Abstract
As society evolves, moving forward into the realms of Industry 5.0, it is imperative to develop a robust collaborative talent management model that can endure external crises such as the recent Covid-19 pandemic. Despite the antiquity of collaborative practices—traceable back to early human societies—both practitioners and academics have frequently focused primarily on exploring the catalysts for collaboration, often neglecting to provide a holistic conceptualization of effective human collaboration. In alignment with the Sustainable Development Goals, this paper endeavors to critically review and delineate the evolution of talent management and collaborative intelligence literature. Employing a systematic review methodology, this study aims to formulate a novel framework for collaborative talent management, drawing upon the principles of a collaborative intelligence mindset, and to suggest avenues for future research in this domain. The findings propose the employment of collaborative intelligence through a mindset perspective as a means to bridge the literature gaps identified in areas such as interdisciplinary and cross-sector collaboration, digitalization and artificial intelligence, collaborative leadership styles, and performance measurement within the field.
Plain language summary
Purpose: The study sought to devise an updated approach to talent management suitable for a rapidly evolving world, particularly in light of challenges posed by events like the Covid-19 pandemic. Methods: A systematic review of existing literature was conducted to trace the evolution of human collaboration and the management of talent. Conclusions: The review identified noticeable gaps in current understanding, especially pertaining to interdisciplinary collaboration, the integration of digital tools and AI, collaborative leadership approaches, and performance metrics. It is posited that embracing “collaborative intelligence,” a mindset focused on effective teamwork, can address these identified gaps. Implications: Adopting the strategies proposed in this study might equip businesses and organizations to navigate future challenges more adeptly, remain contemporary in the era of AI, and foster an environment characterized by enhanced collaboration. Limitations: The study’s conclusions are inherently contingent upon the quality and breadth of the existing literature reviewed. Some areas or relevant studies might not have been explored, and empirical validation of the proposed strategies remains essential for ascertaining their effectiveness.
Introduction
Saniuk et al. (2022) highlighted the sustainability concerns of potential future dehumanization due to the activities of Industry 4.0. To achieve social and economic sustainability in the development of Industry 4.0, the article identified three areas of focus—human-centric, resilience, and sustainability. Citing Lee et al. (2014) and Palazzeschi et al. (2018), the same article elaborated that the information society during Industry 4.0, referred to as Society 4.0, insufficiently shared knowledge and information, thereby limiting the adoption of unified values, potential outcomes and collaboration among members of cross-sector teams. Scholars also predicted that Society 5.0 would utilize artificial intelligence and new technologies to connect people through knowledge and information sharing, leading to the formation of new business chains and social norms (Nakanishi, 2019; Shiroishi et al., 2018, 2019). Konno and Schillaci (2021) added that the success of Society 5.0 revolves around practices and the application of intellectual capital through the lenses of knowledge creation and sharing.
The collaboration of humanity in society is not new, as it can be traced back to the caveman era. The term “collaborative intelligence” was initially coined as “collective intelligence” by Malone (2004) in his book “The Future of Work.” The two terms appear to alternate throughout the body of the literature. Malone emphasized the importance of innovating great ideas, which are unachievable if pursued alone. He also pointed out that when a group of talented individuals cooperates, they generate intelligence that would never have existed at an individual level. The author also predicts that experts in artificial intelligence form another collaborative group that will contribute to a crucial body of essential knowledge in the upcoming years. D’Almeida and Ingle (2020) highlighted the challenges and opportunities in artificial intelligence, identifying issues such as lack of human intelligence in education, engagement of cross-sector collaborative research and development, and the necessity of making a consistent policy and advocacy available.
In contrast to the general concept of collective intelligence, Markova and McArthur (2015) systematically conceptualized the term “collaborative intelligence quotient” (CIQ) as a skillset that enables cross-functional collaboration. They coined the four components of the intelligence quotient as mind patterns, thinking talents, inquiry, and mind share, emphasizing the importance of establishing collaboration as a mindset of “mind share” rather than “market share.” In other words, thinking “what I have, and what you do not have” is now replaced with “the more we share, the more we gain.” The authors further highlighted that diversity presents a substantial challenge in collaborative initiatives. Moreover, a vital competency for fostering innovation through collaborative intelligence involves cultivating the ability to embrace differences among individuals and others. In the context of workplace talent management and work transformation, Yalenios and d’Armagnac (2023) found that when HR managers attempt to try the collaborative exploration phase or simply just have an “on-demand” conversation regarding collaboration with cross-functional managers, the process could facilitate the development of situational solutions that suit the team, although the process is time-consuming.
Companies have navigated the most volatile, uncertain, complex, and ambiguous times amid the recent global Covid-19 pandemic. George (2017) referred to this global economic and business instability VUCA 2.0. However, such widespread disease is not the sole external factor that causing dynamic changes in the business environment. Whysall et al. (2019) observed that the rapid shifts in the business environment have generated ambiguity for strategic human resource management, suggesting that managers must explore innovative talent management strategies. Additionally, in P. Sparrow’s (2019) work, the fragmented nature of the knowledge encompassing talent management is also highlighted. Scholars debated whether the field of talent management is still premature (Collings et al., 2011; Thunnissen et al., 2013), and there were instances when researchers grappled with the precise definition of talent management (Collings & Mellahi, 2009; Garavan et al., 2012; Iles et al., 2010; Lewis & Heckman, 2006; Tarique & Schuler, 2010). Both the assumptions and philosophies employed in the literature were significantly varied to the point of becoming vague (Collings & Mellahi, 2009; Dries, 2013; Gallardo-Gallardo et al., 2013; Meyers et al., 2013; P. R. Sparrow & Makram, 2015). P. Sparrow (2019) further criticized that less than 30% of the body of knowledge has theoretical framing, and what exists is superficial, fragmented, and lacks theoretical underpinnings. There is an urgent need to align talent management theory and practice toward more dynamic and highly collaborative talent management activities. As cited by Schweer et al. (2012), organizations like The Hackett Group found that well-established talent management increased earnings by approximately 18% and greatly enhanced organizational performance. They added that consulting firms such as IBM and McKinsey & Company are more likely to recommend collaborative and social networking tools to promote a well-connected organization, leading to a higher share of the market.
