Volume 27 Issue 1 Article 3 March 2025 Determinants of Interorganizational Employee Mobility: Determinants of Interorganizational Employee Mobility: Systematic Literature Review Systematic Literature Review Iva Zdrilić University of Rijeka, Faculty of Economics and Business, Rijeka, Croatia AND University of Ljubljana, School of Economics and Business, PhD Student, Ljubljana, Slovenia, iva.zdrilic@efri.uniri.hr Petra Doš enović Bonč a University of Ljubljana, School of Economics and Business, Ljubljana, Slovenia Darija Aleksić University of Ljubljana, School of Economics and Business, Ljubljana, Slovenia Follow this and additional works at: https://www.ebrjournal.net/home Part of the Human Resources Management Commons, and the Organizational Behavior and Theory Commons Recommended Citation Recommended Citation Zdrilić , I., Doš enović Bonč a, P ., & Aleksić , D. (2025). Determinants of Interorganizational Employee Mobility: Systematic Literature Review. Economic and Business Review, 27(1), 50-79. https://doi.org/ 10.15458/2335-4216.1352 This Review Article is brought to you for free and open access by Economic and Business Review. It has been accepted for inclusion in Economic and Business Review by an authorized editor of Economic and Business Review. REVIEW ARTICLE Determinants of Interorganizational Employee Mobility: Systematic Literature Review Iva Zdrili´ c a,b, * , Petra Došenovi´ c Bonˇ ca c , Darija Aleksi´ c c a University of Rijeka, Faculty of Economics and Business, Rijeka, Croatia b University of Ljubljana, School of Economics and Business, PhD Student, Ljubljana, Slovenia c University of Ljubljana, School of Economics and Business, Ljubljana, Slovenia Abstract This article provides a comprehensive overview of the determinants of interorganizational employee mobility and proposes their multilevel typology across different sectors and industries. We conduct a systematic literature review of 158 papers in this eld. Our results show that some determinants of employee mobility also appear as mobility effects, closing the “determinants–interorganizational employee mobility–effects” circle. They also show a hierarchy among mobility determinants, different types of interorganizational mobility, and different effects when mobility occurs between competing and cooperating organizations. This study primarily contributes to the career development literature, which recognizes interorganizational mobility as the primary mechanism for achieving individual work–life balance goals. By systematically constructing a multilevel typology of mobility determinants and examining their effects across different sectors and industries, the study’s ndings also contribute to labor economics by helping to understand the motivations of mobile employees and mitigate potential negative consequences of such mobility. Keywords: Interorganizational mobility, Systematic literature review, Multilevel typology, Mobility determinants, Mobility effects JEL classication: J62, M5 1 Introduction I n today’s work environment, employees are the central stakeholders of an organization and one of the most important sources of long-term competitive advantage (Alnidawi et al., 2017; Memon et al., 2009; Pasban & Nojedeh, 2016; Liu et al., 2020). As globalization advances, organizations can no longer rely solely on physical capital and natural resources to compete in the global economy. Instead, human capital has taken precedence to respond appropriately to the changing realities in the global economy (Amankwah-Amoaha & Debrahb, 2011). Unlike the other strategic resources and capabilities that the organization can build, borrow, and/or buy in the marketplace, valuable employees are difcult to obtain or imitate (Campbell et al., 2017), making investment in developing their knowledge and skills imperative to the success of any organization (Bassi & McMurrer, 2007). However, while investing in human capital is a driving force for success, it also poses a great risk to the organization because the investment in an employee’s skills and knowledge often cannot be protected if the employee decides to leave the organization permanently. As ongoing technological, societal, and economic changes intensify the phe- nomenon of interorganizational employee mobility (Mawdsley & Somaya, 2016; PricewaterhouseC- oopers, 2018), the associated risks are particularly pronounced in knowledge-intensive industries such as information technology (IT), semiconductors, nance, pharmaceuticals, higher education, and so forth. In such industries, key organizational resources are largely embedded in the minds of knowledge Received 17 July 2024; accepted 18 December 2024. Available online 10 March 2025 * Corresponding author. E-mail address: iva.zdrilic@efri.uniri.hr (I. Zdrili´ c). https://doi.org/10.15458/2335-4216.1352 2335-4216/© 2025 School of Economics and Business University of Ljubljana. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/). ECONOMIC AND BUSINESS REVIEW 2025;27:50–79 51 employees (Bermiss & Greenbaum, 2016). Possessing this tacit knowledge and being able to take it with them when they leave the organization empowers knowledge employees to shape their own work and careers by taking advantage of various employment opportunities (Donnelly, 2009). For this reason, the phenomenon of interorganizational employee mobility is particularly interesting for such industries. Interorganizational employee mobility is diversely dened as the transfer of human capital from one organization to another (Wynen et al., 2013), as the movement of employees across the boundaries of different employers (Collet & Hedström, 2013; Haunschild, 2003; Litano & Major, 2016), as the job change that accompanies a change of employer (Korpi & Mertens, 2003), as the ow of human capital due to labor market competition between the two organi- zations (Liu et al., 2020), as the transfer of knowledge and skills acquired by individuals through their education, training, and experience from one organization to another (Amankwah-Amoaha & Debrahb, 2011), and as the mechanism by which organizations use and implement new scientic and technological knowledge and information (Lee, 2020). It differs from mere turnover behavior because the latter focuses only on calculating the ratio of employees who left the organization over a period of time to those who remained in the same organization (Price, 1977), whereas interorganizational mobility emphasizes the movement of employees that links two organizations: the source organization, or the organization from which the employee is moving, and the destination organization, or the organization to which the employee is moving. Thus, it is not just about quitting a job, which could be an involuntary act, but about the voluntary decision to change the existing job, which includes changing the organization. In addition, the growing awareness of the importance of knowledge as a competitive advantage, as well as its transferability when highly skilled