literature review in mobile technologies and learning

• Review article
• Open access
• Published: 15 March 2018

A critical review of mobile learning integration in formal educational contexts

• Luís Francisco Mendes Gabriel Pedro   ORCID: orcid.org/0000-0003-1763-8433 1 ,
• Cláudia Marina Mónica de Oliveira Barbosa 1 &
• Carlos Manuel das Neves Santos 1  

International Journal of Educational Technology in Higher Education volume  15 , Article number:  10 ( 2018 ) Cite this article

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The use of digital technology in the learning process and teaching practices in formal teaching is highly dependent on the ability of teachers of introducing it without jeopardizing the richness of the classroom environment, namely the attention that students need to follow the flow of argumentation and to guarantee the quality of the inquiring.

Although several studies value the importance of technologies in our media-enriched world and the "learn anytime and anywhere" motto associated with mobile learning, we argue that the classroom dynamics are becoming more and more at risk with the addictive dimension brought about by the ubiquitous presence of digital devices and social media in students’ lives.

In this article, we will make a critical review of the literature related to mobile learning because there is still a need of more extensive research on the interference of technology in the classroom, especially on how multitasking affects the teacher role in-class as a media orchestrator and learning facilitator.

Finally, we will discuss the use of technology in the formal classroom environment, mainly to stimulate a much-needed discussion about the bright-not-so-bright impacts of technology in the teaching and learning process.

Introduction

The introduction of digital technologies in the teaching and learning process is a theme that spans the literature on Educational Technology since the 1980s. Highly associated with the emergence and more consensual acceptance of new pedagogies and a renewed epistemological approach about the nature of knowledge and of its construction, technologies are often depicted as a set of tools that bear in themselves several solutions to the problems of education.

This optimistic view of digital technology came about with the introduction of the personal computer, then the Internet mainly in the 1990s and is still echoed and very much amplified with the possibilities brought by the pervasive and ubiquitous access to mobile devices and social media platforms in the 2000s.

These latter devices and media frame the emergence of a new learning modality, mobile-learning (m-learning), that was defined as “the processes of coming to know through conversations across multiple contexts among people and personal interactive technologies” (Sharples, Taylor, & Vavoula, 2007 : 224).

The definition of m-learning has evolved in different ways and directions since the first decade of the 2000s. According to Baran ( 2014 : 18), the evolution of these definitions has mainly highlighted m-learning positive characteristics such as “mobility (Sharples et al., 2009 ), access (Parsons & Ryu, 2006 ), immediacy (Kynäslahti, 2003 ), situativity (Cheon, Lee, Crooks, & Song, 2012 ), ubiquity (Kukulska-Hulme et al., 2009 ), convenience (Kynäslahti, 2003 ), and contextuality (Kearney, Schuck, Burden, & Aubusson, 2012 )”.

These different emphases reflect the expected but also the unexpected impacts of the introduction of these digital technologies in the learning process. In the history of m-learning, initial definitions were more device-driven (focusing in immediacy, convenience, access and mobility) while the latter ones are more personal and social-driven, exploring affordances that relate to new technological features of mobile devices such as location awareness, motion detection and augmented reality (Baran, 2014 : 18).

Although, theoretically, these definitions encompass the formal, non-formal and informal contexts in which learning occurs, Cheon et al. ( 2012 ) argue that they reinforce the learning that occurs in real settings, i.e., that are not limited to classrooms contexts. But is m--learning supporting or jeopardizing learning in classrooms?

To be able to critically explore these issues is the main aim of this article. We will begin by a brief review of the literature about m-learning research in order to try to understand the undeniable positiveness related to the use of these devices (Al-Zahrani & Laxman, 2016 ; Wu et al., 2012 ). Following, we will discuss the place and value of mobile devices and social media applications in today’s learning, confronting our optimism about this issue with other opinions which question the fit of mobile devices in some educational contexts.

In the following section, we will present some of the teaching and learning impacts associated with the use of these devices in classroom contexts, namely the issues that result from multitasking and that may be troublesome for the students but also the orchestration issues that arise for the teacher in a multidevice and multimedia classroom. Finally, we will discuss different perspectives related to digital technologies integration in the classroom, trying to provoke a necessary and reality-based discussion about these issues.

Mobile learning research overview

This integrative literature review follows some of our previous work in this area, namely developed by Aresta, Pedro & Santos ( 2015 ) but in this one we focused our analysis on the review of meta-analytical studies about m-learning published since 2010 in order to present a snapshot of the existing research in this field. The keywords used in searching articles in the SCOPUS database were m-learning and meta-analy* and we limited the search to articles from 2010 to 2017. A staged review was conducted, beginning with an initial review of all the abstracts, followed by an in-depth review of selected articles according to the relevance of the journals in which they were published. This review shows that the m-learning is an emerging field of research, showing a steady increase in terms of number of publications since the beginning of the 2000 decade. Some meta-analytical papers in the past years (Chee, Yahaya, Ibrahim, & Noor Hassan, 2017 ; Al-Zahrani & Laxman, 2016 ; Hung & Zhang, 2012 ; Wu et al., 2012 ; Hwang & Tsai, 2011 ) show this progression and reveal the focus on studies related to the effectiveness of m-learning followed by m-learning system design (Chee et al., 2017 ).

These trends and foci are somewhat expected, as pointed out by Hung and Zhang ( 2012 ), because they reveal a predictable path since the introduction of a technology to its adoption and integration. According to these authors, “e-learning research is at the early majority stage and foci have shifted from comparing the effectiveness of e-learning to developing models for e-learning environments and for teaching and learning strategies within various e-learning environments. If m-learning articles follow a similar path, we may expect more research studies on strategies and framework (...) in the near future.” (Hung & Zhang, 2012 : 13).

However, one of the first issues that pretty much every study in this field tries to establish is a stable definition of m-learning. Being a relative new field of study and witnessing some technological breakthroughs in its early existence, several definitions have been suggested since the early 2000s. For instance, some authors identified m-learning as a natural consequence of the e-learning evolution (Georgiev et al., 2004 ), but more recent definitions position m-learning as a method that intersects mobile computing and e-learning (Chee et al., 2017 ), that adopts the use of mobile technology to achieve anytime, anywhere, ubiquitous learning (Hung & Zhang, 2012 ) and that emphasizes learners’ mobility and personalized learning (Vázquez-Cano, 2014 ).

In terms of simple bibliometric data, Hwang and Tsai ( 2011 ) reviewed studies about m-learning published in six major research journals related to technology-enhanced learning from 2001 to 2010 and reported that from 2006 to 2010 the number of articles related to MUL (Mobile and Ubiquitous Learning) almost quadrupled in relation to the 2001–2005 period. These figures can be supplemented by the ones developed by Hung and Zhang ( 2012 ) and Chee et al. ( 2017 ) that conducted meta-analytical reviews of m-learning trends from 2003 to 2008 and from 2010 to 2015. Although these authors used different journal databases, the results also present a parallel evolution pattern in the case of the Hung and Zhang ( 2012 ) study and a more modest but still evolutional pattern in terms of number of publications in the time period reported in the Chee et al. ( 2017 ) literature review.

In terms of sample groups, both Hwang and Tsai ( 2011 ) and Wu et al. ( 2012 ) report that published papers show a high prevalence of studies with Higher Education students, followed by elementary school students and K-12 students. Oddly (or perhaps not, as we will argue further) only a few studies in both meta-analyses were related with the use of m-learning from the professors or teachers’ standpoint.

Regarding the educational contexts of m-learning studies, Chee et al. ( 2017 ) report that when those contexts are revealed, informal learning contexts are predominant, followed by formal contexts and a combination of both. This result is consistent with results reported by other authors, namely by Vázquez-Cano ( 2014 ).

Together with the predominance of informal educational contexts in m-learning published research, Hwang and Tsai ( 2011 ) also report that most studies do not focus on a particular learning domain but rather present results related to motivation, perceptions and attitudes of students towards m-learning. Once again, the perceptions and attitudes of teachers are seldom found. These results are aligned with the ones also reported by Chee et al. ( 2017 ) in a more recent analysis.

Finally, in terms of outcomes, Wu et al. ( 2012 ) report that 86% of the studies on m-learning present positive outcomes. This kind of result is also found in Chee et al. ( 2017 : 123) article, which report that “most of the 144 M-Learning studies present positive outcomes. (...) Neutral outcome ranked next and negative outcome ranked the least.”

These results are very representative of a general positive attitude towards m-learning that crosses much of the literature in this field.

The not-so-glamorous issues missing in m-learning research

The brief snapshot of m-learning research of the last decade gave us some important clues about the major topics of research in this field but, more importantly, about the issues that are seldom if not considered at all.

Among the latter, we would like to stress two particular ones: the use of m-learning in formal educational contexts and the integration of m-learning from the professor/teacher standpoint which will be developed in the following sections.

M-learning and formal education contexts

Regarding digital devices use in education, Gikas & Grant ( 2013 : 18) acknowledge that “(...) there is little applied research into how these tools are actually being used to support teaching and learning with few descriptions of how mobile computing devices and social media are used by university students”.

Being widely regarded as both a formal and informal method or set of practices, it is curious that only a few studies report the use of mobile devices or m-learning strategies in formal educational contexts. We noticed that some authors prefer to see m-learning as a shot to bridge formal and informal learning opportunities, valuing, among others, its context-awareness and situated features. Obviously, we also share this opinion and some of our previous work is precisely related to that (Pedro, Aresta, Santos & Almeida, 2015 ). We also agree with Gikas and Grant ( 2013 ) claim whereas much of the literature has been focusing on the affordances of mobile devices to replicate old methods, strategies, and practices that are mainly teacher-centered and transmission-oriented. We find it very difficult to disagree with these findings. As mentioned by Tess ( 2013 ), we also believe that bringing more scholarship into the implementation of technology as a learning resource is necessary.