Collaborative talent management through collaborative intelligence is a new endeavor for senior managers and human resource practitioners, especially during the transition from Industry 4.0 to 5.0. While the academic literature in this field still lacks rigor, several segments of industrial practitioners are already discussing the potential fruitfulness of strategic talent management through the collaboration of multiple networks, particularly between human and artificial intelligence. Many practitioners highlight leveraging HR data analytics or collaborative intelligence to foster potential for collaboration within an organization (Boomer, 2021; Meier, 2022; Pols, 2019; Staples, 2019; Woolf, 2014). Some are engaged in social collaboration theory using talent visualization tools (Woolf, 2014), others formed a collaborative partnership between the HR department with learning and development experts (Staples, 2019), some explored talent mobility through network mapping (Meier, 2022) while others also delved into the initiatives that involve human and artificial intelligence joining forces (Wilson & Daugherty, 2018). In other words, the conversation about collaborative talent management somehow involves integrating some form of artificial intelligence or the utilization of technology experts. However, Pols (2019) highlighted that people analytics and talent intelligence departments are often merged. Consequently, the two functions and responsibilities are frequently blurred. While data analytics provides the foundation for understanding the talent pool network, talent intelligence experts support business decisions related to talent availability, competitor insights and location feasibility. The process also entails collaborative efforts that connect different stakeholders to achieve a competitive advantage. Despite rigorous scholarly attempts to elucidate collaboration, there remains a conspicuous lack of a definitive prescription. Practitioners often struggle with framing this collaborative paradigm within a coherent and grounded conceptual structure. This deficiency impedes the sustained cultivation of a workforce that produces tangible results.
The Collaborative Intelligence Mindset Theory advocates for sustainability through fostering a collaborative approach that addresses organizational challenges, streamlines decision-making, and spurs innovation. Organizations must extend their focus beyond mere technical resources, placing significant emphasis on cultivating non-technical elements including human skills and competencies, leadership acumen, team coordination, a culture of innovation, governance strategies, and strategies for integrating AI with employees (Chowdhury et al., 2023). Rooted in Markova and McArthur’s (2015) concept of collaborative intelligence, the theory emphasizes the importance of a diverse workforce which, when coupled with a collaborative mindset, optimizes the holistic contributions of an organization’s members. Such an approach not only enhances cognitive diversity but also strengthens the interconnected components of an organization, resulting in an adaptable, progressive, and standout workforce. This resilient and adaptable framework aligns with the United Nation’s Sustainable Development Goal (SDG) No. 17, supporting short-term efficiencies and providing a foundational base for other SDGs. Moreover, while serving as a cornerstone for talent management strategies, it paves the way for long-term sustainable growth and societal development. To accomplish the set objectives below, the review will commence with an exploration of literature related to collaborative intelligence. This will be succeeded by sections on methodology and a presentation of the review’s findings. Subsequent discussions will focus on the gaps discerned within the existing literature and the limitations inherent to this review, culminating in a comprehensive summary and synthesis of the insights gathered in the conclusion section.
To review the development of literature related to talent management and collaborative intelligence using a systematic review methodology.
To conceptualize the Collaborative Intelligence Mindset Theory
To critically identify gaps in the existing literature and acknowledge the limitations within the current review pertaining to collaborative talent management.
The Rise of Collaborative Intelligence
Introduction to Collective Intelligence
Collective intelligence is an earlier term similar to collaborative intelligence, occasionally known as “crowdsourcing,” as discussed in Pavlidou et al. (2020). Surowiecki (2005) defined collective intelligence as the accumulation of information within groups, leading to conclusions or judgments that are often superior to the ideas of individual group members. According to Yu et al. (2018), Mackay (1841) represented one of the earliest instances of literature discussing how each individual in a group offsets potential errors, typically resulting in nearly perfect final answers or decisions. Among the earliest empirical research endeavors, Galton (1907) collected 787 observations of a group estimating the weight of a cattle, demonstrating that group wisdom could outperform any individual experts. Several researchers further explored collective intelligence through the perspective of swarm intelligence in animals and honey bees, creating a metaphor for human team effectiveness mechanisms (O’Bryan et al., 2020; Seeley, 2022). While the concept of group intelligence indeed results in more effective solutions, Reia et al. (2019) raised questions about whether the collective group strategy merely accelerates problem-solving. There is no evidence to support the notion that such a strategy really has a positive impact on attaining the best solutions.
Söilen (2019) lamented that his literature review revealed how the collaborative ideas of collective intelligence, though highly popular in social science literature, may not necessarily align with a specific industry, as discussed in various segments of the body of literature. The concept was applied to numerous interdisciplinary fields, with some creatively integrated into multiple technical domains, thereby further muddling the precise definition of collective intelligence. For instance, certain literature investigated the formation and composition of effective teams to enhance collaborative performance within the interdisciplinary realms of computer science and organizational psychology (Andrejczuk et al., 2018). Several scholars view collective or collaborative intelligence as a compilation of knowledge and experts organized within digital online platforms, utilized as resources for predictions, decision-making, problem-solving and learning (Blewitt, 2010; Demoly et al., 2020; X. Huang, 2018; Radcliffe et al., 2019; Suran et al., 2021; Vermesan et al., 2020). Conversely, some researchers explored optimizing collaborative endeavors between humans and artificial intelligence (Mulgan, 2018; Weld et al., 2015; Williams, 2023; Wilson & Daugherty, 2018; Zheng & Liu, 2022), while others proposed methods for creating, collecting, and refining collective intelligence data through various forms of communicational and informational wired network (Ha et al., 2019; Maier et al., 2022; Nguyen et al., 2019).
One very recent and popular collaborative channel is online social media networks, such as Twitter, Instagram and Facebook. Rachunok et al. (2021) highlighted the use of these social media channels as a communication platform, particularly Twitter. These various social platforms allow individuals and organizations to reach broader audiences to gather data. Such collective data are handy, especially during natural disasters or times of crisis, and the continuous evaluation and enhancement of the data are invaluable for community resilience. One successful example of the accumulation of knowledge through data is an online collaborative project model widely known as Wikipedia (Kaplan & Mazurek, 2018; Lichtenstein & Parker, 2009). This ongoing project empowers different end-users to contribute to scientific, knowledge-related content that is accessible to the public anytime and free of charge. Using a similar concept to this highly successful collaborative project model, companies can improve the efficiency of sharing knowledge and information across the organization in a split second. This further enhances interaction between different stakeholders while triggering the generation of new, great ideas.
However, Olszowski et al. (2021) offered a better conceptualized and down-to-earth humanistic approach to collective intelligence in policymaking. Although policymaking activities were previously constrained to small groups of experts, participation in policymaking had begun to open its gates to a broader collective influence. Inputs from government decisions were an integral component, but transparency and engagement of a diverse background of citizens were encouraged to gather the collective intelligence needed to enhance decision-making. The authors also identified 15 types of research methods by analyzing past collective intelligence literature in policymaking. They found that most articles were computer or information science-related and political science-related.
As the collective brains in policymaking are highly affected by a diverse composition of experts in society, the process comes with challenges. Nevertheless, Schimmelpfennig et al. (2022) recommended embracing the paradox of diversity to achieve innovative outcomes by considering several mechanism functions—societal size and how it is interconnected, the consistency of information transmitted, and cultural trait diversity. Migliano and Vinicius (2022) traced the origin of society’s cumulative culture, utilizing the foraging niche perspective to explain the evolution of the complex human gene-culture since the Stone Age. It requires sophisticated cognitive mechanisms such as demographic conditions, recombination, cultural specialization, shared intentionality, teaching, innovation, and transmission fidelity to evolve into today’s collective intelligence.
