software developers, are at risk. In a report last year , Goldman Sachs concluded that generative A.I. could automate activities equivalent to 300 million full-time jobs globally.

In Mr. Autor’s latest report, which was also published in the National Bureau of Economic Research, he discounts the likelihood that A.I. can replace human judgment entirely. And he sees the demand for health care, software, education and legal advice as almost limitless, so that lowering costs should expand those fields as their products and services become more widely affordable.

It’s “not a forecast but an argument” for an alternative path ahead, very different from the jobs apocalypse foreseen by Elon Musk, among others, he said.

Until now, Mr. Autor said, computers were programmed to follow rules. They relentlessly got better, faster and cheaper. And routine tasks, in an office or a factory, could be reduced to a series of step-by-step rules that have increasingly been automated. Those jobs were typically done by middle-skill workers without four-year college degrees.

A.I., by contrast, is trained on vast troves of data — virtually all the text, images and software code on the internet. When prompted, powerful A.I. chatbots like Open AI’s ChatGPT and Google’s Gemini can generate reports and computer programs or answer questions.

“It doesn’t know rules,” Mr. Autor said. “It learns by absorbing lots and lots of examples. It’s completely different from what we had in computing.”

An A.I. helper, he said, equipped with a storehouse of learned examples can offer “guidance” (in health care, did you consider this diagnosis?) and “guardrails” (don’t prescribe these two drugs together).

In that way, Mr. Autor said, A.I. becomes not a job killer but a “worker complementary technology,” which enables someone without as much expertise to do more valuable work.

Early studies of generative A.I. in the workplace point to the potential. One research project by two M.I.T. graduate students , whom Mr. Autor advised, assigned tasks like writing short reports or news releases to office professionals. A.I. increased the productivity of all workers, but the less skilled and experienced benefited the most. Later research with call center workers and computer programmers found a similar pattern.

But even if A.I. delivers the largest productivity gains to less-experienced workers, that does not mean they will reap the rewards of higher pay and better career paths. That will also depend on corporate behavior, worker bargaining power and policy incentives.

Daron Acemoglu, an M.I.T. economist and occasional collaborator of Mr. Autor’s, said his colleague’s vision is one possible path ahead, but not necessarily the most likely one. History, Mr. Acemoglu said, is not with the lift-all-boats optimists.

“We’ve been here before with other digital technologies, and it hasn’t happened,” he said.

Mr. Autor acknowledges the challenges. “But I do think there is value in imagining a positive outcome, encouraging debate and preparing for a better future,” he said. “This technology is a tool, and how we decide to use it is up to us.”

Steve Lohr writes about technology and its impact on the economy, jobs and the workplace. More about Steve Lohr

Explore Our Coverage of Artificial Intelligence

News  and Analysis

Artificial intelligence is peering into restaurant garbage pails  and crunching grocery-store data to try to figure out how to send less uneaten food into dumpsters.

David Autor, an M.I.T. economist and tech skeptic, argues that A.I. is fundamentally different  from past waves of computerization.

Economists doubt that artificial intelligence is already visible in productivity data . Big companies, however, talk often about adopting it to improve efficiency.

OpenAI unveiled Voice Engine , an A.I. technology that can recreate a person’s voice from a 15-second recording.

Amazon said it had added $2.75 billion to its investment in Anthropic , an A.I. start-up that competes with companies like OpenAI and Google.

Gov. Bill Lee of Tennessee signed a bill  to prevent the use of A.I. to copy a performer’s voice. It is the first such measure in the United States.

Hellblade 2 Previews Have Arrived And It Sounds Like The “Next-Gen” Spectacle We’ve Been Waiting For

I t’s hard to believe, but Hellblade II: Senua’s Saga, a game that Microsoft has been promoting since well before   the arrival of the Xbox Series X|S consoles, will finally be out in a bit over a month. It almost felt as though Ninja Theory had been strangely quiet this close to the finish line, but it turns out it was busy showing off the game in-person to games media overseas and so now we have a heap of fresh hands-on previews of the game to hold us over until its release.

Perhaps unsurprisingly to the NT-devout, but said previews are also hugely positive, with many calling this the “next-gen” (their words, not mine) cinematic gameplay experience that’s been promised by hardware vendors and the likes of Unreal Engine 5 for years now. Like the previous game, it’s a mostly-linear adventure with the path ahead dotted with puzzles and light exploration, while protagonist Senua’s main interactions come in the form of combat against a huge variety of foes. Many of the previews have praised the fluidity of fights and the massive number of different, contextual and fully motion-captured animations that bring it all to life.

While one outlet, GamePro , has highlighted the game’s 30FPS cap and dynamic resolution on both Xbox Series X and Xbox Series S with no “performance” option available, I don’t think it’s something players are going to be overly disappointed with when the presentation is  this filmic. There’s even an anamorphic lens over the whole thing to really drive home that idea.