Nevertheless, it is curious that apparently only a few studies report the results of the use of these devices in the context of class-activities such as a lecture, for instance. Gikas & Grant ( 2013 : 23) report in their study that “(...) traditional college-aged students (...) felt that at times the device could be distracting. The allure of social networking applications that were not being used for class potentially threatened their concentration” but don’t elaborate further on that, presenting next a claim that “(...) older students (...) emphatically stated that the devices were not distracting.” Could this issue be related to the age of students? These authors also report a finding that “(...) while there is not a preponderance of data to support this final implication, there were data to suggest that the student participants also blurred the lines between their personal identity and their mobile computing device. The student participants recognized their need to be constantly near their device.” The tone used on the discussion of this result is, again, very positive. Gikas & Grant ( 2013 , 25) report that being always connected with their mobile devices allowed students to access course information and also gave them the opportunity to interact with the content, potentially contributing to tear apart the existing barrier between learning and real life. Could it be the case that we may be missing something? According to Lepp, Barkley, and Karpinski ( 2015 ), some recent findings suggest a careful consideration of the relationship between cell phone use, and foremost the use of social media, and academic performance.

And what about the overall suitability of mobile devices and social media applications in formal education? Friesen and Lowe ( 2011 : 184), for instance, are outsiders in this optimism game, questioning the suitability of social media for education, claiming that just as “commercialism ultimately render television beyond the reach of education, we conclude that commercial pressures threaten to limit the potential of the social Web for education and learning.”

Reflecting on the commercial priorities of most social media applications, Friesen and Lowe ( 2011 ) argue that the use of these applications could harm education, precisely because they are positively bounded to likeness and agreement, possibly jeopardizing important learning strategies that imply critical inquiry, confrontation, disagreement and dissent. They argue that the “adoption of these platforms would threaten educational dialogue as a process that is central to collaborative learning. The sequence, rhythm, and flow functions of commercial social media present, to use Raymond Williams ( 1974 ) phrasing, ‘a formula of communication, an intrinsic setting of priorities’. The difference separating these priorities (in new social technology) from those of education is clear in the form of social networks, if not also in some aspects of its content.” (Friesen and Lowe, 2011 : 193).

M-learning integration from the teacher’s standpoint

As mentioned before, although many teachers are looking up to technological devices and applications to enhance their classes and promote active learning practices in their students, there are not so many studies that try to understand the integration and the actual results of m-learning practices from the teacher standpoint.

Baran ( 2014 ), for instance, reports on the use of m-learning in teacher education which is a good sign but, still, reports more on preservice teacher’s perceptions and attitudes and not on the integration of m-learning by real practitioners on the field. As argued by Tess ( 2013 : 66), a “(...) reason that may explain the paucity of studies is that SNS (social networking sites) integration is a choice made at the instructor level rather than an institutional decision. As a result, the implementation may be more of a trial that lends itself to action research and ultimately to more questions.”

However, we think that besides the real implementation issues that come about in practice there is an underlying problem to be solved related to the lack of theoretical and pedagogical foundations regarding the implementation of mobile-learning in educational contexts.

Although many authors frame this method on socioconstructivist approaches deriving from the work of Vygotsky and in the appeal of the communication features of mobile devices to set up communities of practice and inquiry and foster collaboration, the fact is that what is participatory and collaborative in any human endeavour is people and not devices or media (Jenkins, Ito & Boyd, 2016 ). Therefore, it seems that, beginning in the teachers themselves but also encompassing students and the overall learning community, there is a lack of true and meaningful participatory culture of mobile devices users that can propel the full promise of technology.

Eijkman ( 2008 ) proposes the term ‘non-foundational network-centric learning spaces’ precisely to define this conundrum. He argues that the use of technology in education is still pretty much centered on an information-driven paradigm and that to fulfill the promise of new media and the devices that support them it is needed, from the part of the teacher and students, a novel approach, centered on collaborative knowledge construction and on a participatory-driven paradigm. This is a major problem because, as reported by Tess’ ( 2013 ) review of literature, the former kind of perception and attitude is still dominant in our schools.

This conundrum has obvious effects in practice. Tess ( 2013 ) reports several studies which suggest that training and guidance is needed for teachers to feel secure to implement this method. Accordingly, Baran ( 2014 : 23) curiously goes a step back and proposes that the problem really begins before, with teachers’ educators, stating that “(...) the literature needs to establish pedagogical and theoretical models that can guide teacher educators in designing mobile learning experiences for preservice and in-service teachers. These models need to present strategies for equipping teachers and teacher educators with methods for integrating mobile learning into classrooms as well as supporting professional learning with mobile tools.”

These are major problems and, in the regular teaching and learning process, we still have not reached the classroom. There, these problems increase in magnitude and several studies report that students rarely used social media for educational purposes (Jones et al., 2010 ), communication regarding coursework was least on the list (Roblyer, McDaniel, Webb, Herman, & Witty, 2010 ) and only 24% of the faculty used SNSs in their courses (Ajjan & Hartshorne, 2008 ).

As stated before, the discussion of the challenges of real integration of m-learning in classrooms is minimal (Baran, 2014 ) and, for instance, there are studies that alert to the fact that the integration of certain practices in classrooms can run, for instance, into a non-existent or incompatible curriculum (Price et al., 2014 ).

Challenges of mobile devices use in the classroom

Digital devices as a distraction in the classroom.

Shirky ( 2014 ), in a lengthy essay on Medium, argued that, although he is an advocate of the use of technology in the classroom, he asked his students to put their laptops, tablets and phones away in classes. The author claims that this decision was made considering that the levels of distraction is his classes were growing despite the existence of two constants: the teacher and the students, which were selected using approximately the same criteria each year. Shirky, then, attributed the cause of this increase of distraction to the pervasive and ubiquitous presence of technological devices in his classroom.

However, this line of thought is not new. Almost 10 years ago, Fried reported this concern referring laptops as likely sources of cognitive overload and distraction and referencing a set of studies that suggested that “[t]he orientation and visual nature of laptops, along with pop-ups, instant messages, movement and lighting of text, and even things like low-battery warnings, make laptops inherently distracting” (Fried, 2008 : 908 for more information).

Although some authors (Selwyn, 2010 ; Siemens, 2006 ) argue that there is, apparently, a new student in our higher education classrooms that is highly connected, collective, and creative and that social and communicative connections may constitute a new form of knowledge that is no more merely instrumental to the learning process (see Friesen & Lowe, 2011 for an entertaining and thoughtful discussion about these topics), there are also other authors (Warschauer, Zheng, Niiya, Cotten, & Farkas, 2014 ) that are questioning the benefits of using mobile devices in the classroom, mainly because of the yet to study effects resulting from its integration.

One of those effects is reported by Shirky ( 2014 ) and by Sana, Weston, and Cepeda ( 2013 ) and it is related with the potential harmful effects on learning resulting from using mobile devices for nearby peers.

According to these authors this effect is particularly serious and Shirky argues that “[t]here is no laissez-faire attitude to take when the degradation of focus is social.”

As a matter of fact, this may be an irreversible and ever-worst problem if, as claimed by Shirky ( 2014 ), digital devices and applications continue to be designed for competing for our attention. In the past years, we witnessed the emergence of very creative forms of notifications in digital environments, beginning in pop-ups and banners, to the most recent badges, roll-ups, and push notifications. These kinds of effects have been suggested in some studies as a possible cause to a negative correlation between electronic media use (including mobile devices) and academic performance as Lepp et al. ( 2015 ) suggest in a thorough review on this issue. Some of these studies also suggest that these effects are not only visible in classrooms but also in homeworking tasks and in the overall quality of time spent studying.

Lepp et al. ( 2015 : 7) argue that more research is needed in these topics because relationship between these variables has been proved but relationship does not mean, necessarily, causality: “[f]uture research should examine the many potential underlying reasons for the negative relationship identified here, including time spent studying and multitasking”.

In the next section, we will present and discuss one of these topics - multitasking - and its effects in educational contexts.

Multitasking and its negative effects on learning

An aspect that is commonly presented as a downside of the introduction of mobile devices in the classroom is the possibility of students engaging in multitasking behaviors with (and within) said devices.

Multitasking has been defined by the American Psychological Association as occurring in those situations “when someone tries to perform two tasks simultaneously, switch from one task to another, or perform two or more tasks in rapid succession”. When multitasking with the use of one medium or more media is considered, the term evolves to “media multitasking”, characterized by Wallis ( 2010 ) as a possible threefold event: (a) between medium and face-to-face interaction; (b) between two or more media; and (c) within a single medium. Baumgartner, Weeda, van der Heijden, and Huizinga ( 2014 ), on the other hand, define it as an activity involving interaction with two different types of media or between one type of media and a non-media related activity, while a bare bones definition by Wang and Tchernev ( 2012 ) presents media multitasking as “multitasking involving at least one media-based stimulus or response”.

A more recent take on media multitasking offered by Patterson ( 2017 ) indicates that “media multitasking can take on many forms, such as multiscreen media multitasking with two or more media devices at once, such as using a smartphone or tablet while other digital media is simultaneously consumed” on a single device. The author differentiates between the terms “digital media” and “digital device” pointing out that “many different modalities of digital media can be consumed on most digital devices”.

Not being a new phenomenon, multitasking has been highly potentiated by the development of digital - especially mobile – devices (Brasel & Gips, 2011 ; Wang & Tchernev, 2012 ; Cardoso-Leite, Green, & Bavelier, 2015 ; Schutten, Stokes, & Arnell, 2017 ). While the behavior itself is, as mentioned, not novel, “what is new are the number and types of digitally based activities in which people can now engage in simultaneously” (Wood et al., 2012 ).

Most of the current university students belong to one of the two highly technological generations: the “Millennials” (the first generation to grow up with digital technology) and the “Centennials” (who have never known a world without computers and cell phones, which they were able to fully integrate into their daily lives), both usually described as tech-savvy and highly engaged with digital technologies which they use for long periods of time and in different combinations. Therefore, digital media consumption and the use of multiple devices apparently does not represent a problem in terms of students’ digital literacy. However, the problem seems to reside in the multitasking effect that results from these multiple uses.

