HRM 4.0 For Human-Centered Organizations

Rita Bissola, Barbara Imperatori, Rita Bissola, Barbara Imperatori

• Publisher: Emerald Publishing Limited
• Year: 2019
• Print ISBN: 9781789735369
• eBook ISBN: 9781789735376
• Topic: Business
• Subtopic: Human Resource Management
• Index: Business

This volume of Advanced Series in Management offers cutting-edge research from Human Resource Management professionals and academics, which engages with the potential opportunities and challenges of digitization in the workplace. 

Contributors introduce fresh evidence and innovative ideas on the changing work environment, to help business leaders' shift to the digital mind-set. The book suggests new ways of managing, organizing, and leading a positive social change towards a human-centered organization. For example, a collaboration between HRM and IT, adoption of design thinking, using integrated analytics, and developing new competences. 

The book explores this new world for HRM, developing critical insights about three main issues:

• HRM's new role in the 4.0 era
• New job and organization design for the smart economy
• New HRM tools and practices for digital organizations. 

HRM 4.0 For Human-Centered Organizations provides both researchers and professionals working in Human Resources Management, Organization Design and Organizational Behaviour with practical guidelines to turn the challenging scenario of Industry 4.0 into a successful transformation for the HRM domain.

Smart Industry or Smart Bubbles? A Critical Analysis of Its Perceived Value

Milou Habraken and Tanya Bondarouk

Abstract

Despite the fact that labels such as “smart industry” and “industry 4.0” (terms used to denote the fourth industrial revolution) have become popular topics within academia and in practice, their meaning remains an issue of concern. It’s a concern that has drawn the attention of various authors. It is a struggle we engaged in as well – specifically regarding the Dutch “smart industry” label – to aid our aim of assessing whether our call to combine forces can be extended beyond industry 4.0 and industrie 4.0. We provide here initial indications of whether there is more unity in meaning and, thus, reasons to take steps toward combining labels. By means of 20 interviews with Dutch smart industry experts, a representation of smart industry was obtained as understood in the Netherlands. Based on this representation, we examined the extent of overlap between the Dutch “smart industry” label and the general term “fourth industrial revolution” as well as the “industry 4.0” label as defined by various scholars. Our findings showed that smart industry in the Netherlands does not match the denotation of an industrial revolution. Several signals were, however, detected indicating that the content observed under the Dutch smart industry label overlaps with what is being presented under the label industry 4.0. These results reveal that there is indeed more unity in meaning between the various labels that exist and, as such, strengthens our call to combine forces.

Keywords: Fourth industrial revolution; smart industry; industry 4.0; meaning; value; combining forces

The industrial world has never before known this freedom (p. 53)–

New technologies appear; long-established businesses fall on hard times; the economic order is threatened; and society itself experiences drastic challenges to values and standards of behaviour (p. 54) –

There are many unknowns (p. 64)

Although the above quotes from Finkelstein and Newman (1984) address the third industrial revolution, they are just as relevant in the current situation since, once again, we seem to be facing economic upheaval. In other words, following the first three periods of turmoil, it is now being claimed that we find ourselves in a fourth industrial revolution. This revolution triggered the resurfacing of the employment debate again (see, e.g., Habraken & Bondarouk, 2017). But it is unique in that it has been announced a priori (Drath & Horch, 2014), and unlike the prior revolutions, there are many different labels used to denote this one. While the third was also known as the computer revolution, examples of labels currently used are industrie 4.0, industry 4.0, smart industry, integrated industry, advanced manufacturing, or industrial internet of things (Davies, 2015; Hermann, Pentek, & Otto, 2016). The presence of such a diverse set of labels makes it challenging to keep an overview of what has been published, leads to duplicates in the list of key words (e.g., Kang et al., 2016), and risks academic progress by implicitly forcing rediscovery of the wheel. The last point is the most important one since it creates a fragmented field of research. It is understandable if the variety in terms is accompanied by significantly different meanings; if not, this fragmentation is unnecessary and counterproductive for academia. The logic behind the previous sentence highlights an underlying problem of the matter we aim to address. That is, we raise the issue of whether the diversity in labels serves an essential purpose. But the field also struggles with the absence of a clear understanding of these labels, a concern that has recently been addressed by various authors (e.g., Hermann et al., 2016; Reischauer, 2018). The publications by Hermann et al. (2016) and Reischauer (2018) also stress the point we want to emphasize (i.e., does the diversity serve a purpose?). While they each focus on a different label, industrie 4.0 versus industry 4.0, it can be concluded from the content of their papers that they consider the other term to be equal to theirs. So why then adopt both, especially in English, and hence international, publications? We would argue – let’s combine forces and stop the use of fancy but superfluous words.