The only real concerns levelled at the game so far in some of these previews have been from a narrative point-of-view, with the shift from Senua’s very introspective story in the first game to something bigger and more outward in the sequel, but with hands-on demos reportedly going for less than an hour there’ll clearly be a lot more to unpack in the full game.

“Its ambitious narrative and accomplished cinematic framing far outweigh whatever shortcomings may be hidden underneath, and the incredible technology powering its beautifully ugly world is a sight to behold,” IGN concluded in its preview. “It’s all shaping up to be another riveting, haunting, and uncompromisingly immersive nightmare of both Senua’s and Ninja Theory’s creation, and one I can’t wait to both enter, and escape, later this year.”

Speaking to the game’s place in Xbox’s library, Games Radar said , “From what I’ve played of Hellblade 2, it’s clearly on track to be one of the most important exclusives that the platform has hosted this generation. Not only is it a fluid and responsive action-adventure game, boasting best-in-class visual fidelity, but it’s also an experience with something meaningful to convey.”

GameSpot’s preview is nicely summarised in its closing line, with writer Jordan Ramée saying, “Truly, May cannot come soon enough. I can’t remember the last time a game preview left me so excited to dive into the full experience.”

Meanwhile, Polygon compares the game’s flair for cinema and drama both in and out of combat to an arcade FMV classic, writing, “Hellblade 2 won’t necessarily be to everyone’s taste with its slow pace, deliberate inputs, and highly scripted, cinematic presentation. It struck me as a modern successor to something like the 1983 interactive animation Dragon’s Lair. As intense and dramatic as the section I played was, it remains to be seen whether the game’s story — a more outward journey for a more mentally balanced Senua — can connect as deeply as Hellblade’s trip into her darkest fears. But there’s no doubting the craft on display, or the immersive sense of presence this game has. It may be a sequel, but it feels like the start of something — like a true next-gen experience should.”

Senua’s Saga: Hellblade II launches on May 21st for Xbox Series X|S and PC. It’ll be available day one on Game Pass.

The post Hellblade 2 Previews Have Arrived And It Sounds Like The “Next-Gen” Spectacle We’ve Been Waiting For appeared first on Press Start .

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Hellblade 2 will finally show us what an Unreal Engine 5 game can do

Inside Ninja Theory’s mad quest for a new kind of realism

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This console generation has been pretty short of “next-gen moments” — those dazzling, techy epiphanies when you see a game do things that were inconceivable on earlier hardware. You can make a case for Ratchet & Clank: Rift Apart ’s lightning-fast loading or Starfield ’s potato physics , but there have been relatively few instances where you can watch the future arrive in real time.

There are a few reasons for this. One is that console supply issues and pandemic-driven development delays led to an unusually long cross-generational phase. Until last year, most games were still being released on PlayStation 4 and Xbox One as well as their successors. Another is that Unreal Engine 5, the latest iteration of Epic Games’ ubiquitous graphics engine, lagged a little behind the new console generation, and large-scale UE5 productions have been slow to appear, with a couple of exceptions .

All of this is why I wasn’t expecting to experience a next-gen moment when I traveled to Cambridge, U.K., to visit the Ninja Theory studio and play Senua’s Saga: Hellblade 2 . But I got one. It’s an astonishingly lifelike narrative action game that applies UE5’s tech, Microsoft’s resources (the company owns Ninja Theory), and the unique processes of a smallish team of technical artists to create something at once grounded and vividly hyperreal. There’s nothing else quite like it.

This won’t come as a total surprise if you played 2017’s Hellblade: Senua’s Sacrifice . Both Hellblade games blend horrific, quasi-mythological action with a realistic approach to the psychology of their heroine, Senua, an eighth-century Celtic warrior with psychosis. Both games have a photoreal visual style with heavy emphasis on performance capture — an area Ninja Theory has specialized in since collaborating with Andy Serkis on its 2007 action game Heavenly Sword .

Quite a lot has changed for Ninja Theory since 2017, however. In 2018, the studio was acquired by Microsoft. It hasn’t grown much since — with 100 people, around 80 of whom are working on Hellblade 2 , this remains a modestly sized team — but Microsoft’s investment is evident in beautiful new offices with a large, dedicated motion capture studio (and, at the insistence of some extremely British local planning regulations, an in-house pub). On my visit, there was no sign or mention of Ninja Theory’s flamboyant founder and Hellblade writer-director Tameem Antoniades. An Xbox spokesperson later confirmed to Polygon that he is no longer with the studio. Antoniades was involved in Hellblade 2 in the early stages, but the game now has a trio of creative leads: environment art director Dan Attwell, visual effects director Mark Slater-Tunstill, and audio director David Garcia.

You would expect a dedication to craft in any game led by three technical artists, but that still wouldn’t prepare you for the extraordinary lengths Ninja Theory is going to in its pursuit of realism. In Hellblade 2 , Senua journeys to Iceland on the hunt for Norse slavers who are decimating her community in the northern British Isles. As press toured the studio, Attwell explained that the route of her adventure had been plotted in the real world, and locations were captured using a mixture of satellite imaging, drone footage, procedural generation, and photogrammetry. The team spent weeks on location in Iceland, studying the landscape, photographing rocks, and piloting drones. They also studied building techniques of the time and virtually constructed doors out of 3D-scanned planks of wood, rather than modeling them. They even made their own rough wood carvings and scanned them in.

