AI-augmented HRM: Antecedents,assimilation and multilevel consequences

The current literature on the use of disruptive innovative technologies, such as artificial intelligence (AI) for human resource management (HRM) function, lacks a theoretical basis for understanding. Further, the adoption and implementation of AI-augmented HRM, which holds promise for delivering several operational, relational and transformational benefits, is at best patchy and incomplete. Integrating the technology, organisation and people (TOP) framework with core elements of the theory of innovation assimilation and its impact on a range of AI-Augmented HRM outcomes, or what we refer to as (HRM^(AI)), this paper develops a coherent and integrated theoretical framework of HRM^(AI) assimilation. Such a framework is timely as several post-adoption challenges, such as the dark side of processual factors in innovation assimilation and system-level factors, which, if unattended, can lead to the opacity of AI applications, thereby affecting the success of any HRM^(AI). Our model proposes several testable future research propositions for advancing scholarship in this area. We conclude with implications for theory and practice.     

How strategic considerations influence decision making on e-HRM applications

Although numerous studies indicate that the added value of human resource management is strongest when HRM decisions are linked to the organizational strategy, practical knowledge about how strategic considerations influence decision-making processes relating to e-HRM is limited. Therefore, the purpose of this study is (1) to examine in three case studies how strategic considerations influence the decision-making process around the introduction of e-HRM applications, (2) to present propositions for further research, and (3) to offer recommendations on how to better include strategic considerations in the decision-making process. Three in-depth case studies of companies' e-HRM implementation were performed using a model on e-HRM strategy formulation (Marler, 2009). The case studies reveal that when specific business drivers are absent from the decision-making process, the main role of e-HRM becomes to provide an infrastructure with a focus on preventing dissatisfaction. In order for e-HRM to be used in a more strategic way, business and HRM should be aligned.Based on the findings, we offer research propositions for academics studying this emerging field of the interconnections between strategy, human resource management and IT systems. In addition, we offer recommendations for HR practitioners on how to optimize the match between business and HRM.     

Unlocking the value of artificial intelligence in human resource management through AI capability framework

Artificial Intelligence (AI) is increasingly adopted within Human Resource management (HRM) due to its potential to create value for consumers, employees, and organisations. However, recent studies have found that organisations are yet to experience the anticipated benefits from AI adoption, despite investing time, effort, and resources. The existing studies in HRM have examined the applications of AI, anticipated benefits, and its impact on human workforce and organisations. The aim of this paper is to systematically review the multi-disciplinary literature stemming from International Business, Information Management, Operations Management, General Management and HRM to provide a comprehensive and objective understanding of the organisational resources required to develop AI capability in HRM. Our findings show that organisations need to look beyond technical resources, and put their emphasis on developing non-technical ones such as human skills and competencies, leadership, team co-ordination, organisational culture and innovation mindset, governance strategy, and AI-employee integration strategies, to benefit from AI adoption. Based on these findings, we contribute five research propositions to advance AI scholarship in HRM. Theoretically, we identify the organisational resources necessary to achieve business benefits by proposing the AI capability framework, integrating resource-based view and knowledge-based view theories. From a practitioner’s standpoint, our framework offers a systematic way for the managers to objectively self-assess organisational readiness and develop strategies to adopt and implement AI-enabled practices and processes in HRM.     

The duality of algorithmic management: Toward a research agenda on HRM algorithms,autonomy and value creation

This study proposes the ‘duality of algorithmic management’ as a conceptual lens to unravel the complex relationship between human resource management (HRM) algorithms, job autonomy and the value to workers who are subject to algorithmic management. Against tendencies to present algorithmic management as having predetermined, undesired consequences (e.g. restriction of job autonomy, poor financial compensation and deteriorating working conditions), our ‘duality of algorithmic management’ perspective offers two amendments to the dominant thinking on HRM algorithms and their outcomes to workers. First, we showcase how algorithmic management simultaneously restrains and enables autonomy and value to workers – with the latter referring to both use (i.e. non-monetary benefits) and exchange value (i.e. monetary benefits) that workers derive from working (under algorithmic management). In doing so, we make the case that the desired consequences of HRM algorithms to workers co-exist alongside the undesired consequences that the literature has mostly reported on. Second, we argue that algorithmic management is shaped by, as much as it shaping, the autonomy and value to workers. We do so by highlighting the ‘recursivity’ of algorithmic management that occurs when software designers and/or self-learning algorithms reinforce or limit worker acts for (re)gaining job autonomy and/or creating value out of HRM algorithms. We conclude this paper with the presentation of avenues for future research into the duality of algorithmic management, which sets the stage for a future line of inquiry into the complex interrelationships among HRM algorithms, job autonomy and value.     