Ophir, Nass, and Wagner ( 2009 ) recorded an average use of four digital media at a given time by the study-participating Stanford students. Patterson ( 2017 ) reports a median level of five different technologies used by students while preparing for an exam. Students also engage in prolonged multitasking behavior for long sittings: in a study conducted by Judd ( 2013 ), 3372 computer sessions of students engaged in self-directed study within an open-access computer laboratory were captured, segmented and analyzed, with the author stating that “multitasking was much more common than focused or sequential behaviors” and was “present in more than 70%, was most frequent in over 50% and occurred exclusively in around 35% of all sessions”.

These are high values but it is worth noting that respondents tend to underestimate the amount of multitasking they engage in (Brasel & Gips, 2011 ), usually self-reporting less multitasking than the recorded through observation, so one can wonder whether these values are still underrepresenting an extensive phenomenon.

While the ability to multitask has traditionally been viewed as a positive attribute and multitasking behavior seems to be on the rise in terms of popularity, several studies have been questioning how multitasking impact learning in a higher education context, taking into consideration that “doing more than one task at a time, especially more than one complex task, takes a toll on productivity” (APA, 2006 ). Distraction – or shared attention – is key in this assessment, since “when we talk about multitasking, we are really talking about attention: the art of paying attention, the ability to shift our attention, and, more broadly, to exercise judgment about what objects are worthy of our attention” (Rosen, 2008 ), which can be affected when new digital technologies are introduced in the classroom.

A reduced efficiency in task completion has been reported when one multitasks in the classroom, with several studies pointing out that tasks performed concurrently require more time for completion and are conducted less accurately than tasks performed sequentially. Bowman, Levine, Waite, and Gendron ( 2010 ) conducted a reading experiment involving undergraduates asked to read a 3828-word passage on a computer monitor, split in three groups: one engaged in instant messaging (IM) before reading the passage, a second group engaged in IM while reading the passage and a third group who did not engage in IM at all. The students who engaged in IM while reading took between 22% and 59% longer to read the passage than the other groups, even after deducting the time spent messaging. The underlying concept is that there is an added time needed to switch back and forth between the tasks. Subrahmanyam et al. ( 2013 ) conducted one experimental study focused on the exploration of the effect of medium and opportunities to multitask while reading two different passages (on paper, tablet, or laptop) and while multitasking or not. The authors report that while the reading medium did not have a significant statistical impact, “those who multitasked took longer to read” and “it may simple be less disruptive if one multitasks on a medium/device that is separate from the reading medium”.

Therefore, multitasking with digital devices (mobile phones, tablets or laptops) can have negative impacts on the learning outcomes, leading to a poorer academic performance, while further studies have highlighted how a high use of social media has negative impacts on academic engagement. Wood et al. ( 2012 ) tried to assess the learning outcomes of 145 University students, divided into 3 groups (paper-and-pencil note-taking, word processing note-taking and a natural use of technology condition) following off-task multitasking with social media and communication tools (Facebook, MSN, email and texting) when learning from classroom lectures. The authors concluded that the participants who chose not to use technology or used minimal amounts outperformed the participants who opted to engage in intensive multitasking. Furthermore, the participants involved with off-task activities with both Facebook and MSN engaged in more off-task activities than the two tasks assigned to them and more than the other participants, which the authors link with the somewhat attractive, engaging and interactive character of the activities provided by both platforms. Junco ( 2012 ) investigated the relationship between the frequency of multitasking with some ICTs and academic performance measure by semester grade point average (GPA), concluding that while multitasking with Facebook and text messaging with cell phones negatively predicted overall semester GPA, multitasking with other ICTs (such as email, information search, or instant messaging) did not. The author links this to either the "characteristics of the technologies themselves or by qualitative differences in how the technologies were used by the students", with Facebook, messaging and texting being used mostly for social purposes and emailing and searching for academic ones.

However, multitasking behaviors with media devices have the potential to affect not only users but also nearby peers. In a couple of experiments, Sana et al. ( 2013 ) investigated whether multitasking with a laptop would hinder learning of both multitaskers and their peers. In the first experiment, 44 undergraduates were asked to attend a university lecture and take notes with their laptop and further instructed to complete a series of non-related online tasks at any point during the lecture, mimicking the typical student web browsing habits. A post-lecture comprehension test containing 20 questions to evaluate simple knowledge and a further set of 20 question to evaluate application of knowledge was conducted with the purpose to measure learning. The authors concluded that participants who multitasked on the laptop scored significantly lower in the post-lecture comprehension test than the ones who did not multitask. In a second experiment, a new set of participants (38) was asked to take notes of the lecture using paper and pencil, some seated in view of multitasking peers and others with a distraction-free view of the lecture. The participants who were in view of multitasking peers scored significantly lower in the comprehension test, than the ones who were not. All these detrimental effects associated with multitasking - affecting not only students who willingly multitask but also those nearby - raise a significant challenge for instructors: can the advantages brought about by the inclusion of mobile devices in a classroom setting still be harnessed, while avoiding the potential for distraction they may pose, and how? The aforementioned studies report also on multitasking practices with non-digital devices. Therefore, more research is needed in order to find if these results are transferable to m-learning cases and if, as Selwyn ( 2009 : 368) puts it, “(…) digital technologies may be contributing to an increased disengagement, disenchantment and alienation of young people from formal institutions and activities. For example, young people are derided as being more interested in using digital technologies such as the internet or mobile telephony for self-expression and self-promotion than for actually listening to and learning from others”.

Orchestration: The need of a new skill for the teachers’ role

When discussing the effects of integrating mobile devices with teaching and learning on students’ learning performance, Sung et al. ( 2016 : 266) bring about the concept of orchestration as an important topic. They define it as “the efforts of building harmonious relationships among components to enable compatible, efficient, and effective technology-enhanced teaching and learning environments”. The components mentioned by Sung et al. ( 2016 ) were previously proposed by Dillenbourg ( 2013 ) and Dimitriadis, Prieto, and Asensio-Pérez ( 2013 ) and include technological components (hardware and software), educational context components (for instance, learning and teaching processes in different settings) and finally components related to users (teachers and students).

This is an old discussion. Many technological implementations in the past were not very successful because of a lack of attention to one or more of those components. One of the causes that is traditionally suggested for this problem is the lack of preparation of teachers. Solutions, therefore, point to the inclusion of mobile-enhanced instruction modules in teacher education programs (Sung et al., 2016 ).

While we agree with this claim it is our opinion that, perhaps once again, we may be overlooking the complexity of this issue by overvaluing technological issues. Theoretical, pedagogical and methodological issues related to the integration of these devices in educational contexts should be more valued. Educational contexts are ecological by nature and the addition of one more element does not mean that we have the old environment plus one element. The whole environment changes so all the elements of the ecosystem must adapt to the new conditions (see Gibson, 1986 for a thorough discussion of this topic). When this new element is technological this ecological nature is even more visible. Media devices are very powerful in terms of its appeal and bring about new spaces, times and geographies to the classroom. Teachers, but also students and the overall community, must be prepared to integrate these devices to extend learning opportunities but not forgetting that, in order to learn, along with discovery learning methods there must be inquiry, debate but also explanation and lecturing.

Regarding the role of the teacher, as stated by Shirky ( 2014 ), “[t]his is, for me, the biggest change — not a switch in rules, but a switch in how I see my role. Professors are at least as bad at estimating how interesting we are as the students are at estimating their ability to focus. Against oppositional models of teaching and learning, both negative - Concentrate, or lose out! - and positive - Let me attract your attention! - I’m coming to see student focus as a collaborative process.”

Dimitriadis et al., 2013 : 497) sum up this discussion with a definition of orchestration that encompasses both the ecological nature of technology in the teaching and learning process and the new skill of the teachers’ role: “From these discussions we can see a new trend raising, which we believe lies in the heart of the use of the orchestration term: that of how teachers (and/or students) appropriate and integrate in their practice the different technologies at their disposal (either digital or paper-based, generic or intended for orchestration).”

The orchestration of devices, methods and the constant adaptation to the reality of students and the class dynamic is an ongoing and collaborative process. Maybe teacher education programs should also be.

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DigiMedia, Department of Communication and Arts of the University of Aveiro, University of Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal

Luís Francisco Mendes Gabriel Pedro, Cláudia Marina Mónica de Oliveira Barbosa & Carlos Manuel das Neves Santos

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LP conceived the study, developed the paper and conducted the literature review and reflection on mobile learning. CB conducted the literature review and reflection on multitasking. Both authors read and approved the final manuscript. CS developed the first draft of the paper.

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Correspondence to Luís Francisco Mendes Gabriel Pedro .

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Luis Pedro holds a PhD in Educational Technology (2005, University of Aveiro, Portugal). He currently is an Assistant Professor at the Department of Communication and Arts, University of Aveiro, Portugal. His research interests are related with social media development, integration and assessment in educational and training contexts, which have been developed in several MSc and PhD supervisions and through the coordination and participation in externally funded research projects.

Cláudia Barbosa has a graduate degree in Teaching of English and German, from the University of Aveiro, where she is currently working towards a PhD in Multimedia in Education. She has been involved as project manager in several FP7, H2020 and other international and national funded projects. Her current research interests lie in the use of technologies to support language teaching and learning and media multitasking.

Carlos Santos is an Assistant Professor at University of Aveiro, and holds a PhD in Information and Communication in Digital Platforms. Since 2009, he is the coordinator of the SAPO Campus research project ( http://campus.sapo.pt ). Since 2016, he is the technical lead researcher of the Global Portuguese Scientists (GPS) platform ( http://gps.pt ). His research interests are related with Personal Learning Environments, promotion of Web 2.0 tools in educational contexts, gamification, recommendation systems and technology for building communities.

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The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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Pedro, L.F.M.G., Barbosa, C.M.M.d. & Santos, C.M.d. A critical review of mobile learning integration in formal educational contexts. Int J Educ Technol High Educ 15 , 10 (2018). https://doi.org/10.1186/s41239-018-0091-4

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DOI : https://doi.org/10.1186/s41239-018-0091-4

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Literature Review in Mobile Technologies and Learning

Naismith, L., Lonsdale, P., Vavoula, G. & Sharples, M. (2005)   Literature Review in Mobile Technologies and Learning . Report 11, NESTA Futurelab. Bristol: NESTA Futurelab.