Character art director Dan Crossland showed us real costumes that had been made to fit the actors by a London-based costume designer using period-appropriate techniques, and then scanned in by the studio. Behind Crossland’s desk there was a mannequin plastered in mesh, putty, feathers, and deconstructed scraps of fabric — a spooky, hand-sculpted prototype enemy design.

Over in the combat team’s section, principal action designer Benoit Macon, a very tall and ebullient Frenchman, explained that the game’s fight sequences weren’t traditionally animated, but 100% mo-capped. I watched stunt professionals act out finishing moves on the performance capture stage while animation director Guy Midgley shot them in a close, roving handheld style, using a phone in a lightweight rig.

The playable results of this fully mo-capped fighting system are quite unique. Combat in Hellblade 2 is one on one only, slow-paced, and very brutal. In the fight scenes of the demo I played — which also featured pattern-spotting puzzles and some atmospheric, grueling traversal — there’s a heightened sense of threat as Senua faces hulking and aggressive opponents, and the characters loom large in the unusually tight camera angles. This might not be the over-the-top combat of DmC: Devil May Cry , but it’s still very effective.

In a small, soundproofed studio on the top floor, Garcia worked with the two voice actors playing the Furies, which is how Senua thinks of the voices in her head who keep up a constant commentary on the action and her state of mind. (As with the first game, scriptwriter Lara Derham has worked with psychology professor Paul Fletcher and with people who have experienced psychosis on the portrayal of the condition’s effects.) The actors prowled around a binaural microphone — essentially a mannequin head with microphones for ears — hissing and murmuring their lines as if at Senua herself. Garcia, a Spaniard with an infectious sense of wonder, is called a “genius” by his co-workers. His growling, chattering soundscapes are players’ principal point of access into Senua’s state of mind, and they’re as overwhelming now as they were in 2017.

The lengths to which Ninja Theory is going to ground this digital video game in physical reality might seem quixotic — even contradictory — but the proof is in the playing. The game, which I played on Xbox Series X, looks stunning, whether it’s rendering the black, smoking slopes of an Icelandic volcano or the pale, haunted eyes of Senua performer Melina Juergens. But beyond that, Hellblade 2 has a tactile immediacy that seems to operate at an almost subconscious level. Ninja Theory’s artists are seeking an emotional connection with the player that, they believe, can only form if the player thinks that what they are seeing is real.

“I think that the human mind does [a thing where] you think you know what something looks like, but then actually, when you look at what that thing is, in reality there’s way more chaos in it. It’s not quite the same as what you picture in your head,” Slater-Tunstill said. “If you were just sculpting off the top of your head, the environments or the characters or whatever, it just is going to lose some of that nature, some of that chaos.”

Attwell said that Unreal Engine 5 has made this realist approach more more achievable, both because of the level of fidelity available in the engine’s Nanite geometry system , and because “the turnaround between scanning the thing and putting it in the level is drastically cut, and you can spend that time finessing.”

“You can think more about the composition,” Slater-Tunstill agreed. “And with the kind of lighting volumetrics we can now do, everything just beds in much better. It’s more believable.”

Overall, the sense from the Ninja Theory team is that UE5 has removed a lot of barriers for video game artists, and that players are only just starting to see the results. “It feels like the graphical leap that we’ve managed with this is like... We’re on the trajectory we wanted,” Attwell said.

You only need to lay eyes on Hellblade 2 briefly to understand that you’re seeing the next evolution of game technology. It’s not just the engine, though — there are a bunch of factors aligning to make Hellblade 2 a tech showcase. For one, the game design is extremely focused. This isn’t some wild open-world simulation ; it’s a linear, narrative-first action game. As an Xbox first-party studio, Ninja Theory has the luxury of building for fewer formats. Also, it’s been given the time to experiment. Touring the studio, Microsoft’s investment in Ninja Theory starts to make a lot of sense. The tech giant hasn’t just acquired a boutique developer, but also an R&D unit that explores the technical and artistic frontiers of a specific game-making process.

The result is a game made with an unusual degree of focus. Hellblade 2 won’t necessarily be to everyone’s taste with its slow pace, deliberate inputs, and highly scripted, cinematic presentation. It struck me as a modern successor to something like the 1983 interactive animation Dragon’s Lair . As intense and dramatic as the section I played was, it remains to be seen whether the game’s story — a more outward journey for a more mentally balanced Senua — can connect as deeply as Hellblade ’s trip into her darkest fears. But there’s no doubting the craft on display, or the immersive sense of presence this game has. It may be a sequel, but it feels like the start of something — like a true next-gen experience should.

Senua’s Saga: Hellblade 2 will be released May 21 on Windows PC and Xbox Series X.

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