Similarly, the literature related to collective intelligence involving research and higher education industries again showed ambiguity and vagueness. Many researchers have centered their discussion around utilizing digital intelligence systems, social media and collaborative networks that are related to collaborative learning and imparting knowledge (McCauley et al., 2019; Meza et al., 2018; Mora et al., 2020; Nehls & Livengood, 2018; Nuninger & Châtelet, 2018; M. A. Peters & Jandrić, 2018; Vătămănescu et al., 2018; W. T. Wang & Lin, 2021; H. Y. Wang & Sun, 2021), institutional collaborative innovation (Crain et al., 2022; Lombardi et al., 2019), data and intellectual capital management (Kaplan & Mazurek, 2018; Secundo et al., 2018), and monitoring the learning process (Sobnath et al., 2020; Ullmann et al., 2019).
Secundo et al. (2018) highlighted the benefits of intellectual capital management, a term that involves everything in the organization that can create value—including information, intellectual property, experience, knowledge and intellectual material. Using their framework, which succinctly presents four questions: “What,”“Who,”“Why,” and “How,” and sought to probe the collective intelligence in institutions. Although the framework somewhat ambiguously illustrates the collaborative potentials on campus to achieve what they termed “the third mission,” the article did provide several motivations for establishing external collaborative efforts. These included the development of innovative entrepreneurial human capital, the enhancement of the capacity for action, and the promotion of knowledge exchange through social engagement and regional development. Similarly, M. A. Peters and Jandrić (2018) introduced the concept of a creative university, envisioning it as a digital university based on the open innovation of a public knowledge ecosystem. This vision comprises a shared ethos of co-responsibility, co-design, co-creation, and co-production; new platforms that encourage peer production; and the emphasis of openness, interconnectivity and interactivity. Some researchers have identified a resilience deficit in much of the collaborative integration in higher education, contending that it fails to provide depth to the representation of collective intelligence. For instance, Agudelo González et al. (2022) examined the collaborative digital competencies of an undergraduate program. They found that the curriculum lacked breadth when addressing issues of copyright and collective intelligence, particularly in terms of internet securities and technology misuse.
The Emergence of Collaborative Intelligence
Another term, “collaborative intelligence,” emerged at the turn of the 21st century. Although the term seems to have alternated with the notion of collective intelligence over the past two decades, the new term appears to have evolved to explore how humans and the digital world join forces. Using the keywords “collaborative intelligence” to search the Scopus database, the attempt retrieved only 244 results. This relatively small number of results suggests that collaborative intelligence is a newer niche term compared to collective intelligence. Most of the results came from technical fields such as information systems, computer sciences, engineering and mathematics. Only 23 articles appeared when the business, management and accounting filter was applied to the search, but the fields of information technology and engineering were still prominent in the list even after such a filter was applied. The results were refined to 22 articles, as another article was excluded due to a broken link. However, the thematic results extracted from this search seem redundant with the term “collective intelligence.” For example, the collaborative intelligence articles generated are primarily technological-related, such as using the Internet of Things, social media, and online platforms to harness collaborative intelligence through connected networks, systems or innovation clusters (Adamides & Karacapilidis, 2020; Cachia et al., 2007; Eni, 2016; Gill, 2013; Milani & Santucci, 2009; Sang Ko & Nof, 2012; Vogt & Fink, 2011; Wanas et al., 2008; Zhong et al., 2013), fuzzy collaborative intelligence (Chen, 2011; Chen & Wang, 2018), cross-functional collaboration and knowledge sharing between multidisciplinary fields (Lindblom & Martins, 2022; Pinto et al., 2020), cybersecurity threats in collaborative networks (Liu et al., 2022; Solansky & Beck, 2021), and a substantial number of the articles propagate collaboration intelligence as cooperation between humans and artificial intelligence (Abraham et al., 2020; Chowdhury et al., 2022; M. H. Huang & Rust, 2022; Lin & Mattila, 2021; Paschen et al., 2020; Wilson & Daugherty, 2018; Zhong et al., 2015). Like collective intelligence, articles also view collaborative intelligence as a means of crowdsourcing and distributing work to a group of people (Chiu et al., 2014), collaborative leadership in the intelligence community (Kolmstetter, 2014), and as a strategic decision-making driver during the fourth industrial revolution (Lanteri, 2021).
The uniqueness of collaborative or collective intelligence is centered around the social notion of human relations and collaboration (Gittell, 2011). Since humanity’s earliest existence, people have collaborated and united to create sustainability and resilience to survive. Zhong et al. (2015) explained that collaboration involves the functionalities of both coordination and cooperation. Coordination involves communicating and exchanging information to achieve a mutual strategic advantage between stakeholders, whereas cooperation is the effort to share information among the people involved. On the other hand, Surowiecki (2005) offered a comprehensive meaning to the wisdom of crowds, emphasizing that collective wisdom is a market judgment or prediction of future events that only could be useful if the conditions of crowd diversity, independence and decentralization are fulfilled. In contrast to many of the past works of literature regarding collaborative and collective intelligence, Markova and McArthur (2015) coined the role of collaborative intelligence quotient as a critical component in a new collaborative mindset called “mind share,” replacing the outdated traditional “market share” perspective. The market share way of thinking—“I have it, and you do not,” is no longer relevant. The value of a product is not determined by scarcity anymore but by how innovative and disruptive it could be, and all only could be possible with collective resources, ideas and relationships. People are brought together inevitably to think together, solve problems, and create breakthrough innovations. Individuals are forced to work and brainstorm together among diverse personalities, time zones, cultures and across continents. The authors described that the collaborative intelligence quotient involves four expedited collaboration strategies, namely mind patterns, thinking talents, inquiry, and mind share.
Background to Talent Management
It is not unusual for researchers to investigate the historical background to appreciate the present and conceptualize the future. Talent management is a term that was coined in the late 1990s by a group of researchers and practitioners in McKinsey and Company. It was a research project that ended with the publication of a book called The War for Talent by Michaels et al. (2001), displaying the increasing need for top talents that had become competitive in the marketplace. The book started to look at the specification of talent management differently, seeing “talent” as more of a disposition of an individual, which the company has to embrace through highly strategic attraction, development and retention efforts. At the turn of the 21st century, the term talent management became managers’ top concern. It was not until Silzer and Dowell (2010) that the term talent management received its official entitlement in the field’s literature.
Pre-War for Talent
Although the historical development of talent management only received its emerging explication in recent years, P. Sparrow (2019) identified six enabling concepts that contributed to the development of The War for Talent. The enabling concepts could be traced back to the 1980s—1990s, which sees the amalgamation of earlier human resource activities that were previously separated into resourcing and career development initiatives. The first enabling concept sees Beer et al. (1984) discussing the lifecycle perspective, highlighting the importance of recruiting, developing, and retaining employees throughout the entire tenure in an organization. The second enabling concept emphasizes the competency theories (Boyatzis, 1982), arguing that the whole human resource recruitment and career development system could be based on abilities, skills and personal motivation that could indicate job fit. The third enabling concept emerged following the general discussion on the idea of employees as human assets. The concept relied on the performance-potential framework to identify the best performing employees (Odiorne, 1984), resembling the marketing concept of product-market categorization such as stars, dogs, cash cows and problem children.
