Elevating talents' experience through innovative artificial intelligence-mediated knowledge sharing: Evidence from an IT-multinational enterprise

https://doi.org/10.1016/j.intman.2021.100871Get rights and content

Abstract

Managing talent and growth in sizeable global information technology (IT) multinational enterprises (MNE) facing technological disruption requires a well-developed innovation strategy. This study presents novel insights into how a large MNE shared knowledge through artificial intelligence (AI) mediated social exchange using effective global talent management (GTM) strategies. Analyzing in-depth qualitative interview data from an extensive global technology MNE subsidiary, this research draws upon the literature on the knowledge-based view (KBV), AI-mediated social exchange theory and GTM, and explores how, through an AI-mediated knowledge-sharing exchange, the MNE managed its knowledge needs. Findings suggest AI-enabled talent applications improved individual experiences of talents at this MNE pursuing an innovation strategy. Findings from the data analysis suggest that first, an innovation-led strategy and culture created a social context for sharing of talent-specific knowledge through knowledge-based data systems embedded in talent-focused AI applications. Second, talent-focused knowledge sharing using AI-mediated social exchange applications resulted in talents experiencing varying personalization levels and positive experience in terms of increased job satisfaction and commitment and reduced turnover intentions. Implications for MNEs in emerging markets to manage global talents in an AI embedded digital social exchange for effective individual outcomes.

Introduction

Multinational enterprises (MNEs) operating in the global technology services sector in emerging markets can be viewed as a complex constellation of resources, confronted with the challenge of coordinating and managing knowledge to remain competitive, provide innovative solutions and retain, not just their clientele but also their talent (Farndale et al., 2014, Farndale et al., 2021; Kogut and Zander, 1992; Liu, 2019). In dealing with the paradox of growth, replication of knowledge across MNE subsidiaries can expose the MNE to imitation threats, but as Kogut and Zander (1992) suggest, firms that specialize by innovating and learning new ways to recombine existing knowledge and capabilities can overcome this paradox. Doing so would require cooperation and collaboration between individuals, thereby suggesting the need for a robust social relations system and a social exchange (Blau, 1964) between employees and key stakeholders. The idea that an MNE can effectively deliver the transfer and recombination of different forms of knowledge has been noted as a critical aspect of an evolutionary theory of MNEs (Kogut and Zander, 1993). Further, Kogut and Zander (1993) argue that firms are repositories of knowledge holding and use multiple transfer mechanisms. For example, the channel through which technology is transferred to a range of an MNE's stakeholders is driven by the advantage and growth a firm seeks to enjoy as it expands across borders. The more complex and hard-to-imitate ways in which a firm's existing knowledge bases are organized or recombined, the greater the likelihood that an MNE will gain advantage from such an organization and integrate knowledge. Kogut and Zander's (1993) notion of combinative capabilities regarding how a firm combines elements of tacit and explicit knowledge bases is what provides it with the opportunity to gain advantage and growth. Therefore, a key focus is for the MNEs to transfer knowledge and new technologies both within and across an MNE's network of subsidiaries through a robust social exchange mechanism and other organizing principles. As Grant (1996) noted in his conceptualization of the KBV of a firm, knowledge integration to a firm's productive function is a dynamic capability.

For achieving improved business performance, Minbaeva and Collings (2013) suggest that GTM needs to be linked to the global strategic intent and strategic business process of an MNE. A strategic business process refers to an approach in which the competitive potential of an organization's resources and capabilities are realized (Ray, Barney and Muhanna, 2004) by improving the innovativeness of individuals in delivering unique client solutions, which are hard to copy by the competitors. Thus, GTM needs to develop a diverse global talent pool and provide them opportunities to generate new knowledge and recombine innovation capabilities (Mellahi and Collings, 2010) that would operationalize the corporate innovation strategy in talents' daily routine work. In this endeavour, the adoption of AI applications (Abraham et al., 2019; Ellmer and Reichel, 2021; Greasley and Thomas, 2020; Kumar et al., 2020) can help connect several people management issues GTM practices with global talents to manage them effectively in the direction of the strategy across the network of the subsidiaries of MNEs. This requires a corporate culture that embraces and encourages ‘creativity and innovation’ among the young workforce (Brant and Castro, 2019) and work creatively (Morris et al., 2016). Given the importance of GTM to promote innovation within the corporate culture, we note the applicability of an adaptable culture. An IT-based MNE facilitates change and flexibility gained via AI-enabled GTM to assist the MNE to remain productive and responsive based on the global environment in which it operates.