Coping with an emerging market competition through strategy-human resource alignment: case study evidence from five leading Japanese manufacturers in the People's Republic of China

This paper documents how PRC-based Japanese affiliates can align their human resource management (HRM) policies and practices with their business strategies based on in-depth interview surveys of the five leading Japanese manufacturers in China. In particular, using a multiple-informant research design to interview both top and human resource managers in each site, the study has attempted to clarify the strategic fit between the business strategies pursued and HRM practices adopted in China. Our interviews with top management for each affiliate revealed three types of business strategies that the PRC-based affiliates adopt to gain competitiveness in the Chinese market. Separate interviews with HR managers further clarified that affiliates' HRM efforts are aligned with the strategy each affiliate is pursuing. The findings are used to suggest several hypothesized relationships between the particular strategies pursued and the HRM practices adopted in China. Furthermore, this study has provided some important insights as to how the choice of affiliate-level business strategies in China affects the ‘hybridization’ processes of the HRM policies and practices used in the cross-national business environment.     

Introducing a multi-stakeholder perspective on opacity,transparency and strategies to reduce opacity in algorithm-based human resource management

Artificial Intelligence and algorithmic technologies support or even automate a large variety of human resource management (HRM) activities. This affects a range of stakeholders with different, partially conflicting perspectives on the opacity and transparency of algorithm-based HRM. In this paper, we explain why opacity is a key characteristic of algorithm-based HRM, describe reasons for opaque algorithm-based HRM, and highlight the implications of opacity from the perspective of the main stakeholders involved (users, affected people, deployers, developers, and regulators). We also review strategies to reduce opacity and promote transparency of algorithm-based HRM (technical solutions, education and training, regulation and guidelines), and emphasize that opacity and transparency in algorithm-based HRM can simultaneously have beneficial and detrimental consequences that warrant taking a multi-stakeholder view when considering these consequences. We conclude with a research agenda highlighting stakeholders' interests regarding opacity, strategies to reduce opacity, and consequences of opacity and transparency in algorithm-based HRM.     

基于伦理视野下企业并购中的人力资源整合框架

并购后人力资源的整合是企业并购成功不可缺少的重要环节。由于存在诸多影响并购成功的HRM因素,以及人力资源管理者在遵守多元化的伦理原则的同时需要承担一些特定的角色,使得在并购前后、在人力资源管理中出现伦理困境。因此,可以从价值、文化两个层面来分解HRM整合的实现框架。HRM价值整合需要综合考虑HRM伦理动因、冲突、风险。HRM文化整合则需要在企业文化冲突的背景下,从员工选拔、培训、绩效、薪酬等方面进行文化整合,并在HRM战略下建立统一的社会责任文化。两个层面的整合都需要进行HRM伦理实践。     

Customer service-focused HRM systems and firm performance: evidence from the service industry in Taiwan

Abstract

This study adopts a resource-based view, attraction–selection–attrition theory and a focused approach to examine the link between service-focused human resource management (HRM) systems and firm performance. Internally, we examine employee competency as the ‘black box’ between service-focused HRM systems and firm performance. Externally, we examine the effect of the interplay of a service-focused strategy and service-focused HRM systems in predicting firm performance. We collect data from different sources (i.e. executives, human resource members, and line managers). Using a final sample included 2120 respondents from 175 firms in the service industry in Taiwan, the results show that customer service-focused employee competency transmitted 53% of the effect of customer service-focused HRM systems on firm performance. In addition, when a firm implements a less service-focused business strategy, service-focused HRM systems leads to significant improvement in firm performance. Implications for research are discussed.     

Artificial intelligence (AI)-assisted HRM: Towards an extended strategic framework

Artificial intelligence (AI) affects human resource management (HRM), and in so doing, it is transforming the nature of work, workers and workplaces. While AI-assisted HRM is increasingly considered a strategy for improving organizational productivity, the academic literature has not yet offered a strategic framework to guide HR managers in adopting and implementing it. However, existing research in this area offers an opportunity to build such a framework. This systematic review of 67 peer-reviewed articles helps to achieve this objective. We critically examine the organizational and employee-centric outcomes of AI-assisted HRM and develop a strategic framework to guide its practice and future research.     