The fourth enabling concept focused on human resource planning, mainly forecasting, planning, and staffing employees to meet business needs. These activities involve measuring employees’ effectiveness through data collection and augmenting succession planning of management roles by recognizing the right competencies (Fitz-enz, 2000; Gubmann, 1998). Boam and Sparrow (1992) called this a total resource development system. Further developing the concept of planning in human resources, Zuboff (1988) added the fifth enabling concept centered around the empowerment of talent, in which the value is highly influenced by the impact of information technology and innovation. These so-called “intellective skills” are recognized as hard-to-replace company assets that should be strategically aligned and developed. Smart (1999) also added that organizations should recruit and develop only the best people, and the author referred to these people as “topgrading.” The sixth and final argued that establishing job fit in human resource management is no more relevant. Lawler (1994) suggested the pay-for-the-person approach rather than the pay-for-the-job approach. This approach saw the shift of the human resource system that source and develop highly skilled people who possess self-efficacy to design their jobs fittingly. However, it is rare to find these human assets that display the mentioned new critical skill.
The six enabling concepts of talent management provided valuable insights and assumptions that later became the foundational theories for the term of “talent management,” as indicated in Silzer and Dowell (2010). P. Sparrow (2019) argued that these enabling concepts likely contributed to the success of Michaels et al.’s (2001) The War for Talent. The concepts were cleverly repackaged and reassembled to emphasize the need to adopt a talent mindset, craft a compelling employee value proposition, reinvent recruitment strategies, and incorporate development initiatives throughout the organization. One of the book’s most significant contributions was its emphasis on the importance of differentiation and affirmation of employees. It introduced the categorization of A-B-C players in talent management: investing heavily in “A” players (those who consistently deliver results and inspire others), nurturing and developing “B” players (those who meet expectations but show limited growth), and decisively addressing “C” players (those who underperform or set poor examples). The book heralded four new philosophies in the modern era of talent management. Firstly, it brought to the forefront the importance of employer branding, leveraging marketing theories to appeal to and retain the standout “stars.” Next, talent management was reframed not merely as intricate HR processes but also as activities that differentiate and affirm, making talent feel valued beyond their pay grade. Thirdly, talent management began to be viewed as an effort to nurture a leadership culture that acknowledges and caters to an elite talent pool’s needs. Finally, the book introduced the idea of fostering two types of healthy competition—an interpersonal focus on individual talent management and an intrapersonal perspective between individuals.
Post-War for Talent
Although the insights from The War for Talent significantly influenced the understanding of talent management in the twentieth-first century, Collings and Mellahi (2009) emphasized the importance of identifying and managing critical positions or roles within the organization. This focus helps ensure effective succession planning and prevents the over-investment in non-strategic roles. The strategic approach involves pinpointing these essential roles, developing current high-performing individuals to assume them, creating a differentiated human resource structure to facilitate these activities, and ensuring their commitment to the company (Boudreau, 2010; Boudreau & Jesuthasan, 2011; Boudreau & Ramstad, 2005; Cappelli, 2008; Huselid et al., 2005; Ingham, 2007). This collection of literature emphasizes the necessity of aligning succession planning with the company’s overarching business strategy. Translating the business strategy into specific talent requirements is pivotal. It’s essential to use appropriate frameworks to segment the desired skills, enhancing the “science” behind decisions about the mobility of the organization’s “star” talents.
Another research segment sees talent management as a risk management concept and waste elimination. This concept dwells on ways to clear uncertainties, avoid undesirable outcomes or wastage, and remedial measures to bad outcomes within human resource operations (Cascio & Boudreau, 2012; P. Sparrow et al., 2015). Centered around strategic activities such as risk optimization, management and mitigation frameworks, this concept raised concerns regarding illusions in predictions, governance of talent systems, and the functional ownership of these activities, which could involve others besides the human resource department. Similarly, there was also literature that explored the different dilemmas of ethical risks, highlighting the minimal requirements in treating people with respect, allowing a certain amount of individual freedom and eliminating toxic leadership (Greenwood, 2002; Painter-Morland et al., 2019; Swailes et al., 2014).
As the development of “star” talents progresses and becomes a norm in many companies, several researchers started to raise the shortcoming of companies in managing the social dimensions of their talent management initiatives (Cheese et al., 2008; Davies & Kourdi, 2010; Schiemann, 2009). These scholars were concerned about companies overlooking the combined efforts of the human capital, hence restricting their talent management efforts to limited employee engagement. Similarly, some critics lamented that HR managers still rely on intuitive instinct or are over-dependent on previous beliefs or experiences in talent management (Mäkelä et al., 2010; Makram et al., 2017; Vaiman et al., 2012). Although HR data are now readily available, organizations fail to fully synthesize data through usable metrics or analysis. As companies began to realize the importance of cultural values and equity in the workplace, this led to the debate on the notion of the elites versus egalitarian style in talent management (Dries, 2013; Farndale et al., 2010; Meyers & van Woerkom, 2014; Schiemann, 2014; Scullion & Collings, 2011; P. Sparrow et al., 2014; Stahl et al., 2012). Sheehan and Anderson (2015) particularly highlighted that talent management should consider choosing from inclusionary or exclusionary workplace diversity policies by looking through lenses of theoretical frameworks such as social identity, in-group or outgroup dynamics and structuration.
Boudreau (2010) once suggested retooling talent management by utilizing frameworks from diverse organizational systems, disciplines and constituents that share mental models that could play a valuable role in managing a diverse workforce. For example, some scholars see talent management activities as interrelated with the studies of supply chain management frameworks. In the supply-chain field, Makarius and Srinivasan (2017) proposed the notion of matching product supply with customers’ demand in the manufacturing field as an integrated approach to access the talent supply pool. In this supply-chain-adapted talent management strategy, companies are urged to enhance the network and access to talent suppliers and other appropriate sources to avoid a mismatch of the required talents in a particular job vacancy (Keller & Cappelli, 2014). In another scenario, Cascio (2002) used the concept of inventory analysis, which identifies levels of inventories, surpluses or shortages that optimize cost and risk. This context inspired the idea of downsizing, turnover and performing separation of functional and dysfunctional employees.
In another segment of contemporary research in talent management, Reis et al. (2021) explored literature from 2016, and they found a rising interest in creating innovative employer branding in talent management. According to Sandeepanie et al. (2023), the employees’ psychology contract is the mediating and moderating factor in the relationship between talent management and employer branding. This concept is especially true to attract generation Z, the next generation of fresh human capital entering the workforce in the coming years. Pandita (2022) noted that these fresh graduates had already formed their branded employer criterion since tertiary level education—they demand innovation, flexibility, individualism, and a craving for career development rather than monetary incentives. Nevertheless, Kurek (2021) has also shown that onboarding, talent management process or improvement of employee competencies through artificial intelligence has changed the process of traditional employer branding into digital employer branding.