Mobile technologies are a familiar part of the lives of most teachers and students in the UK today. We take it for granted that we can talk to other people at any time, from wherever we may be; we are beginning to see it as normal that we can access information, take photographs, record our thoughts with one device, and that we can share these with our friends, colleagues or the wider world. Newer developments in mobile phone technology are also beginning to offer the potential for rich multimedia experiences and for location-specific resources.

The challenge for educators and designers, however, is one of understanding and exploring how best we might use these resources to support learning. That we need to do this is clear – how much sense does it make to continue to exclude from schools, powerful technologies that are seen as a normal part of everyday life? At the present time, however, the models for using and developing mobile applications for learning are somewhat lacking.

This review provides a rich vision of the current and potential future developments in this area. It moves away from the dominant view of mobile learning as an isolated activity to explore mobile learning as a rich, collaborative and conversational experience, whether in classrooms, homes or the streets of a city. It asks how we might draw on existing theories of learning to help us evaluate the most relevant applications of mobile technologies in education. It describes outstanding projects currently under development in the UK and around the world and it explores what the future might hold for learning with mobile technologies.

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Literature Review in Mobile Technologies and Learning

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Poornima Ram

Norbert Pachler

"The volume features a selection of research papers presented at a symposium on mobile learning which was organised by and took place at the WLE Centre on February 9th 2007 and brought together leading researchers and practitioners in the field from the UK and Continental Europe. Unlike many other events on mobile learning, the symposium deliberately focused on learning, rather than on technology, and contributions came from invited speakers, rather than through an open call. The symposium attempted to take stock of where mobile learning was at as a field of research as well as to start to delineated a future research agenda, which is exactly what the various contributions to this volume, in their different ways, attempt to do. This is particularly important in view of the considerable challenges that confront research into mobile learning such as: the relative breadth of possible definitional bases, the rapid obsolescence of relevant technologies, its temporal and geographical distributedness, the lack of appropriacy of traditional research paradigms or the complex ethical issues involved. The symposium as well as this publication testify to the fact that the field of mobile learning has outgrown its infancy and is a maturing field in research terms as well as in terms of its conceptualisation."

British Journal of Educational Studies

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Mobile Technologies in Education

Lyubomir Serafimov

The lowering prices and the growing functionalities of the mobile technologies combined with the increasing need of interactions not only between people but also between people and various kinds of information have led to significant broadening of the mobile technologies usage. Once used mainly for voice and messaging services, they are now tools for socialization, entertainment, shopping, work, and learning. The application of mobile technologies in the learning process was regarded a novelty 10 years ago but now it is more and more widely considered common, and, in numerous cases, very important. The purpose of this paper is to present an overview of some key aspects of the mobile technologies applications in the learning process – brief history and definitions, current state, and tendencies.

Abdul Jabbar , Sue Folley

Mobile learning is not a new concept, learning whilst on the move or away from formal educational settings has been happening a long time with the use of books, journal articles, television, radio etc. However what is new is the vast array of devices now available to access learning and to take advantage of being mobile but still being able to capture information and share, collaborate and upload it to a worldwide audience. Mobile learning (mlearning) is a growing area of pedagogic research, however much of the research so far has focused taking traditional types of instructional learning and putting it on mobile devices, rather than using mobile devices for increased engagement in teaching sessions or for using the devices for capturing information as and when the students are mobile. Herrington et al (2009) claim that although mobile technologies have the potential to be used as powerful learning tools within higher education, “their current use appears to be predominantly within a didactic, teacher-centred paradigm, rather than a more constructivist environment” (p.2). Most mobile learning devices are centred around social learning, e.g. mobile phones, so their pedagogic potential needs to be further explored. So what is mlearning? Is it to do with use of mobile phones or just the student being able to access information whilst mobile? Kukulska-Hulme & Traxler (2007) define mobile learning as “learning delivered or supported solely or mainly by mobile technologies. These include hand-held computers, PDAs, mobile phones, smart phones wireless laptop pcs and personal media players such as the iPod.” (p.181). However they go on to say that this definition is bound by current technologies, and as technology is constantly changing and improving, we should be flexible in our definition of mobile learning.

Handbook of Research on Human Development in the Digital Age

Josh Herron

With an awareness of the unique characteristics of an increasingly mobile world and referencing socio-material mobile learning frameworks, this chapter will provide an overview of the initial stages and growth of mobile learning. The authors also discuss university initiatives to support mobile learning, and examine the implications of mobile technologies for teaching and learning. Additionally, the chapter will introduce a case study detailing the Mobile Learning Innovation at Anderson University (SC) and highlight its impact on the teaching and learning culture on its campus.

Yiannis Laouris

Abstract In this paper we challenge current definitions of mobile learning and suggest that the direction of progress, both in theoretical/applied research as well as its role as a tool that serves social transformation and development, will be determined and even dictated by the availability of an adequate definition.

Ferial Khaddage , Kim S Flintoff , Barry F . A . Quinn PhD MSc BDS LDS MRD FDS FFD FHEA FDTFEd FFGDP FNCUP , Immo Kortelainen , Lucila Pérez

During the Fourth International Summit on ICT in Education (EDUsummIT, 2015) which was held in Bangkok, Thailand, members of the Thematic Working Group 2 (TWG2) discussed methods, strategies, and guidelines for some of the issues and challenges in the design, implementation, evaluation, and policy development of mobile learning. Some major key challenges were highlighted and discussed along with issues that policy makers, teachers, researchers, and students are facing in mobile learning. Based on the outcome from the framework that identified barriers and limitations along with dynamic criteria for mobile learning implementation, which was the outcome of TWG2 from the EDUsummIT 2013 (Khaddage et. al., 2015), the group briefly summed up major challenges and identified possible solutions that could be applied to solve these challenges. The implemented framework classified challenges into four categories: Pedagogical challenges, technological challenges, policy challenges and research challenges. Any new technology leads to new pedagogies, new policy and new research; these four factors combined can form a solid infrastructure that may help adopt effective ways of mobile learning application (refer Khaddage et. al., 2015 to read more about the model). All evolutionary change usually takes place in response to ecological interactions that operate on the overall ecosystem, and in this case the interaction is obvious between these four challenges and they can allow the understanding of the structure and function of each one of them. Understanding the relationships between these challenges are essential for a proper mobile learning integration and a successful mobile learning ecology (Zhao & Frank, 2003). Mobile learning as a concept and theory has evolved rapidly, it is no longer considered technocentric (devices and technologies), it is more about the learner’s mobility and how we as educators can engage them in learning activities without them being wirely restricted to a physical location. Hence comes the challenge of finding appropriate and effective methods to blend formal and informal learning as seamless learning can occur anytime, (formal in‐classroom, or informal outside classroom).

Technology has played a significant role in changing the face of higher education. Mobile technologies are playing an increasingly important role in college students' academic lives. Devices such as Smart phones, tablets, and e-book readers connect users to the world instantly, heightening access to information and enabling interactivity with others. Applications that run on these devices, let users not only consume but also discover and produce content. As such, they continue to transform how college students learn, as well as influence their learning preferences, both within and outside the classroom. A complex relationship exists between education and technology. The learners of 21 st century live technologically integrated lives. They do not distinguish between cellular telephones, text messaging devices, cameras, internet browsers, email readers, music players and satellite navigation systems. They just carry them in their packets. Mobile learning (m-learning) is an extension of e-learning that includes the use of technology that can be carried easily in a pocket or purse, used 'on the go', turned on instantly without the need to boot up, is internet capable through a wifi connection and other features such as word processing ability, html browser, SMS messaging, camera, MPs players, GPS etc., The popularity of mobile technologies among college students is increasing dramatically. Many universities now use mobile technologies and create mobile-optimized versions of their websites or build stand-alone applications that can be downloaded from mobile application stores. This paper will help us to understand how mobile technology very much useful in the field of Education.

Klaus Rummler

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RPA as a Challenge Beyond Technology: Self-Learning and Attitude Needed for Successful RPA Implementation in the Workplace

• Open access
• Published: 01 April 2024

Cite this article

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• José Andrés Gómez Gandía 1 ,
• Sorin Gavrila Gavrila   ORCID: orcid.org/0000-0002-7574-5504 1 ,
• Antonio de Lucas Ancillo 1 &
• Maria Teresa del Val Núñez 1  

Companies are immersed in a process of digitalization that transforms business models and creates value due to the increase in technology. The adoption of new technologies has a great impact on organizations, not only at an economic level but also on their products, processes, and human resources. This process will result in a series of necessary changes to align with their internal competencies and optimize the investment made. This digitalization generates a digital transformation that affects both large companies and SMEs, with the result that new technologies are subject to continuous change, requiring the development and training of workers with the necessary skills to cope with it. Within this transformation, the automation of processes is a constantly growing topic in the business world, as it generates a series of benefits for organizations that they would not otherwise be able to acquire. Process automation reduces the workload in repetitive processes and provides more time for employees to attend to end-customer requests. The adoption of this technology will provide the company to be adapted to a changing world experiencing an increase in productivity, effectiveness, and efficiency. This research focuses on how the process automation provides the organization with a wide range of benefits such as workload reduction and increased productivity for most of the company. Although process automation can bring many benefits to the workplace, it is important to recognize that its use does not always automatically lead to a systematic improvement of workers’ skills. In this context, it is also important to note how employee training is necessary to face this new reality. Employee training and adaptation is critical to the organization’s sustainability. Training will need to be aimed at equipping the employee with technical skills to enable them to effectively use and implement technology and to assimilate it as a complement and not as a threat. To analyse the individual’s awareness of the digitization of the workplace, the automation of tasks and the advantages or disadvantages that may result from the introduction of technology, a questionnaire was developed, and 103 valid responses were obtained and analysed. This has resulted in a series of hypotheses that have been tried to be validate throughout the research work. These results have important implications for organizations seeking to implement automation and provide a basis for future research in this constantly evolving field.