The aim of this study is to assess whether the call to combine forces can be extended beyond the labels industry 4.0 and industrie 4.0. We do so by focusing on the smart industry label. In other words, the value of smart industry is assessed by examining the level of overlap with the interchangeable label industry/industrie 4.0. This approach was chosen since their descriptions have already been addressed by scholars. A definition of smart industry is still required, however. To establish one, we conducted an interview-based study with smart industry experts from the Netherlands. We therefore do not claim to offer the definition of smart industry. But we provide initial indications of whether there is more unity in meaning and, thus, reasons to take steps toward combining labels. As a result, our research firstly contributes new insights to the present lack of a clear understanding for labels of the fourth industrial revolution. Second, we offer an initial reflection on the necessity of the multitude of terms and resulting fragmentation.

The remainder of this chapter is structured as follows: first, we briefly illustrate the manner in which smart industry is depicted in reports from the Dutch smart industry team and the confusion that occurs here. Next, the research process is outlined, after which we present the results from interviews conducted with smart industry experts. On the basis of these findings, a viewpoint of smart industry is developed. Using this perspective, we finally turn to our question of what is the value of smart industry.

Strict Technological Determinism?

The first official mention of smart industry in the Netherlands can be found in the Dutch report from April 2014 (Huizinga et al., 2014). The team behind this report consists of five important parties: the Ministry of Economic Affairs, the Chamber of Commerce (KvK), the Dutch employers’ organization for the technology industry (FME), the Netherlands organization for applied scientific research (TNO), and the confederation of Netherlands industry and employers (VNO-NCW). In this report, smart industry is defined as a strategic vision of the future industry. Such industries are stated to have flexibility in production, being able to (fine)tune to customers’ needs, and make use of the entire supply chain for value creation. Subsequently, these outcomes are said to be enabled by a network-centric approach, utilizing the value of information, information and communication technology (ICT), and the latest available proven manufacturing techniques. A recap of this description can be found later in the report when it mentions that “smart industry – driven by information, digitization, networks, and manufacturing technologies – will improve quality, increase flexibility, increase automation, enhance participation within the value chain and enhance interaction with customers” (Huizinga et al., 2014, p. 25). The above highlights that smart industry is seen as a future view of industry stemming from technology. It reflects a cause-and-effect chain in which the origin of the change is viewed from a technological standpoint. In other words, these descriptions as well as descriptions that can be found in other documents adopt a strictly deterministic (Orlikowski, 1992), or technologically imperative, perspective on smart industry (Strohmeier, 2009). The report from 2018, for example, states that:

smart industry is about future-proof industrial & product systems; these are smart and interconnected and make use of Cyber Physical Systems. Digitization, connectivity and new manufacturing & product technology are drivers for this. (Ahsmann et al., 2018, p. 9)

Though they are scarce, smart industry documents also include descriptions that point toward a less strict, deterministic approach:

the previous sections mainly dealt with technologies, but this is too limited. Experience shows that the implementation of technologies for the purpose of benefiting from its opportunities takes special expertise and an innovative attitude (Huizinga et al., 2014, p. 2)

and

smart industry is about more than technological developments, ICT and different business models. It is the employee who will have to make a difference and it is important that the employee has the right skills and knowledge. (DutchSmartIndustryTeam, 2015, p. 2)

They add a moderating effect, specifically the contextual variable “skilled workforce,” to the causal chain stated earlier. In doing so, a more moderate deterministic or contingency model is adopted (Orlikowski, 1992; Strohmeier, 2009).