Managing talent through GTM practices has been noted as a key source of sustained competitive advantage for MNEs (Mellahi and Collings, 2010), but firms must also develop relevant managerial and technological intra- and inter-organizational capabilities (Kothari et al., 2013; Del Giudice and Maggioni, 2014; Sarala et al., 2016). Further, MNEs face challenges of coordination and integration of GTM practices for leveraging their internal talent across their network of subsidiaries. One innovative technological solution is to use AI-based knowledge sharing applications through appropriate technological platforms. Knowledge-based systems are considered one of the most successful applications of AI (Akerkar, 2019). Research examining how MNEs in emerging markets create innovative technological knowledge-based systems to share key inter- and intra-organizational knowledge (Cabrera and Cabrera, 2002) across an MNE's network (Hansen, 2002) using innovative artificial intelligence (AI) enabled applications is relatively scarce. AI systems can employ a range of AI-based approaches to deliver expert problem-solving and decision-making capabilities through expertise- and knowledge-based domains. For example, AI-aided decision-making and problem-solving can occur through data mining, wherein by discovering knowledge within databases through pattern recognition, clustering or other algorithms, specific knowledge sharing can occur through an AI-mediated social exchange (Ma and Brown, 2020).

Further, developments in machine learning allow efficient knowledge sharing for faster decision-making, problem-solving and numeric predictions (Akerkar, 2019). Specifically, innovative technological products developed using human cognition for better interaction (Lu, 2019; Ma, 2019; McColl and Michelotti, 2019; Silic et al., 2020) may lead to better social exchange between individuals, technology and the organization. Such an approach offers a novel and effective way to share diverse sets of knowledge concerning a talent's needs across the geographical and skill boundaries of an MNE.

Based on the above assertions, the need for a supportive social exchange mechanism (Blau, 1964) to allow for the recombination of knowledge and capabilities for MNEs to gain an advantage is vital. The adoption of AI applications and GTM practices opens up an AI-mediated social exchange (Ma and Brown, 2020) and allows MNEs to replicate and recombine their diverse knowledge through advanced AI technologies to prevent imitation ensuring integration of knowledge. Therefore, we argue that by sharing knowledge through an AI-mediated exchange and using GTM as a higher-order organizing system, MNEs can seek employees' cooperation, ability, and motivation. Further, by creating a trusting environment, employees' opportunity to share knowledge through AI applications will support an MNE's growth. Furthermore, through such an exchange, a range of employee outcomes can likely be significantly enhanced. Through these effective mechanisms, MNEs can continue to share and replicate critical knowledge within and across their subsidiaries.

Given the importance of talent for sustained competitive advantage and innovation in MNEs, several gaps remain regarding the role of technology for specific aspects of talent management (TM) (O'Shea and Puente, 2017). For example, there are gaps in managing the talent's experience, satisfaction, retention, and commitment through an AI-mediated GTM practice, especially as MNEs seek to gain a competitive advantage through people (Malik et al., 2020). Therefore, this research investigates how the use of an innovative set of AI applications and sharing diverse sets of knowledge through an AI-mediated social exchange can impact talent experience at this MNE. We build our argument on Ma's (2019) AI-mediated exchange theory (AI-MET), an extension of the social exchange theory (Blau, 1964), which suggests that through a technology-mediated social exchange, sharing knowledge of the GTM occurs through AI-applications. The AI-MET focuses on the human-AI interactions through diverse AI applications and aims to influence talent's experience of GTM practices across the MNE's network and affect talent's micro-level outcomes through a social exchange, an agency of autonomous systems (Ma and Brown, 2020). The application of such technology-mediated knowledge-sharing social exchange, though critical, is not yet researched in the context of GTM and may be regarded as a revolution of digital disruption in leveraging talent through AI-based applications. We contribute to the Special Issue's call on managing internal and external organizational knowledge for building sustained competitive advantage through GTM from an emerging market context. Specifically, this research bridges the above-identified gap by addressing the following research questions. 1) How do a firm's innovation strategy and innovation culture affect AI-mediated knowledge sharing of GTM practices? 2) How do talents experience technology-mediated knowledge sharing of AI-enabled GTM practices; 3) How does the AI-enabled GTM practices influence employee attitudes and behaviours through AI-mediated social exchange?