Avoid common mistakes on your manuscript.

Introduction

Digital transformation has emerged as a critical element for the survival and advancement of contemporary businesses, presenting challenges in the integration and exploration of new technologies (Rêgo et al., 2021 ). Industry 4.0 technologies have revolutionized global manufacturing trends, with businesses adopting Industry 4.0 models to meet customized demands and compete globally (Jamwal et al., 2021 ).

This digital transformation has created more intricate working conditions, necessitating upskilling of employees to meet the demands of increasingly specialized and complex jobs (Moore et al., 2020 ). Companies must cultivate a culture of learning in their work environment (Hochhauser, 2018 ) to develop innovative products, services, and processes, restructuring knowledge for technological innovations. It is relevant to consider the impact and employee perception of daily technology use (Bag et al., 2021 ; Hameed et al., 2021 ; Yuan & Cao, 2022 ).

The significance of digital technology policies in economic growth is particularly crucial in the era of the 4th industrial revolution (Zhao & Yang, 2023 ). The impact of digital technology and its adoption for organizational innovation is a central area of study (Li et al., 2023 ), as markets evolve with changing customer needs accelerated by technology dynamics, compelling companies to adapt (Nyagadza, 2022 ).

Given the exponential growth of technologies, diverse approaches must be considered. The digital transformation process involves individuals related to organizational culture and leadership in companies (Velyako & Musa, 2023 ). The development of technological capabilities relies on individual actions at all management levels, emphasizing technical competencies and interactions between managers and employees (Feeny & Willcocks, 1998 ; McLaughlin, 2017 ).

The use of AI provides new opportunities for organizations to innovate their value proposition. AI, particularly in combination with increasing production linkages, assists companies in responding specifically to customer needs and personalizing their range of services (von Garrel & Jahn, 2022 ). This research underscores the value of robotic process automation (RPA) in the globalized digital world, considering new applications and perspectives for business strategy. RPA, an innovative technology, automates repetitive, rule-based tasks previously performed by humans, providing savings to implementing organizations (Ivančić et al., 2019 ; Syed & Wynn, 2020 ).

As AI, including other AI-based applications, integrates into human resource management (HRM) approaches, it opens possibilities for innovation. However, it has the potential to impact the labour market by replacing certain jobs, including those involving cognitive elements (Dwivedi et al., 2019 ). The ongoing advancement of digital technologies, coupled with changes in production structures, affects global worker well-being (Aryal et al., 2019 ; Parteka et al., 2024 ).

Current technology, utilizing big data and machine learning, enhances machines’ ability to perform cognitive, physical, and linguistic tasks, creating new jobs (Gibbs, 2022 ). In this technological context, while the internal workings of these systems remain generally unexplained (Gligor et al., 2021 ), workers must upgrade and retrain themselves to coexist with AI systems (Jaiswal et al., 2022 ). Among technologies, AI stands out, radically changing how work is done and who does it. The biggest impact will be to complement and augment human capabilities, not replace them (Wilson & Daugherty, 2018 ). AI is faster, accurate, and rational, but lacks intuition, emotion, and cultural sensitivity—qualities that make humans more effective (De Cremer & Kasparov, 2021 ).

One prominent challenge in adopting AI within organizations is the lack of information about the purpose of its incorporation (Babic et al., 2020 ). Therefore, bridging the existing gap in the current literature through practical research linking employee digital training on digitization and robotic process automation (RPA) and its impact on daily job performance becomes essential. Additionally, exploring the influence of digital skills in society and their implications in the business area will be investigated.

This research begins with the “ Literature Review and Theoretical Framework ” section that raises the theoretical framework where digitalization and its relevance in the organization are raised. At this point, different approaches linked to the influence of training in the development and implementation of new technologies are considered. In the “ Methodology " section, the methodology is developed. In the “ Analysis and Results ” section, the results are presented and developed. The “ Discussion and Conclusions ” section develops the discussion and conclusions of the research considers limitations and future lines of research.

Literature Review and Theoretical Framework

With digital technologies shaping competition in many industries, predicting the future of potentially disruptive technologies becomes an essential task for business leaders concerned about the survival and success of their organizations (Krotov, 2019 ). Digitization is used to describe some social and technical phenomena, as well as the process of adoption and use of digital technologies across a broad spectrum whether individual, organizational, or societal (Legner et al., 2017 ).

Theoretical Framework

The study has been developed from different sources with a research work, examining the areas to be addressed by the research in terms of process automation and the impact produced not only in the organization but also in the workers.

As indicated in the literature consulted, the methodology used in empirical research can be qualitative, quantitative, or a mixture of both to obtain data that will provide us with the necessary information to carry out the desired analysis.

The paper proposes a qualitative methodology, defining it as “a systematic method for collecting information from a sample of entities in order to construct quantitative descriptors of attributes of the general population of which the entities are members” (Groves et al., 2011 ). Non-numerical data are collected, which can provide us with more varied information that can provide us with a complete explanation (Jansen, 2013 ). Both Glaser and Strauss ( 1967 ) and Becker ( 2008 ) as well as Robinson ( 1951 ) will be mentioned as precursors in the use of this methodology. In this type of qualitative research, both the data collection and the research question are developed in interaction with the data analysis (Maxwell, 2012 ).

The digitalization of organizations, as outlined in various studies (Horlacher et al., 2016 ; Shet & Pereira, 2021 ), has the potential to bring about substantial changes by transforming business models and creating value. Managers are required to possess specific skill sets to navigate the constant organizational changes driven by automation (Shet & Pereira, 2021 ). Digital transformation, defined as the changes brought about by digital technologies affecting a company’s business, involves shifts in products/services and organizational structure (Horlacher et al., 2016 ). However, preparing for digital transformation is a complex task, necessitating the development of aligned digital capabilities within the organization, including its people and culture, towards a set of organizational objectives (Kiron et al., 2016 ).

Continuous learning is highlighted as a crucial requirement, with managers responsible for upgrading their employees’ skills for technology-driven jobs (Popkova & Zmiyak, 2019 ; Shet & Pereira, 2021 ). Successful digital transformations demand the cultivation of new organizational capabilities for survival and success (Li et al., 2018 ). Notably, an incremental approach is recommended, focusing on augmenting human capabilities rather than replacing them (Davenport & Ronanki, 2018 ).

Furthermore, digital transformation should be perceived as a change in organizational mentality, emphasizing innovation and the creative capacity of its people (Vilaplana & Stein, 2020 ). Managers play a crucial role in maintaining collaboration within teams and motivating them effectively (Shet & Pereira, 2021 ).

In terms of new insights, the impact of process automation on human resources and organizational processes is emphasized. The shift towards more specialized IT profiles may displace low-skilled positions, and AI applications are expected to fill permanent jobs, with short-term tasks being outsourced (Braganza et al., 2021 ). This evolution may lead to resistance from workers in easily replaceable roles, necessitating effective communication from managers to mitigate rejection (Arslan et al., 2022 ; Li et al., 2019 ). The economic impact of these changes on organizations is highlighted, reinforcing the importance of strategic planning for digital transformation. All references are based on reputable sources and contribute to a comprehensive understanding of the challenges and opportunities associated with digitalization in the organizational context.

Figure  1 shows how the three areas studied in this research work are around the threat of losing the job by automating the processes involved. It can also be seen the different questions that have been used to pose the hypotheses of the research.

Evaluation methodology. Own elaboration

Digital Transformation

Companies that have invested in digital innovation in recent years now find themselves in need of an alignment of their internal competencies to optimize return on investment, recognising that they need to adapt rapidly to new market conditions (Abele et al., 2015 ) and adapt to a data-driven approach to decision-making (Tiwari & Raju, 2022 ) using data and data analytics to inform business decisions (Pisoni et al., 2023 ).

The adoption of new technologies has always been a great challenge for organizations, and the greater the impact, the greater the challenge. The scale and pace of digital transformation makes investments in digitization inevitable for companies of all sizes and sectors (Hossnofsky & Junge, 2019 ). In highly competitive environments, organizations cannot maintain their advantage without innovation (Ranjbar et al., 2020 ). The process of digital transformation affects people who are connected to organizational culture and leadership in companies. One of the purposes of this research is to examine the role of digital organizational culture with respect to digital transformation and the development of the firm’s competitive advantage (Velyako & Musa, 2023 ). Digital transformation is being influenced by various technologies (Nosalska & Mazurek, 2019 ; Siderska & Jadaan, 2018 ). Therefore, the continuous advancement of both AI and robotic process automation provides companies with competitive advantages and market dominance (Kot & Leszczyński, 2019 ). The goals and objectives of the organization are influenced by digital transformations in operations, which affect the organization’s products, processes, structure, and business concept (Matt et al., 2015 ). This advancement influences daily work routines and consequently working conditions (Metall, 2015 ). Trends ( 2016 ) indicate that digital technologies are currently everywhere, modifying business models and radically transforming the workplace and how work is performed.