In recent years, technology-enabled talent management strategy has gained popularity. Some scholars argued that, although the kick-start of Industry 4.0 had provided plenty of digital automation and cyberspace intelligence to society, many still reported that there were many challenges in knowledge sharing to encourage cross-functional collaboration (Lee et al., 2014; Palazzeschi et al., 2018). In one example, Gurusinghe et al. (2021) claimed that theory-based correlation for adopting HR analytics and contextual factors that affect building a predictive HR analytics capability is still lacking. Engineers such as Vatousios and Happonen (2021) also indicated that, although HR analytic automation still faces challenges, the data provide a practical analytical framework for decision-making by comparing team members’ performance and optimizing learning and development. Although Wiblen (2016) noted that a set of talent management criteria are complex to pre-defined and automate as it is subject to consideration of human and organizational capital, Wiblen and Marler (2021) stressed that collaboration between HR managers, departmental managers and automation experts are essential to pre-determine the appropriate automated criterion based on the needs of the organization.
Although artificial intelligence and analytics have played an essential role in human resource management recently, Pols (2019) highlighted the differences between people analytics and talent intelligence. The two departments often merge in organizations, causing blurry lines between the two roles in the company. People analytics usually focuses on current staff, looking inwardly using tools and systems. On the other hand, talent intelligence emphasizes leveraging external data, tools and sources to identify talent management opportunities that the external market offers to understand the bigger picture. The author also highlighted the role of talent intelligence in studying salary benchmarks, competencies forecasting, talent insights and availability, competitor insights, and location feasibility. Talent intelligence experts usually collaborate with the talent acquisition department 1 day and with a strategy department on another. They are called for in various departments for their predictive advisory skills regarding talent management insights and skills in leveraging artificial intelligence data.
While automation and artificial intelligence ruled Industry 4.0, scholars were also concerned with aligning their HR strategies to global environmental sustainability. Ogbeibu et al. (2022) indicated that green talent management significantly predicted employees’ turnover intentions. The concept emphasizes the organization’s importance in aligning and deploying capabilities and resources to achieve the United Nation’s environmental sustainability while maximizing profits through tangible achievements that are valuable, rare, not easy to imitate, and not excessively complex to organize. While Renwick (2018) noted that articles related to green human resource management are still pretty new to the field, Haddock-Millar and O’Donohue (2022) indicated that there were two distinct approaches to aligning the objectives of talent management in achieving environmental sustainability. First, adopting the green approach in talent management affects environmental-driven changes in the business (Glen et al., 2009; Ren et al., 2018; Renwick, 2018). The means could involve defining talent requirements, discovering new sources of talent, developing talent potential, and deploying the right employees to the right place. Glen et al. (2009) called these increasing talents “green talents.” They were driven by four emerging factors due to environmental sustainability initiatives—new values, new technologies, new investments and new regulations. Ren et al. (2018) also presented the second approach in green talent management, which involves promoting behavioral and attitudinal changes. Kivinda et al. (2021) also stressed that green HRM is particularly effective in promoting retention behaviors when the employer branding is tagged to the organization’s commitment to environmental values, strategies and orientation.
The Frameworks
Mindset Change Management
Jacobs et al. (2013) depicted change management as fraught with risk, often failing to meet expectations or incurring high opportunity or process costs. Nevertheless, it remains crucial for preventing problems and enhancing organizational resilience. Armenakis et al. (2007) emphasized that top management identified five critical sentiments precipitating the inclination for organizational change: discrepancy, appropriateness, efficacy, principal support, and personal valence for change. Lewin’s (1947a, 1947b) three-step model of “unfreezing,”“change,” and “refreezing” served as an early guide for subsequent research in organizational change.
Developed by former McKinsey consultants T. J. Peters and Waterman (1982), the McKinsey’s 7s framework delineates interconnected factors affecting organization change capability. Originally designed to aid organizations in mapping changes to enhance efficiency and pinpoint process inadequacies (Helmold, 2021), it has found application in diverse business contexts, including quality management of the research ethos in a business college (Paquibut, 2017), effective leadership-in-management in education (Rausch et al., 2001), key account management implementation (Guenzi & Storbacka, 2015), and university service improvements (Jollyta et al., 2021). In the context of change management research utilizing the McKinsey 7s framework, the “style” component refers to the leadership approach influencing the way employees work within the company. To attain competitiveness, possessing appropriate “skills” is imperative. The “staff” component pertains to talent management and maximization through training and proper allocation. “Systems” relate to the processes and typically involve technology-related implementations required for daily operations. Conversely, “structure” delineates the role of organizational hierarchy and the definition responsibility delegation. “Strategy” outlines the plans to attain competitiveness across various business segments, developed to navigate the complexity and ambiguities of the virtual environment. Lastly, “shared values,” the core of all components, articulate the company’s mission and culture, guiding workforce behavior. Shared values and staff diversity components are significantly influenced by various drivers and both internal and external environments; hence, this paper considers these two components as dependent variables.
Collaborative Intelligence Mindset Theory
As Markova and McArthur (2015) implied, the roots of collaboration begin with a change of mindset. The Collaborative Intelligence Quotient (CIQ) is a valuable component of collaboration. The CIQ mindset component is akin to a tree seed; once planted, it will extend its roots, and a tree will flourish. This tree requires drivers like sunlight, water, and nutrients from the ground. Similarly, once a collaborative mindset is established, appropriate drivers must support the mindset to enable collaboration. The established collaborative mindset will enable diverse, cross-functional work and cultural diversity, constructing company values conducive to excellent collaborative partnerships. The theoretical framework can explore what encourages collaboration within an organization and examine the integral types of mindset, the diverse workforce required, and the core values ideal for collaborative projects. The research framework proposed in this study is named The Collaborative Intelligence Mindset Theory, as shown in Figure 1. Supported by the drivers, a collaborative mindset represents a resilient and strategic talent management framework for the future of Society 5.0. Table 1 outlines how the framework components could probe ways to cultivate a collaborative mindset throughout the organization.
The collaborative intelligence mindset theory.
Collaborative Intelligence Mindset Theory.
Only five areas from McKinsey’s 7s change management framework (refer to Table 1) were used to investigate collaborative talent management. In the proposed framework, diverse people and the core values are seen as dependent variables rather than independent ones in this study. Core values and workforce diversity are not drivers of collaboration. In other words, collaborative organizational values involving diverse talents will only exist if a collaborative mindset is successfully established. The role of a diverse workforce in talent management is not new, and one perfect example of this notion can be seen in the findings of Croitoru et al. (2022). They found that diversity in the workforce could significantly raise motivation, innovation, leadership, and social responsibility. Therefore, we can now see how the collaborative intelligence mindset framework is directly linked to strategic talent management.