A problematic transition to a strategic role: human resource management in industrial enterprises in China

The Chinese government has launched extensive reforms to encourage integration with the global economy. Our research investigates the implications for human resource management practices of the changing business environment in China, ownership of organizations, organizational strategies and strategic integration of the HR function. We conducted two surveys in major Chinese cities in 1994/5 and 2001/2, with managers of state-owned, privately owned, collectively owned and foreign-invested enterprises.

Regression analyses showed that organizational strategy and organizational ownership, in contrast with earlier research, were not found to be strong predictors of HRM practices. The changing business environment in China and participation by the HR function in strategic decision-making were the strongest predictors of HRM practices. Overall, a strategic role for the HR function and implementation of ‘Western’ HRM practices are becoming more prevalent in China, although the legacy of traditional practices endures and new challenges are emerging.     

Employee experience –the missing link for engaging employees: Insights from an MNE's AI-based HR ecosystem

Analyzing multiple data sources from a global information technology (IT) consulting multinational enterprise (MNE), this research unpacks the configuration of a digitalized HR ecosystem of artificial intelligence(AI)-assisted human resource management (HRM) applications and HR platforms. This study develops a novel theoretical framework mapping the nature and purpose of a digitalized AI-assisted HR ecosystem for delivering exceptional employee experience (EX), an antecedent to employee engagement (EE). Employing the theoretical lenses of EX, EE, AI-mediated social exchange, and engagement platforms, this study's overarching aim of this article is to establish how AI-assisted HRM fits into an organization's ecosystem and, second, how it impacts EX and EE. Our findings show that AI-assisted applications for HRM enhance EX and, thus, EE. We also see increases in employee productivity and HR function's effectiveness. Implications for research and practice are also discussed.     

The role of human resource management in cross-border mergers and acquisitions

Cross-border mergers and acquisitions (M&As) have become the dominant mode of growth for firms seeking competitive advantage in an increasingly complex and global business economy. Although human resource management (HRM) can play a value-adding role in the merger process, existing research and evidence does not clearly demonstrate how it can do so. This paper addresses the neglected human side of M&As by providing a strategic fit framework to assess the link between M&A strategy and HRM strategy. Because cross-border M&As are of an order of magnitude more complex than domestic mergers, we examine contingencies in national contexts that influence outcomes in the merger process. We draw on recent empirical evidence to highlight HRM roles in terms of resources, processes and values that reflect the influence of both strategic fit and national context in the integration stage of cross-border M&A.     

An interdisciplinary review of AI and HRM: Challenges and future directions

Artificial intelligence (AI) has the potential to change the future of human resource management (HRM). Scholars from different disciplines have contributed to the field of AI in HRM but with rather insufficient cross-fertilization, thus leading to a fragmented body of knowledge. In response, we conducted a systematic, interdisciplinary review of 184 articles to provide a comprehensive overview. We grouped prior research into four categories based on discipline: management and economics, computer science, engineering and operations, and others. The findings reveal that studies in different disciplines had different research foci and utilized different methods. While studies in the technical disciplines tended to focus on the development of AI for specific HRM functions, studies from the other disciplines tended to focus on the consequences of AI on HRM, jobs, and labor markets. Most studies in all categories were relatively weak in theoretical development. We therefore offer recommendations for interdisciplinary collaborations, propose a unified definition of AI, and provide implications for research and practice.     

The relationship between human resource management practices,business strategy and firm performance: evidence from steel industry in Taiwan

The primary objective of this study is to investigate the relationship between human resource management (HRM) practices, business strategy and firm performance. We examined the following HRM practices: training and development; teamwork; compensation/incentives; HR planning; performance appraisal; and employment security. We surveyed 236 managers working at steel firms in Taiwan to explore their perceptions on the impact of HRM practices and business strategy on firm performance. The results of this study are summarized as follows: (1) HRM practices will be positively related to firm performance; (2) there is a close linkage between HRM practices and business strategy; (3) business strategies will be positively related to firm performance; (4) integrating HRM practices with business strategies will be positively related to firm performance.     