Currently, there has been an increase in technologies that have impacted and driven digital transformation (Hofbauer & Sangl, 2019 ). In turn, within the organization, AI is understood as a multidisciplinary science capable of being applied to the development of new business strategies, relevant for the survival and continuity of the business (Blanco-González-Tejero et al., 2023 ). The advancement of digitization has equally affected individuals as well as SMEs (Gavrila Gavrila & de Lucas Ancillo, 2021 ) or large companies. Even though companies have been pushed towards digital change, they are also the enablers of business transformation, creating new sources of opportunities as well as a threat to those who do not adapt (Kane et al., 2015 ). The use of new technologies gains relevance in the day-to-day operations of the company, as well as Big Data and communications due to their great potential in the business world (Prinz et al., 2016 ). Consequently, tools are constantly changing, as well as the knowledge and skills required to use them. There are arguments that the introduction of AI in the workplace can also lead to the creation of new jobs, especially in sectors focused on the development and application of AI technology (Puzzo et al., 2020 ). With a digital mindset, employees throughout the organization will be equipped to seize all the opportunities that come their way (Neeley & Leonardi, 2022 ). Technology is an ever-changing environment in which companies are subject to continuous change (Corso et al., 2018 ). In this environment, it is necessary to have a change management where one of the most important points is the development and training of the people in the organization with the necessary skills (Kohnke, 2017 ); consequently, the following hypothesis has been proposed:

H1: Employee training is a driving force that impacts on work automation

RPA in the Work Environment

The concept of robotic process automation (RPA) is a concept that has been implemented in organizations for some time now, focusing on efficiently and automatically resolving large administrative and back-office processes (Madakam et al., 2019 ; Wang et al., 2022 ). Its main application lies in tasks, mainly administrative and systematic, on established information systems, where the cognitive activity required is limited (Penttinen et al., 2018 ). RPA does not require the teardown of existing systems, making it easy to integrate into existing business processes (Shet & Pereira, 2021 ).

Automation has increased due to the growing use of information technologies, disrupting the labour market. Even though jobs consist of multiple tasks, some of them can be replaced by robots, but it is necessary to think about how humans can complement this automation (Autor, 2015 ). Automation is replacing those individuals who perform routine and repetitive tasks of low complexity with robots programmed to carry them out (Doménech et al., 2018 ). Therefore, it is important to establish a systematic approach to RPA implementation, which includes process identification and prioritization; cost and benefit evaluation, role, and responsibility definition; change management; and performance measurement and analysis. According to Siderska ( 2020 ) and Eloundou et al ( 2023 ), RPA is a driver of digital transformation and can provide several benefits to an organization, including (a) efficiency and productivity (Hou et al., 2023 ; Wang et al., 2022 ), (b) accuracy, (c) cost reduction, (d) improved customer experience (Hou et al., 2022 ), and (e) increased scalability enabling managers to use RPA as a driver for manufacturers to improve productivity, meet consumer expectations, and continuously drive low-cost product innovation (Shet & Pereira, 2021 ). In this line, Porter and Kramer ( 2011 ) suggest that the adoption of this technology can improve efficiency and productivity, which, in turn, can allow companies to invest in higher value-added areas and ultimately create higher-quality and better-paid jobs.

Therefore, it is crucial to provide an overview of the critical factors that contribute to the success of implementing RPA as part of an organization’s digital transformation. In this regard, authors such as Leopold et al., ( 2018 ) have pointed out key characteristics such as repetitive, manual, rule-based, and high-volume tasks as good indicators for the implementation of RPA technologies. This is because the use of RPA allows employees to focus on more complex tasks that require creativity and can bring more value to an organization (Siderska, 2020 ). The impact of new technologies such as AI on workers’ skills is likely to depend on the specific tasks and skills to be automated (Chuang, 2022 ). However, each organization should evaluate its own processes with the aim of using technology to improve human work, enabling workers to focus on higher-value tasks. Therefore, it is essential to analyse the advantages that RPA implementation brings to the company therefore the following hypothesis has been proposed:

In terms of automation:

H2a: Process automation can be perceived as a threat by being more efficient than the worker.

H2b: Workers’ creativity increases in digitized workplaces when they are properly trained and coexist with automation systems.

Digital Education

Organizations seem to realize every day the advantages of a symbiotic relationship between workers and AI in the workplace. The organization should foster a culture of continuous learning and development by upskilling employees. This relationship requires workers to develop the technical, human, and conceptual skills necessary for the adoption of AI in the workplace, and organizations must invest in continuous training for their workers, taking time to update and retrain their skills in today’s workplace (Zirar et al., 2023 ). This will result in and provide motivation for employees and help the organization to attract and retain talent within the organization. Furthermore, a culture of continuous learning and development will help organizations meet changing business needs and remain competitive in a highly changing environment (Cukier, 2020 ) (Fig. 2 ).

Phases of adopting AI in the workplace

Digital transformation affects the sustainable growth of companies from a perspective of dynamic capabilities (Yao et al., 2022 ). The future trend will lead to the disappearance of numerous job positions, of which those that do not disappear will undergo significant changes as automation takes place (Review, 2020 ). This may suggest that there is a “trust” problem among workers and it can be argued that this problem may progressively improve as workers improve their skills (Gillath et al., 2021 ). Employees must feel comfortable and find it useful, relevant, satisfying, and easy to use. The employees’ perception of the technology and its features is one of the factors to be considered in the learning process of this technology. Therefore, training is a fundamental aspect to achieve effective implementation of digitalization in both public and private companies. In this sense, several authors have considered the influence of skills training on performance and organizational culture (González-Tejero & Molina, 2022 ). Organizations need to develop clear and compelling value propositions so that employees appreciate the benefits of acquiring new skills and learning to use AI systems (Sofia et al., 2023 ).

The Human Resources department has become a vital service in the paradigm shift of employee training. According to Bersin, ( 2016 ), digital disruption and social networks have transformed how organizations handle the hiring, management, and support processes for their personnel. Thus, this department acts as an intermediary between employees and digitalization, as it is important to incorporate digital elements into the way work is done to perform tasks effectively, considering that many employees live in a digital world (Bersin, 2016 ).

In this context, automation is expected to soon transform jobs, workplaces, and workforces (Mashelkar, 2018 ), where digitization and automation will lead to the replacement of some existing jobs with new ones requiring entirely new skills. The importance of analytical skills will decrease as AI assumes more analytical tasks, tasks that require rule-based and logical thinking. There are arguments that suggest that the introduction of AI in the workplace may also generate new jobs, especially in sectors centred on the development and application of AI technology (Puzzo et al., 2020 ). Workers are faced with a greater complexity of their daily work tasks and are required to be resilient and adaptable to new (and challenging) work environments (Longo et al., 2017 ). The adoption of AI will affect both knowledge workers and blue-collar workers, as AI has the potential to improve worker productivity (Leinen et al., 2020 ).

In this case, a distinction is made between white-collar and blue-collar workers (Gibson & Papa, 2000 ; Waschull et al., 2022 ). Blue-collar workers are not as exposed to automation of their jobs because of the physical and manual nature of their work, which is complex to replicate through automation. However, white-collar workers are more susceptible to job loss due to the integration of RPA into work environments. These workers perform cognitive and analytical tasks that RPA has begun to address effectively.

In the next years, both company executives and employees will face a series of challenges as they encounter disruptive digital technologies. People will need to upskill appropriate capabilities for newly defined jobs and work closely with AI technologies to do well in their employment (Jaiswal et al., 2022 ). In recent years, numerous systems, including web applications and apps, have been designed to facilitate human resource management activities and identify skills gaps in the workforce in order to introduce AI solutions effectively (Sofia et al., 2023 ). The importance of analytical skills will decrease as AI assumes more analytical tasks, tasks that require rule-based and logical thinking. They will need to learn different important skills, with digitalization and dealing with increasingly omnipresent digital technologies standing out, as well as developing empathy towards their colleagues’ technological preferences (Agrawal, 2018 ). Thus, the learning of new technological competencies is crucial for digital transformation, but it also needs to consider that employees are motivated to use them (Neeley & Leonardi, 2022 ). Building on contributions like those made by Seibt and Vestergaard ( 2018 ), it should be acknowledged that there will be close collaboration between robots and workers in many areas of the organization. In this sense, it is essential for companies to consider how humans can enhance machine efficiency and how machines can improve human actions, as well as redesigning business processes to facilitate collaboration between them (Wilson & Daugherty, 2018 ). However, from the worker’s perspective, one of the main fears is the perceived threat of being replaced by a computer (Stettes et al., 2017 ). As a result of technical advances, unskilled workers will have to adapt and engage in tasks involving social and creative cognitive skills (Rainnie & Dean, 2020 ). Therefore, training and knowledge associated with the advantages and disadvantages that technologies bring to the organization should be considered. Consequently, understanding the role of training in new technologies and its influence on employee awareness is key. It is necessary for organizations to be able to adapt to the changing environment in an agile manner. This agility will become a competence (Stein & López, 2014 ). Every agile organization must share the same idea, being purpose and vision, which gives meaning to change and promotes it, as this will be the nexus on which the innovation necessary to cope with market demand will be based; therefore, the following hypothesis has been proposed:

H3: The symbiosis between employee and process automation provides better customer service.

Methodology

Methodology and structure.

As indicated in the literature consulted, the methodology used in empirical research can be qualitative, quantitative, or a mixture of both to obtain data that will provide us with the necessary information to carry out the desired analysis. The paper proposes a qualitative methodology since it collects non-numerical data, which can provide us with more varied information. The survey, in studies with a quantitative or qualitative scope, is the most frequently used of all other techniques, including in the virtual online and offline environment, always supported by a properly structured and automated questionnaire to ensure the efficient and transparent handling of a large volume of data in almost real time. Among the traditional and virtual environments, there are obvious advantages, disadvantages, and limitations in the application of techniques and tools in data collection, but due to advances in artificial intelligence and technological advances, old stereotypes have been broken in the virtual environment, ensuring the quantity and quality of data and significantly decreasing the errors that could occur (Cisneros et al., 2022 ).

The survey has been based on a single attempt and has a single empirical cycle (research question, data collection, analysis, and report) and seeks to study the diversity of a topic within a given population by means of qualitative survey Fig. 3 .

Research model. Own elaboration

Elaboration and Collection of Survey Data : The survey is elaborated and designed to collect the information necessary to carry out the study.

Construct Allocation : Verification that the variables studied reflect or measure the theoretical construct for which they were designed.

Initial Analysis : Understanding of the different categories in the audience and their responses using a cross-tabulation format.

Threat Perception Analysis : The analysis will be carried out considering the perception of threat with respect to a series of variables.

Data Collection and Sample Characteristics

The questionnaire used was designed based on an electronic form that was distributed among a target group of more than 300 people, which, from the perspective of the objectives of this research, was considered very representative, since in the environment of the University of Alcalá, it includes not only education but also aspects related to administration. At the end of the questionnaire period, 103 samples were collected after eliminating incomplete and invalid samples.