In seeking to address the above research questions, our study contributes to the literature on GTM and knowledge sharing in MNEs in three ways. First, it focuses on examining how an innovation-led strategy and culture leads to GTM practices embedded within innovative AI applications in the MNE's operations. Second, it presents new findings of how, through an AI-mediated knowledge-sharing social exchange, the talents experience personalization, hyper-personalization and individualization of GTM practices. Third, this research highlights how an AI-mediated knowledge-sharing social exchange articulated the positive experience of talent concerning GTM in increased job satisfaction, commitment and reduced turnover intentions. The rest of the paper is organized as follows. We begin by reviewing the literature on GTM and innovation, followed by knowledge sharing in MNEs, GTM and an AI-mediated knowledge-sharing exchange. This is followed by the impact of such an exchange on employee outcomes. The next section explains our conceptualization and methods. The findings are followed by a discussion, conclusion where we state the implications of our findings to be beneficial for MNEs in emerging markets to manage global talent in an AI-embedded knowledge-sharing social exchange for an efficient and effective individual and firm performance.

Section snippets

Knowledge sharing in MNEs, global talent management and AI applications

Following the KBV of a firm, the role of GTM practices and organizational processes are critical in delivering strategic business outcomes (Kogut and Zander, 1992) as knowledge is ‘socially constructed’ and is viewed as residing in ‘organizing of human resources’, which requires individual relationships to be managed by a set of organizing principles (Kogut and Zander, 1992, Kogut and Zander, 1993). As such, MNEs' need to coordinate and manage essential knowledge between the headquarter and

Research design

Keeping in mind a relatively novel phenomenon of the role of AI-enabled applications for GTM, a qualitative case study design of a large and unusually representative and revelatory case is expected to yield rich and nuanced contextually rich data (Eisenhardt, 1989) and is in line with theory-building efforts for developing new models and theoretical contributions (Corley and Gioia, 2011; Malik et al., 2019; Thomas et al., 2011; Whetten, 1989). A unique and single case study design has several

Role of innovation strategy in elevating GTM

This sub-theme that emerged from the data analysis reinforces the fact that AITECH centred around its core business strategy of innovation that inspires each member of the organization. The core innovation strategy emerged as a significant source of elevating GTM in the organization. The influence geneses from the talent acquisition itself as a senior HR official explained:

In terms of innovation, we do hiring for people who can survive an innovation ecosystem. We look at people who most

Discussion and conclusion

In the context of an emerging market, our study's findings unveil how MNEs share knowledge for managing their global talent using innovative technology-based solutions while maintaining their competitiveness and aligning strategically and culturally GTM practices to support innovation. More specifically, the study's analysis unfolds the importance of understanding how GTM systems can be enriched through an innovation strategy and innovation culture that enables AI-based application and AI-MET

Implications for theory and practice

Our study has several implications for both theory and practice concerning knowledge sharing in MNEs operating in emerging markets and focusing on GTM and its practical use via an AI-mediated exchange for elevating employee attitudinal and behavioural outcomes. A key theoretical implication lies in showing the effective use of the GTM system, which can be enabled via AI-based applications. Second, such AI-applications can yield positive employee attitudinal and behavioural outcomes through

Limitations and future research

Our study has some limitations offering avenues for future research. First, we adopted a single case study based on an emerging market. However, it was unusually representative and revelatory to investigate novel context-specific data. Comparative studies can be suggested to conduct between emerging markets to gain valuable insights into AI-enabled GTM (Raman et al., 2013), in particular organizational and individual outcomes. Second, we could not address macro-environmental factors such as the

Grant funding

The authors would acknowledge funding support from the International Research Collaboration Grant 2019-2020, University of Newcastle, Australia.

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