Chinese multinationals’ approach to international human resource management: a longitudinal study

Abstract

This study explores how emerging market service firms, operating in developed markets, approach human resource management (HRM). Data analyzed in this article were drawn from a longitudinal case study of the Australian subsidiary of a Chinese multinational bank. We find that subsidiary HRM follows host country and global best practices. However, the way that this hybrid HRM system was implemented shows traces of Chinese origin. A key finding from this study is that although our case bank officially adopted a polycentric approach to subsidiary staffing, employing host country nationals, the subsidiary predominantly employed locals with a Chinese ethnic background. We also find the case bank’s strategy in international HRM has evolved from a focus on localization to global standardization. This global standardization, however, is shaped in line with global best practices rather than home country management model. These findings highlight the need for future studies to adopt a more nuanced approach to examining international HRM strategies, especially when analyzing host country effect or localization strategy.     

HRM at a Crossroads: Comments on “Evolution of Strategic HRM Through Two Founding Books: A 30th Anniversary Perspective on Development of the Field”

I agree with Bruce Kaufman's evaluation of the HRM field and the danger to its relevance if change does not take place in the field's almost exclusive use of normal science, lack of focus on organizational change and development and indifference to the development of useful and usable knowledge. I use the development of the Harvard Business School's first‐ever required course in HRM and Managing Human Assets, the book that grew from the course, to illustrate why and how the strategic HRM perspective of the book emerged. I go on to argue that we do not need more proof that HRM practices are associated with financial performance. The best CEOs already believe this, though they need knowledge and practices that will enable them to develop a positive sustainable HRM system. HRM research, both normal and action science, must answer the question of how a “great” HRM system—practices, leadership, organization design, culture, and change processes—might be developed over time (its takes years) and how great companies can avoid the liquidation of intangible assets in the face of pressures for short‐term performance. I describe my own work to illustrate the future direction HRM should take methodologically and conceptually. © 2015 Wiley Periodicals, Inc.     

HRM as a pluralistic forum: assumptions and prospects for developing a distinctive research capability

In this paper, the dilemmas facing HRM on how to establish itself as a distinct area and yet avoid a particular disciplinary allegiance and level of analysis are assessed. HRM is seen, like business strategy, as a meeting ground between established disciplines.

HRM research to date is then assessed in this light and problems of the potential limitation ofcontributions of other relevant disciplines are assessed. The established findings in organizational development (OD) have, it is argued, been particularly neglected in HRM analyses of organization change. The importance of developing pluralistic HRM research is re-affirmed.

HRM as a pluralistic forum     

A critical review of algorithms in HRM: Definition,theory, and practice

The recent surge of interest concerning data analytics in both business and academia has been accompanied by significant advances in the commercialization of HRM (Human Resource Management)-related algorithmic applications. Our review of the literature uncovered 22 high quality academic papers and 122 practitioner-oriented items (e.g., popular press and trade journals). As part of our review, we draw several distinctions between the typical use of HRM algorithms and more traditional statistical applications. We find that while HRM algorithmic applications tend not to be especially theory-driven, the “black box” label often invoked by critics of these efforts is not entirely appropriate. Instead, HRM-related algorithms are best characterized as heuristics. In considering the implications of our findings, we note that there is already evidence of a research-practitioner divide; relative to scholarly efforts, practitioner interest in HRM algorithms has grown exponentially in recent years.     

Best friend or broken tool? Exploring the co-existence of humans and artificial intelligence in the workplace ecosystem

Artificial intelligence (AI) has become an important topic in business literature and strategy talk. Yet, much of this literature is normative and conceptual in nature. How organizational members perceive AI and the job role changes that come with it is, so far, largely unknown territory for both HR scholars and practitioners. We sought to investigate the relationship between humans and AI and conducted an in-depth exploratory study into the co-existence of humans and two early-stage AI-solutions, one for “low-status” automation and another for “high-status”; augmentation. We suggest that different organizational groups may engage in distinctly different sensemaking processes regarding AI, an important insight for successful HRM strategies when AI is being introduced into the workplace. Moreover, contrary to recent conceptual work, our findings indicate that AI-enabled automation and augmentation solutions may not be detached from nor exist in tension with each other. They are deeply embedded in organizational processes and workflows for which people who co-exist with the technologies must take ownership. Our findings, in part, go against discussions on AI “taking over” jobs or deskilling humans. We describe a more nuanced version of reality fluctuating around the various ways different organizational groups encounter different AI-solutions in their daily work. Finally, our study warns against unconditional technological enthusiasm, managerial ignorance of the nature of work that employees undertake in different organizational groups, and a neglect of the time and effort required to successfully implement AI-solutions that affect not only the home organization but also members of the broader ecosystem.