In summary, the first official definition of smart industry and even a more recent one from 2018 formulate the label in quite a strictly deterministic manner. However, several notions can be found that depict a different story, and hence nuances are visible that can impact the value of the label. A clearer picture was therefore developed, via interviews, of smart industry as understood in the Netherlands.

Method

Participants and Procedure

Along with the program office and the steering committee, the Dutch smart industry team consists of a forum group whose members represent a diverse set of sectors and are tasked with creating support, stimulating, connecting, exchanging knowledge, realizing togetherness, and making bottlenecks negotiable and solvable (Berentsen et al., 2014). Given this role and the diversity of the members of the smart industry forum, we approached them¹, via email, with the question of whether they would like to discuss the meaning of smart industry (see Appendix for details on respondents). The interviews were held between December 2016 and February 2017. After 15 interviews, data saturation started to occur. To achieve full saturation, an additional five interviews were conducted to prevent essential aspects of smart industry from being overlooked. Consequently, we conducted 20 interviews in total. Of these participants, 15 were members, or appointed alternatives, of the smart industry forum group. Five participants were non-forum members but had been recommended as knowledgeable and actively involved in smart industry. In line with the goal of the study, we held the interviews as open conversations and asked respondents how they viewed, defined, and interpreted smart industry and/or which aspects they associated with it. Interviews centered on this one single question, which was approached without the use of any preset topics in order not to influence the outcomes. Participants were encouraged to explain things more and provide examples if they did not do so themselves. Interviews lasted an average of 47 minutes and were digitally recorded where possible; this was the case for 17 out of the 20 interviews. We transcribed the recorded interviews verbatim (resulting in 106,315 words of transcripts) and emailed them to the participants with an invitation to “review it and send any comments.” Participants were asked to return any feedback or corrections within two weeks. All edits received were taken into account in the data analysis.

Data Analysis

Using Atlas.ti, we first open-coded all transcripts. Chunks of text received codes based on the content that was being discussed in that segment (e.g., background on prior industrial revolutions) or terms that were explicitly stated in that part (e.g., 3D printing, zero defect, big data). In subsequent rounds we only considered pieces of text that contained codes that were of relevance to the research goal of this chapter. Consequently, segments that contained codes addressing, for instance, the background on the three earlier industrial revolutions or insights into the Dutch smart industry team were omitted. The next rounds of analysis were used to develop the remaining codes. This implied that we rephrased code names to fit their content better and bundled codes with similar meanings under a new code (e.g., codes such as internet, IT, digitalization were combined to form the code “digitized”). We also created four headings to group several related codes. In doing so, the distinct direction of each code was maintained, compared to having bundled them under a new code. These headings contained codes associated with the expected changes in output of organizations (i.e., products) or the production phase (i.e., production process) and contained organizational departments (i.e., other processes) or types of interactions (i.e., relations) expected to be subject to change. Eventually, 31 codes remained, which we checked and found that they matched the notes taken during the three non-recorded inter...

Table of contents

• Cover
• Title Page
• 1. Smart Industry or Smart Bubbles? A Critical Analysis of Its Perceived Value
• 2. An Analysis of the Contribution of e-HRM to Sustaining Business Performance
• 3. HR Devolution in the Digital Era: What Should We Expect?
• 4. Lost in Holacracy? The Possible Role of e-HRM in Dealing with the Deconstruction of Hierarchy
• 5. Use of Relational eHRM Tools in Gig Worker Platforms
• 6. The Human–Technology Interface in Talent Management and the Implications for HRM
• 7. Blockchains 2019 in e-HRM: Hit or Hype?
• 8. Building Synergies with HR Analytics Capabilities
• 9. HR Analytics in the Digital Workplace: Exploring the Relationship between Attitudes and Tracked Work Behaviors
• 10. Themes and Trends in Smart Working Research: A Systematic Analysis of Academic Contributions
• 11. Social Media in HRM: A Humanistic Management Perspective
• 12. Exploration of Social Media Capabilities for Recruitment in SMEs: A Multiple Case Study
• Index