The data collected have been processed and analysed using SPSS statistical software, highly regarded for quantitative research (Yockey, 2018 ) in the field of social sciences, mainly due to the large amount of information available. SPSS can be highlighted for its usefulness, adequate handling, and easy comprehension, having inside it a great variety of statistical topics oriented mostly to social sciences, covering all the needs of statistical calculation of researchers and professionals in the field to which it is applied (Pacheco et al., 2020 ). Therefore, the study has been of the descriptive type, using a quantitative approach and cross-sectional design. A questionnaire was sent to working people from different industries and sectors.

Following the proposed theoretical framework and the literature review, the research model was developed to reflect the relationship between training and the impact on automation, the perception of automation on work efficiency, how automation allows workers to act more creatively, and finally how task automation can improve the organization's service.

Analysis and Results

Data and construct allocation.

The model has been carried out considering the structure of a part of the evaluation questionnaire (Table  1 ) taking into account the variables EDU, EXP, and AGE, which focus on training, on the attitude towards automation and finally on the implementation and use of automation. From this point on, the three constructs were considered.

The first one “Self-learning” seeks to know the training that the interviewees have with respect to new technologies and the consideration that the interviewees have of the automation of processes. The second one “attitude” seeks to know the opinion of the interviewees with respect to their attitude towards process automation. Finally, the third one “Implementation and use” seek to know the perception of the interviewees about the actual implementation and use of these new technologies.

Initial Analysis

The initial analysis included the variables of form, experience, and age. The variable EDU is taken to check how the interviewee feels about whether he/she can consider him/herself threatened depending on his/her training. The EXPERIENCE variable is taken to check the relationship between the respondent’s years of experience and the sense of threat he/she may feel. The AGE variable will allow us to check the relationship between how threatened the respondent feels depending on the age of the interviewee.

The tables show how respondents answered the questions, considering themselves threatened or not by automation.

In this table (Table  2 ), the variables Q1_EDU (educational level of the participants) and the variable Q_THREAT (respondents who feel that their job may or may not be threatened by automation). The table indicates that out of 103 people participating in the survey, 60 do not consider their job to be threatened by RPA, while 43 feel that it is threatened.

The table shows a varied distribution of educational levels among the respondents, and most of them do not have the perception that their job is threatened by automation. However, it can be observed that respondents with lower educational levels have a higher sense of threat from the automation of their work.

In Table  3 , the variables Q2_EXP (years of work experience of the respondents) compared to the variable Q_THREAT (respondents who feel that their job may or may not be threatened by automation).

The Table  3 shows a varied distribution in the work experience of the respondents, and as mentioned earlier, most of them do not consider their job to be in danger. However, it can be observed that respondents with higher levels of experience have a greater sense that their jobs may be threatened by automation.

Finally, Table  4 shows the variable Q3_AGE (describing the age of the participants) and the variable Q_THREAT (respondents who feel that their job may or may not be threatened by automation). The table shows that respondents aged between 45 and 59 are the most represented in the sample, and there is a significant number of respondents who do not consider their job threatened by automation. These results suggest that the perception of automation threat may be influenced by the age of the respondents.

Perceived Threat Analysis

An ANOVA analysis is performed because two groups of different sizes are to be compare. In this case, ANOVA will allow us to determine the validity of our hypothesis by comparing the means of the different groups and assessing whether the observed differences are statistically significant.

The ANOVA analysis (Table  5 ) reveals significant differences in the perception of the respondents regarding the attitude towards RPA-assisted work and the implementation and use of RPA in the workplace. There are responses in which there is a high probability that the differences observed between groups are statistically significant, which implies that they differ significantly in relation to the variable analysed ( p  < 0.05).

In summary, the results indicate that there is a certain polarization in the perception of the respondents regarding the adoption and use of RPA in the workplace, suggesting that it is important for organizations to understand and address the concerns and opinions of their employees in this regard.

Welch’s t -test is an adaptation of Student’s t -test and is more reliable when the two samples have unequal variances and different sample sizes as in our case. By means of this test, it will check the consistency Table 6 .

Finally, in order to determine the magnitude and the trend of the groups, the descriptive statistics provided the results from the table (Table  7 ) which indicate that the surveyed individuals have a positive perception regarding the implementation and use of robotics in the workplace. Most of the surveyed individuals perceive that the implementation of such tools can aid in daily work as robots can work more effectively and efficiently. However, there is a neutral perception regarding the efficiency of robotics in customer service and the threat of task automation within an organization.

Statistical tests (Table 6 ) indicate that there are significant differences in respondents’ answers to some questions related to robotic process automation. In particular, the results indicate that there is no perceived direct impact on automation due to the respondents’ education.

The study that has been conducted provides a series of conclusions that come to demonstrate that the hypotheses that have been considered are valid. The fear of losing their job to a robot and being replaced by a robot is perceived more by people with an intermediate level of education than by people with higher education. In this case, hypothesis H1 is affirmed ( H1: Employee training is a driving force that impacts on work automation).

In this case, there is no correlation, so it is true that regardless of education, automation occurs in people with both higher education and primary education.

The adoption of this technology can improve efficiency and productivity, and it will allow us to validate our hypothesis H2a which indicates that the use of robots makes the worker more efficient and leads to fewer errors. The hypothesis “H2a: Automation is perceived to be more effective than the worker” can be considered valid.

Question Q8 (“Robot-assisted workers will be more productive”) and question Q9 (“Robot-assisted workers will make fewer mistakes”) have a significant difference in respondents’ answers, suggesting that most respondents who do not perceive automation as a threat agree that robots increase productivity and reduce errors at work.

The automation will allow the employee’s performance of more complex tasks and requiring more creativity, being able to validate our hypothesis ( H2b: Workers’ creativity increases in digitized workplaces when they are properly trained and coexist with automation systems ) which states that people who do not consider it a threat free them for other, more creative tasks.

Question Q13 (“Robots replace employees in routine activities, leaving creative and competent activities and exception management to them”) also shows a significant difference in respondents’ answers, suggesting that most respondents who do not consider automation a threat agree that robots can help employees focus on more creative and less routine tasks.

Automation can procure a number of benefits to the organization including enhanced customer experience, and it is considered how humans can improve the efficiency of machines and how machines can improve human actions so our hypothesis ( H3: The symbiosis between employee and process automation provides better customer service ).

Questions Q16 (“I want to know more about how robots could help me”) and Q19 (“Do you think that by using RPA you can be more efficient in serving customers with the same (human) resources”) have a significant difference compared to questions Q18 (“Investing in automation tools can make everyday life easier”) and Q20 (“Robots can work faster than employees”), suggesting that there is an interest in learning more about automation and consider that it could be beneficial for their work.

The result of this research highlights several factors crucial to the successful implementation of robotic process automation (RPA) in the organization, supported by the survey data. These factors focus on addressing employee perceptions and concerns, as well as recognizing the potential benefits of automation. The following can be highlighted:

Customized Educational Approach: Training programmes will be designed to suit employees with intermediate educational levels, as the survey indicates that this group perceives more fear of losing their jobs due to automation.

Efficiency and Error Reduction: Focuses on improving efficiency and reducing errors when implementing RPA.

Encouraging Creativity and Complex Tasks: Explain that automation will enable employees to perform more complex and creative tasks. It is essential to convey that RPA not only does not replace but also enhances work capabilities by complementing each other.

Improved Customer Experience: Automation will be integrated with a focus on improving the customer experience.

Discussion and Conclusions

The result of the research provides some statements that are carried out with process automation. In this case, it can be affirmed that workers are in favour of automation because they are aware that it helps their daily work and that it allows them to be much more efficient than without automation. It can also be seen that the threat they perceive is the same for people with higher education and those with basic education.

The authors conceive automation as something positive for the organization, even though there are several variables that depend not only on technology but also on people: (1) Automation will be limited to a prior study of the process in which a large process must be divided into sub-processes to be carried out. (2) Even though the result may seem positive, it is necessary to consider the limitations on the economic side to not only face automation but also the necessary training for its employees. (3) Automation must be well communicated to employees to avoid rejection by them.

Automation has transformed the way it works by providing new opportunities within the organization and improving the effectiveness and efficiency of certain processes. The use of process automation in companies generates a differentiated perception among employees, with some being in favour of its use in the workplace and others not. The results indicate that the interviewees have moderate levels of training and technological knowledge, suggesting a need for further education in this field and more information about the technology used by the organization they work for, to improve acceptance and implementation of automation.

Conclusions

The research model formulated outlines a framework for clarifying the complex interaction between training initiatives and their consequent impact on automation within organizational contexts. It highlights the importance of identity in understanding how employees respond to the introduction of AI and what outcome it produces. The model explores in detail the dynamics that encompass the perceived influence of automation on work efficiency, focusing on the ways in which automation facilitates creativity among employees. In addition, it considers how task automation, as a key component, contributes to improved organizational service delivery. AI in the workplace will continue to transform the nature of work and the skills required to perform it are increasingly important for both, staff and organizations to address the gap between their current skills and those that will be needed to successfully address these changes. Identifying and understanding this skills gap is the first step in developing effective strategies to improve and reskill the workforce. Once this skills gap is identified, organizations can develop strategies to upgrade and retrain their workforce to fill this gap and ensure that they have the necessary skills to use AI effectively.

By adopting this approach, the research model provides us with an analytical basis for understanding the relationships between training, automation, worker perceptions, creativity, and organizational service improvement.

Specifically, it should be considered that while the results suggest a positive attitude towards automation, concerns still exist. While employees’ fear of job loss due to AI is often due to exaggeration of AI capabilities (Willcocks, 2020 ), this perceived fear alters workplaces and changes employees’ behaviour, such as knowledge sharing versus hiding (Pereira & Mohiya, 2021 ).The implementation of automation requires an investment in training and knowledge of new technologies, as well as clear and effective communication about its implementation, to avoid issues with employees (Zirar et al., 2023 ). The study performed by Manis and Madhavaram ( 2023 ) identified in this research three key factors that contribute to the threat of AI identity in the workplace: job changes, loss of status position, and the perception of artificial intelligence as a potential threat. This should be considered by decision-makers within the organization. It is therefore crucial for organizations to understand and address the concerns and opinions of employees in this regard.