Methodology
In accordance with the previously stated objectives and conceptualized framework, this paper endeavors to synthesize the present state of knowledge concerning collaborative talent management. In doing so, it seeks to deepen the understanding of this specialized subject by delineating future research priorities. Ensuring the value of this systematic review to its readership, this paper commits to providing a transparent, comprehensive, and accurate representation of the review process, adhering to the PRISMA quality checklist as highlighted by Page et al. (2021). Paul and Criado (2020) categorized systematic reviews into method-based, theory-based, and domain-based formats. Given that this review primarily focuses on synthesizing the literature specific to the domain of collaborative talent management, a domain-based approach is aptly selected. Furthermore, the authors emphasize that thematic reviews grounded in a structured framework tend to be more favorably received due to their inherent robustness.
In this review, the keyword “collaborative talent management” was purposefully selected to encapsulate the essence of contemporary talent management strategies, especially those pertinent to collaborative work settings through a mindset perspective. By employing the Web of Science database, known for its extensive and superior collection, this review seeks to bolster the trustworthiness and thoroughness of its findings. Upon initially using the keywords in quotation marks, only two articles emerged, one of which appeared irrelevant. To broaden the search, the terms were adjusted to “collaborative collaboration in talent management” without quotation marks. Only the “article” filter was applied, with no other automated exclusion criteria used. Given the niche nature of the papers retrieved, this review encompasses peer-reviewed articles from all available year ranges. This yielded 63 results, which was narrowed down to 35 after applying the “article” filter. After reviewing abstracts and excluding non-English and overly technical articles, the final list for this review consisted of 20 articles. To elucidate the data collection process, a PRISMA flow chart (Page et al., 2021) is utilized, as depicted in Figure 2, which delineates the entire process.
PRISMA flow chart.
It was noteworthy that the search produced a limited number of articles specifically on collaborative collaboration in talent management. These articles spanned a variety of industries and demonstrated diverse collaborative talent management initiatives. The reviewed articles addressed issues related to collaborative talent management, either directly or indirectly, in sectors such as healthcare (Kovacs & Drozda, 2015; Veith & Marin, 1996; Wijaya et al., 2019), research and higher education (Barnes et al., 2021; Borah et al., 2019; J. S. Huang & Brown, 2019; Kumaraswamy & Chitale, 2012; Saetnan & Kipling, 2016; Samuel et al., 2016), leadership and human resources (Ibarra & Hansen, 2011; Maheshwari et al., 2017; Makarius & Srinivasan, 2017; Schweer et al., 2012; Yalenios & d’Armagnac, 2023), internet and technology (Camarinha-Matos & Afsarmanesh, 2012; Suzuki & Yamamoto, 1999), advertising and marketing (Addison et al., 2017; Patwardhan et al., 2019), and construction and geo-analytics (Ma et al., 2021; Walker & Lloyd-Walker, 2016).
Findings
Medical and Healthcare
The earliest article in these search results dates back to Veith and Marin (1996), where medical practitioners explored a collaborative, multispecialty approach to deliver optimal endovascular treatment using new endovascular technologies. The collaborative approach is essential as it strengthens the relationship between vascular surgeons and other interventionalists, such as radiologists, when deploying endovascular technologies. Collective leadership can generate a sufficient number of well-trained specialists, addressing staffing shortages in vascular disease treatment. Although efficiency is likely to increase when vascular surgeons and interventional radiologists collaborate across departments, this transition won’t be immediate due to resistance from various stakeholders. Nevertheless, the collaborative efforts can reduce costs by preventing care segmentation, avoiding unnecessary procedures motivated by financial self-interest, and eliminating biased procedure based on a doctor’s background or skills.
Kovacs and Drozda (2015) investigated team-based cardiac care, drawing on the resources, skills and ideas from various specialists, and recommended offering education tailored for teams rather than just individuals. However, a noticeable gap existed, as there were few medical certifications designed to evaluate medical teams. Furthermore, there was an evident need to provide patient data specifically for team-oriented purposes, and a significant allocation of resources should be channeled toward establishing innovation centers to enhance team-based cardiac care. In a distinct collaborative effort documented by Wijaya et al. (2019), Siloam Hospital in Thailand fostered collaboration between the marketing and talent management departments using virtual communication to gage and elevate patient satisfaction. This virtual approach yielded predominantly positive results, boosting the patient satisfaction index by 80% within a span of 6 month.
Leadership and Human Resources
Leadership and human resource practitioners have long been concerned with developing a workforce that collaborates more effectively across the organization to gain a competitive edge. Ibarra and Hansen (2011) emphasized that a collaborative leadership style is crucial for nurturing a talent-rich workforce. They pinpointed competencies in four domains: assuming a global connector role, attracting diverse talents, serving as a role model for collaboration in top management, and providing robust support for employee collaboration. The authors expressed concern that many leaders failed to harness the benefits of a well-connected company, often overly relying on command-and-control leadership styles. While a well-networked company yields positive outcomes, it also presents challenges, including maintaining a consistent operating ethos, navigating differences in beliefs, and managing diversity.
Several empirical studies have shown that a collaborative network in sourcing organizational talents can lead to a significant increase in an organization’s revenue. According to Schweer et al. (2012), consulting firms, including McKinsey & Company and IBM, have observed that companies investing in social networking and collaborative tools in daily operations often enjoy higher profits and market share than less-connected counterparts. These authors also noted that organizational network analysis can shed light on collaboration patterns within a company and gage the effectiveness of group interactions within specific teams. By employing this system, human resource managers can monitor individual employee performance and strategically position the most suitable candidates within broader, untapped network. By analyzing the data provided by the network analysis, managers can categorize employees into four categories: high-performing talent, marginalized talent, hidden talent, and underutilized talent. While insights derived from these employee categories might seem inconsequential initially, their interrelations can profoundly impact organizational performance if thoughtfully considered. Intriguingly, only 20% of employees within an organization are high-performing. Thus, while companies often dedicate considerable resources to high-performing talents, it is equally important to integrate and harness the potential of marginalized, hidden, and underutilized talents to explore untapped revenue avenues.
Talent management in human resources has evolved to encompass collaborative efforts from various fields. For example, Maheshwari et al. (2017) identified employer branding as crucial for fostering a long-term, trust-based collaborative relationship between an organization’s human resource and marketing departments, drawing from marketing concepts. These concepts are derived from signaling theory and reputation management literature, with a particular emphasis on workplace encompassing corporate culture, job nature, career growth opportunities, and competitive compensations. Additionally, some talent management strategies treat talents analogously to supply chain management, emphasizing the importance of efficient collaboration between the organization and talent suppliers (Makarius & Srinivasan, 2017). This approach employs strategic sourcing, a tactic that addresses risks such as mismatches while managing talent demand and supply. Conversely, others believe the focus should be on maintaining a high-quality alignment of the human resource ecosystem, which fosters collaboration throughout an organization (Yalenios & d’Armagnac, 2023). The ecosystem is dynamic, necessitating that companies adeptly navigate three-step framework of alignment-disalignment-realignment in response to ecosystem’s demands. By doing so, companies ensure they are continually prepared for disruption when external dynamics dictates a shift.
Internet and Technology
Digitalization for collaboration is an indisputably essential tool in numerous aspects of collaborative talent management. In the earlier stages of business digitalization, frameworks designed for dispersed collaborative-development environments were in high demand. Suzuki and Yamamoto (1999) introduced an integral framework named SoftDock, which has been widely employed as a model in the application development for varied purposes, including medical record systems, hypermedia model management, digital libraries, and distance learning.
In another study, Camarinha-Matos and Afsarmanesh (2012) examined a new digital ecosystem that encompasses technological, organizational and social paradigms. This ecosystem enables a network of collaborative support for aging professionals, allowing them to continue their professional contributions post-retirement and simultaneously meeting the industry’s demand for a skilled and experienced workforce. This research holds significance in the literature, as it potentially prolongs the professional lives of senior individuals. It harmoniously integrates professional contributions with social and leisure activities, facilitated by inter-generational interactions that counteract the formation of elderly enclaves in an urban setting.
Marketing and Advertising
Another avenue of study in collaborative management highlights the dynamic marketing and advertising sector. In the United States, despite its limited discussion in academic circles, the imperative for the creative industry to foster collaboration among diverse creative talents is undeniable. However, the contemporary digital prerequisites of 21st-century media further challenge the evolution of these creative agencies. The talent pool requirements are diverse, spanning business and marketing specialists, data analysts, technology and digital content experts, media and account planners, as well as artists and writers. In their empirical research, Patwardhan et al. (2019) emphasized that, to thrive in the ever-changing digital marketing landscape, leaders continuously explore innovative methods for team collaboration, adaption, and negotiation. Such a mandate necessitates a fundamental shift in organizational structure, favoring a more horizontally integrated approach over the traditional vertical hierarchy. Successful leaders exhibit transformative soft skills, leveraging relationships to cultivate a collaborative ethos throughout the organization.
Similarly, Addison et al. (2017) observed that B2B collaboration in marketing is seldom encountered, primarily because the traditional marketing benchmark of opportunism in selfish selling impedes the growth of numerous promising entrepreneurial practices. The authors introduced an innovative alternative termed Intellectual Capital Sharing (ICS). This approach diverges from conventional selling methods, instead offering free consultation based on knowledge, experience and capabilities that assist business partners in addressing challenges while fostering customer value co-creation. They pinpointed four affective aspects in selling that nurture collaborative partnerships: relating, bonding, caring, and building trust.
Project Management & Construction
According to Walker and Lloyd-Walker (2016), in Australia and New Zealand, it is customary to incorporate project or program alliances in the project procurement of engineering ventures, such as those in the construction of buildings, utilities, sewage, water, rail, and road. They introduced the concept of relationship-based project procurement (RBP) taxonomy. This represents a collaborative effort encompassing platform foundational facilities, behavioral factors, and deliberately arranged processes, routines and means to achieve the intended outcomes. The infrastructural components lay a robust foundation for collaboration at the platform foundational facilities level. Conversely, the behavioral factors allude to the desired levels of authentic leadership, trust-control balance, commitment to innovation, and various mindsets or cultural factors. To bolster and promote these behavioral elements, well-conceived processes, routines, and means are deemed essential.
In a sub-segment known as geo-analytics, there’s a demonstrated need to leverage multidisciplinary stakeholders within the construction field. Ma et al. (2021) proposed eight core activities centered on a visualization-based method. These activities and experts are interconnected through a web-based workspace comprising online tools and resources. The geo-analytic processes include resource collection and context definition, data processing, data analysis, data visualization, geo-analysis model construction, model effectiveness evaluation, geographical simulation, and decision-making. In their case study, they discovered that the proposed prototype system effectively encourages collaboration and mitigates the challenges of specific interdisciplinary issues, establishing a solid participatory foundation for constructing viable solutions to the problem at hand.
Research & Higher Education
Research and development are paramount in numerous national and international science strategies, focusing especially on sustainability and climate change components. For instance, within the food and agriculture industry, Saetnan and Kipling (2016) explored the potential of knowledge hubs like MACSUR (Modeling European Agriculture with Climate Change for Food Security) in facilitating interdisciplinary collaboration. MACSUR, establishing the partnership of 70 institutes across 18 countries, bridges disciplines such as agriculture economics, farms, livestock, crops and grasslands, focusing on the impacts of climate change. The authors emphasized the advantages of such global collaboration, including enhanced research impact, increased probability of high-quality international publication, higher citation rates for internationally co-authored papers, and the generation of innovative insights from diverse backgrounds. They also noted several obstacles to collaboration, including misalignment of reward systems within disciplines, perception of trade-offs in career advancement, academic biases toward interdisciplinary collaboration, and other heterogeneities like disciplinary norms, practices and language. The establishment of knowledge hubs and advanced network analytics has made such collaboration more feasible, with the effectiveness of MACSUR’s approach empirically validated over 3 year of observation. However, sustaining these hubs requires substantial long-term institutional and resource support to maintain networking activities.
Similarly, J. S. Huang and Brown (2019) applied analytic tools from Social Network Analysis to demonstrate the dynamics of research collaboration among researchers in higher education. This system promoted diversity, discouraged coercive collaborations, and optimized team size. Many concur that collaborative talent management in higher education necessitates a thorough, well-developed learning system that enables knowledge sharing, facilitates communication, and encourages interaction between the industry and institution. It should also incorporate a reward system to acknowledge sharing efforts (Kumaraswamy & Chitale, 2012). Barnes et al. (2021) emphasized the importance of cultivating an institutional culture in higher education that supports academician career progression through the integration of values, practices and behaviors, such as equity, inclusion, and ethical collaboration, as well as institutional resources and support. They discovered that academicians value interconnected performance management, continuous career management system, comprehensive induction, collaborative organizational structure, and competitive salary. Besides internal institutional collaborative management, Samuel et al. (2016) emphasized the significance of collaboration with external industries to ensure higher education curricula align with industry needs, producing graduates equipped with relevant skills and knowledge. Their case study specifically focused on educating the manufacturing sector through a collaboration between a research university and a community college.
Discussion
Collaborative talent management has been identified as a pivotal strategy, crucial for enhancing performance, innovation, and efficiency within a multitude of sectors such as medical and healthcare, leadership and human resources, internet and technology, marketing and advertising, project management and construction, as well as research and higher education. The prevailing literature on collaborative talent management, although limited in its scope, offers crucial insights into the vital role that collaboration plays in navigating challenges and securing competitive advantage. While strides have been made in elucidating the value of collaboration, there remain notable gaps in the literature and promising avenues for future research, highlighting the pressing need for more robust and extensive explorations into the complex realm of collaborative undertakings in organizational landscapes. In alignment with the objectives of this review, this article proposes a series of potential future studies designed to address the identified gaps in the existing literature.