Attention should also be given to the associated costs and the concerns that this will generate among employees who view automation as a threat. Finally, it is expected that the implementation of automation can improve productivity and efficiency within the organization, provided that a smooth transition is carried out and accepted by the employees involved in this change.

Theoretical Implications

This research focuses on the positive side of process automation, as well as on the adverse effects on the participants. The research attempts to find the link between the hypotheses raised by answering each one of them. Only a small part has been covered, and much remains to be explored about how process automation affects many other areas of the company.

The study that has been conducted provides a series of conclusions that come to demonstrate that the hypotheses that have been considered are valid. Automation has increased due to the growing use of information technologies, bursting into the labour market. Even when jobs are composed of several tasks, some of these can be replaced by robots, but it is required to think about how the person can complement this automation (Autor, 2015 ), in line to what Stettes et al. ( 2017 ) indicates regarding the fear of losing their job to a robot. The fear of being replaced by a robot is perceived more by people with an intermediate level of education than by people with higher education. Porter and Kramer ( 2011 ) indicate that the adoption of this technology can improve efficiency and productivity, which indicates that the use of robots makes the worker more efficient and leads to fewer errors. In turn, Siderska ( 2020 ) indicates that automation will allow the employee’s performance of more complex tasks and requiring more creativity which states that people who do not consider it a threat free them for other, more creative tasks. According to a study by Schlegel and Kraus ( 2023 ), companies are looking for RPA professionals and expect them to not only have the ability to use certain tools but also specific skills such as business process management or human resources (Madakam et al., 2019 ). According to Madakam et al. ( 2019 ), the cognitive skills of workers as well as their specific skills are equally important.

Eloundou et al. ( 2023 ) and Siderska ( 2021 ) consider that automation can provide several benefits to the organization among others improved customer experience and Wilson and Daugherty ( 2018 ) consider how humans can improve the efficiency of machines and how machines can improve human actions so a large majority of respondents believe it improves customer service.

Digitalization has ensured that automation has taken hold in companies seeking to differentiate themselves from the rest of the competitors. So much so that employee training and adaptation to change is necessary to bring this change to fruition. The result of this change will result in organizations having trained employees capable of meeting new technological challenges, as well as the ability for faster implementation of any new automation-based technology. This will result in greater efficiency and effectiveness of the organization’s employees.

On a theoretical level, it is necessary to continue studying RPA and discover the possible challenges that it will provide to the organization both externally and internally, considering the impact that it can have not only on the technical side but also on human resources.

Practical Implications

The practical result of this study is to see how automation affects the work environment from three areas such as training, attitude towards process automation, and finally on the implementation of this in the workplace.

This automation process takes the organization to a new stage in which (1) it knows the fears that its employees may have regarding automation; (2) it seeks to know the degree of training/aptitude of the employees in automation issues and from here it can make decisions about whether further training is necessary; and (3) it seeks the implementation taking into account the employees to carry out this process.

Although the proposed hypotheses have been validated, in this study, process automation involves very extensive processes. The samples have been taken from persons in active employment, so the results could be biased given the low level of automation that currently exists in many organizations.

It is important to consider the following: (1) The environmental impact of automation as it is known that robots can improve energy efficiency and reduce waste in production processes, but the production and maintenance of robots can have a negative environmental impact. (2) As technology continues to advance it is important that researchers continue to investigate the impact that automation has on all areas of society and a call is made for research in this area with the aim of fostering the area of thoughtful research and facilitating a culture of innovation and digital business collaboration. (3) This will enable companies and organizations to implement effective strategies and policies for the adoption of robotic technologies and ensure a transition for the workers affected by it.

Finally, the study indicates that (1) the authors also warn that even though it can provide many benefits, it is also necessary to see the other side, which are the problems when carrying out automation. A previous study of the process to be automated must be carried out, not every process can be automated. (2) Even when the study seems positive, in real conditions, it might not be so positive due to the different economic and social factors that this entails. The lack of confidence of the management in the project can lead to failure. (3) The help of two people will be necessary, one who knows the process and the programmer who will carry out the automation of this one to avoid possible failures, either of concept or of automation.

In resume, the successful implementation of RPA relies on a combination of customized educational strategies, focus on efficiency and creativity, and the integration of automation to improve the customer experience. These factors not only address employee concerns, but also maximize the potential benefits of RPA in terms of productivity and service quality.

Research Limitations

Although the proposed hypotheses have been shown to be valid, this is only a small part of the research that can be conducted. Process automation addresses many areas of the organization, and this study discusses training, attitude, and workplace implementation and use. The study has only used a small portion of variables that may not reflect the entire reality of the process automation universe or perhaps in terms of the adequacy of the number of variables could have been increased at the cost of making a more accurate questionnaire, but also more complex for the participant.

This research focuses on the positive aspects of process automation and how it can influence the attitude of the people involved in the implementation and use within the workplace, as well as their knowledge of it. It is possible that many companies, even though they are aware of the concept of automation, do not consider its implementation due to lack of resources, either economic or because they do not have employees trained in this area, but even so, organizations will need to invest in upskilling workers to create a more adaptable and skilled workforce that can cope with the new challenges and opportunities of the future.

Lines to Follow

Considering the analyses carried out and the results obtained, it should be considered that research on the processes and tasks susceptible to automation are key in the strategies. The RPA is a growing trend in the business environment and that day by day is transforming the company and its vision of Human Resources. The use of new technologies is part of our daily lives, either as part of an organization or as a society. Although there are many studies, it is believed that there is still much to be explored, so several lines of research are necessary to continue advancing in this field. Thus, it becomes relevant to consider how technologies can impact the health and well-being of robot-assisted workers and how to mitigate potential negative effects.

Other future research line is to analyse the impact that process automation will have on society and the economy within the company. In turn, this will make it possible to consider the gender gap and the inclusion of technologies as facilitating and integrating tools in the face of labour inequalities. It is well known that automation can improve efficiency and productivity, but on the other hand, it will lead to job losses. Research will need to be conducted on how companies will be able to balance automation with the need to retain workers and ensure a just transition for affected workers. Another possible study will be how workers can be upgraded and trained to adapt to these technological changes.

In addition, analysis and evaluation of the benefits and costs of implementing and using RPA in different industries and organizations and its relationship to productivity, efficiency, and profitability should be considered.

Data Availability

Data is available upon request.

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Gómez Gandía, J.A., Gavrila Gavrila, S., de Lucas Ancillo, A. et al. RPA as a Challenge Beyond Technology: Self-Learning and Attitude Needed for Successful RPA Implementation in the Workplace. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-01865-5

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Ai-assisted literature reviews.

ChatGPT has a reputation for generating hallucinations, or false information. So can an Artificial Intelligence (AI) platform be trusted to assist in a literature review? Yes, if the tool you are using is the right one for the job. ChatGPT and Copilot are not designed to provide accurate citations. Instead, use them to brainstorm research questions. Keep alert for misinformation, hallucinations, and bias that could be part of the generative AI’s responses. Be aware of historical biases in the literature, which can also influence the output you encounter. 

Be sure to keep track of what tools you use, your purpose for using them, and the output from your interactions. Be prepared to disclose the AI tools, databases, and criteria used to select and analyze sources. Remember you are the one ultimately responsible for anything you create, generative AI is only your assistant.  

Try these five AI platforms to assist you in your literature reviews and academic research: 

• Copilot . Many people are exploring the ways that AI can be used to improve research. Even with a general generative AI platform like Copilot, you can use AI to help you brainstorm or discover new perspectives on research topics. An example prompt for this purpose can be found in David Maslach's article, "Generative AI Can Supercharge Your Academic Research," “I am thinking about [insert topic], but this is not a very novel idea. Can you help me find innovative papers and research from the last 10 years that has discussed [insert topic]?”  
• Elicit . This AI research assistant helps in evidence synthesis and text extraction. Users can enter a research question, and the AI identifies top papers in the field, even without perfect keyword matching. Elicit only includes academic papers, since Elicit is designed around finding and analyzing academic papers specifically. Elicit pulls from over 126 million papers through Semantic Scholar. Elicit organizes papers into an easy-to-use table and provides features for brainstorming research questions. 
• Consensus . This is an AI-powered search engine that pulls answers from research papers. Consensus is  not meant to be used to ask questions about basic facts such as, “How many people live in Europe?” or “When is the next leap year?” as there would likely not be research dedicated to investigating these subjects. Consensus is more effective with research questions on topics that have likely been studied by researchers. Yes/No questions will generate a “Consensus” from papers on the topic. Papers in Consensus also are from Semantic Scholar. Results in a Consensus search can be filtered by sample size of the study, population studied, study types, and more. This makes Consensus an interesting tool for finding related literature on your search topic. 
• Research Rabbit . An AI research assistant designed to assist researchers in literature research, discovering and organizing academic papers efficiently. It offers features such as interactive visualizations, collaborative exploration, and personalized recommendations. Users can create collections of papers, visualize networks of papers and co-authorships, and explore research questions. Unlike the previous two platforms listed, Research Rabbit doesn’t start with a question, but a paper that already is known. You need to have a starting article to go down a “rabbit hole” to see connections between papers. 
• Litmaps . A similar tool to Research Rabbit, a Litmap shows the relationships between the articles in your collection in the form of connecting lines which trace the citations for you. It allows a user to start with a citation, or a seed, and then through a simple interface, investigate connections between papers. 

For further reading, see " How to Write AI-Powered Literature Reviews: Balancing Speed, Depth, and Breadth in Academic Research " which includes a helpful table comparing the different tools that specialize in literature searching. And check out the February 2024 webinar, " Unlock the Power of AI for Academic Research " hosted by Tracy Mendolia-Moore and Brett Christie for more information on this topic.