A domain warranting meticulous exploration is the array of interdisciplinary barriers that hinder optimal collaboration. Existing literature meticulously articulates various impediments to interdisciplinary synergy, encompassing aspects such as reward systems, trade-offs and career progression, prevailing academic biases, and the entrenched norms, practices, and languages specific to each discipline (Ma et al., 2021; Saetnan & Kipling, 2016; Samuel et al., 2016; Walker & Lloyd-Walker, 2016). Subsequent research endeavors should pivot toward identifying and implementing pragmatic strategies capable of transcending these identified barriers, aiming to cultivate enhanced interdisciplinary collaborations. There is a plausible need for a transformative reassessment and recalibration of reward systems and a comprehensive reshaping of disciplinary norms and practices to orchestrate a climate that is more hospitable to interdisciplinary dialogs and integrations.
The process of measuring and evaluating the efficacy of collaborative talent management stands out as another dimension necessitating deeper scholarly inquiry. The extant literature, such as Ibarra and Hansen (2011), highlights the pivotal role of collaboration in amplifying both performance and resultant outcomes. Nonetheless, the research landscape remains scant on elucidating methods that excel in gaging and assessing the reverberations of collaborative talent management on the performance of an organization. Subsequent research could endeavor to forge refined metrics and evaluative instruments, aimed at quantifying the advantages accruing from collaborative endeavors and identifying realms needing enhancement. Such advancements would empower organizations to render enlightened decisions pertinent to their collaborative pursuits, facilitating a more nuanced understanding of the interplay between collaboration and organizational performance.
An additional focal point in academic exploration is discerning the role digitalization plays in enriching collaboration. Although extant literature subtly intimates the substantial importance of digitalization (Camarinha-Matos & Afsarmanesh, 2012; Suzuki & Yamamoto, 1999), research on how to optimize a myriad of digital tools and technologies for collaborative talent management is still in its infancy. Prospective studies could immerse into the creation and refinement of novel digital platforms and tools, meticulously crafted to enhance collaboration spanning varied industries and sectors. A conceivable trajectory for research could encompass the scrutinization of the effectiveness of current digital instruments and the delineation of distinctive features that underpin successful collaborative milieus. It might further extend into examining the potential implementations of digital automation in collaborative initiatives that leverage diversity, reflecting the neuroscientific tenets inherent in the CIQ framework (Markova & McArthur, 2015). Such exploration promises not only to bridge existing knowledge gaps but also to provide empirical insights into the intricate synergy between digitalization and collaborative realms.
Moreover, a meticulous exploration into leadership styles and the fostering of a collaborative culture is imperative. While existing literature unequivocally recognizes the importance of collaborative leadership styles in galvanizing a talented workforce (Tayebloo & Shirvani, 2015), empirical evidence delineating the most efficacious leadership styles and practices to inculcate a collaborative culture remains sparse. Subsequent studies might endeavor to critically examine the distinct leadership qualities and conduct conducive to flourishing collaborative talent management, possibly illuminating the methodologies through which such qualities and conduct can be developed and refined within organizational settings. This approach could potentially facilitate a richer, multifaceted understanding of the symbiotic relationship between leadership and collaborative organizational environments, contributing invaluable insights to the domain of collaborative leadership as well as virtual leadership (Chew & Mohamed Zainal, 2022).
Finally, the exploration of the possible advantages and inherent challenges of cross-sector collaboration stands as a relatively untrodden domain. The existing review predominantly concentrates on collaborative undertakings within distinct sectors, leaving a discernible void in research regarding the prospective benefits and impediments inherent to cultivating collaborative talent management across diverse industries and sectors. Subsequent research endeavors could meticulously investigate the prospects and dilemmas intrinsic to cross-sector collaboration, unearthing optimal approaches and strategies conducive to successful inter-sector cooperative ventures. This scrutiny may unveil singular synergies and avenues for innovation that would presumably remain undiscovered, fostering a deeper, more nuanced comprehension of the latent potential residing in cross-sector collaborative initiatives.
While the current review offers insightful perspectives, it is subject to several limitations. Firstly, it is inherently limited by the scant volume of existing literature specifically dedicated to collaborative talent management, possibly leading to an absence of comprehensive insights into the subject matter. Additionally, the review’s focus on distinct sectors potentially restricts its applicability to diverse industries and contexts, leaving the realm of cross-sector collaboration largely uncharted. Moreover, the absence of quantitative empirical data in the review might impede the provision of more substantial evidence and discerning insights. The review is chiefly tailored to discern research lacunae, bypassing a meticulous exploration of the complexities of collaborative talent management via rigorous methodologies and discerning evaluations. Thus, it falls short of offering an exhaustive evaluation of the validity and quality of the prevailing literature, elements crucial for shaping forthcoming research inquiries and trajectories. Nonetheless, despite these constraints, the review serves as a pivotal precursor for delving into the importance of collaborative talent management and underscores the imperative for more nuanced explorations in the identified crucial domains.
Conclusion
This review attempts to conceptualize collaborative talent management by integrating a collaborative intelligence mindset perspective. The novelty of this paper will lie in its valuable findings that stimulate epistemological conversations regarding the concept of collaborative talent management. The existing body of literature on talent management has predominantly been vague, not to mention the evolving business environment and external influences shaping managerial decisions. Therefore, the collaborative intelligence mindset theory serves to bridge the viability gap and foster collaboration in the field of talent management, as illustrated in Figure 3. Questions derived from the perspective of a collaborative mindset, enumerated in Table 1, will also aid managers in implementing a collaborative culture, providing clear guidance toward effective collaboration. Although the current research directions proposed can offer a preliminary conceptualization of collaboration from a collaborative intelligence mindset perspective, this novel concept is still in its infancy, necessitating further research for empirical investigation and validation.
Research gaps of collaborative talent management.
Covid-19 has likely served as a wake-up call for transformation into more resilient and sustainable businesses (van Zanten & van Tulder, 2020). Companies are now considering altering how a business operates, thus reshaping perspectives on leadership and talent management initiatives. The current paper aligns with several of the United Nations’ Sustainable Development Goals (SDGs): SDG No. 3—good health and well-being; SDG No. 8—decent work and economic growth; SDG No. 9—building resilience, promoting sustainability, and fostering innovation; and finally, SDG No. 17—partnership for the goals (United Nation, n.d.). United Nations emphasize partnerships as a crucial mechanism, enhancing the enactment of change strategies (Duane et al., 2022). The achievement of partnership and collaboration will indeed facilitate the attainment of other sustainable goals. Now is the time to think innovatively to navigate future vulnerabilities while promoting the well-being of humanity through all forms of collaboration.
Footnotes
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The work was supported by Universiti Sains Malaysia and Ministry of Higher Education under the Fundamental Research Grant Scheme (FRGS)
[Grant Number: FRGS/1/2020/SS01/USM/02/1]
Data Availability Statement
Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.