employees move to another organization (Ahn & Ok, 2019), has led to increased research on both the determinants and effects of interorganizational employee mobility and the approaches employers take to manage them. Early research on interorganizational employee mo- bility focused primarily on the effects of mobility (Tzabbar & Cirillo, 2020), such as creativity and in- novation that drive productivity, behavior change, or- ganizational survival, nancial performance, and so forth (Lee, 2020). On a larger scale, employee mobility led to a new concept of career development, the so- called boundaryless career, which allows employees to break away from traditional lifelong employment with a single employer and go beyond the boundaries of any single organization (Arthur et al., 2005). How- ever, more current research on interorganizational employee mobility highlights the importance of ex- amining its determinants (Tzabbar & Cirillo, 2020). Despite the many partial ndings on these determi- nants, they have remained somewhat scattered and ambiguous (Aguirre et al., 2015; Kornblum et al., 2018; Lee, 2020). Some studies consider them only as individual motivations and incentives, while oth- ers consider them as contextual determinants, but without specifying the differences between the per- spective of a single organization and that of a broader labor market (Kornblum et al., 2018). Moreover, some studies suggest that one mobility determinant such as position in the organizational hierarchy prevents or negatively affects interorganizational mobility (i.e., the higher the position in the current organization, the less likely the employee is to move to another organi- zation; Ali et al., 2018; Campbell et al., 2012), while some other studies show that the same determinant drives or positively inuences the same phenomenon (Kiefer et al., 2022; Sgobbi & Suleman, 2015) because an employee with a better rank in one organization also has greater chances of moving to another orga- nization. Similar inconsistent results are also found in studies with other mobility determinants such as gender, education, organizational size, organizational location, and so forth. All these ndings indicate that current research on interorganizational employee mobility has still not provided a comprehensive overview of its determinants. Due to the increasing liberalization of markets and ever-changing insti- tutional arrangements (Amankwah-Amoaha & De- brahb, 2011), an in-depth analysis of mobility deter- minants becomes particularly important and relevant for the success of economic policies aimed at reducing spatial inequalities and developing economic growth strategies (Amarante et al., 2019). Moreover, scholars today point out that no mobility research can be con- ducted without considering the context of employee mobility that denes the opportunities and values as- sociated with this phenomenon (Tzabbar & Cirillo, 2020). Therefore, current mobility research also raises awareness of interorganizational mobility as a mul- tilevel phenomenon, thus implying its determinants as well as effects should be studied across economic, organizational, and individual levels (Tzabbar & Cir- illo, 2020). Nevertheless, such a multilevel approach to examining determinants has already been pro- posed and applied, but only in the context of the employee turnover literature (Moynihan & Landuyt, 2008; Muchinsky & Morrow, 1980). And since the phenomenon of interorganizational mobility goes be- yond a simple turnover behavior, current mobility re- search urgently calls for a missing multilevel typology 52 ECONOMIC AND BUSINESS REVIEW 2025;27:50–79 of its determinants which would contribute to fully and properly understanding the entire phenomenon. Accordingly, the main purpose of our paper is to develop a comprehensive multilevel typology of the determinants of interorganizational employee mobil- ity across different sectors and industries. With such a typology, we provide a deeper understanding of the factors that precede interorganizational employee mobility. We also provide insights into the interac- tions among the various determinants and identify those that remain underresearched. On a broader level, our study helps address the negative effects of mobility and reap the benets of improved cooper- ation between source organizations and destination organizations. It also explains what inuences indi- viduals’ decisions to leave their employers, which in turn has implications for all the organizations in- volved (Wezel et al., 2006). Based on a systematic literature review of 158 papers in the eld, we con- duct an in-depth analysis of mobility determinants and propose a multilevel typology. We also address mobility effects since they are an inevitable part of any mobility research, and it is necessary and urgent to consider their multilevel nature too (Tzabbar & Cirillo, 2020). The rest of the paper is organized as follows: The next section describes the methodology used in the systematic literature review, while the following sections focus on the multilevel typology of mobility determinants and the observed mobility effects, followed by concluding remarks. 2 Methodology of systematic literature review To develop a multilevel typology of the deter- minants of interorganizational employee mobility, we conducted a systematic literature review, as this method has proven to be clear and reproducible, producing similar results even when the entire process of reviewing and selecting articles in the target eld is repeated (Pickering & Byrne, 2014). More specically, we based our systematic literature review on Traneld et al.’s (2003) three-stage process, with planning of the review, that is, dening the main research objective and identifying key data sources, as the rst stage. The second stage refers to conducting the review, which begins with identifying keywords and search terms. This is followed by selecting and screening the studies and determining their inclusion and exclusion criteria, based on which the reviewers decide whether to retain or exclude some of the studies for further analysis after reading them thoroughly and assessing their quality (Cronin et al., 2008). The data-extraction process then follows, and this involves documenting all necessary information and characteristics of each study retained for analysis (e.g., title, authors, journal, publication details, etc.). This enables data synthesis, in which the results of the various studies on a topic or a research question are summarized and integrated (Traneld et al., 2003). The third and nal stage of Traneld et al.’s (2003) process involves reporting and dissemination, which includes a descriptive analysis of the target eld (e.g., main authors, main journals, number of citations, etc.) and writing the full report based on the ndings of the entire analysis, as well as recommendations for researchers and practitioners. As illustrated by Fig. 1, which shows our entire review process, we started Traneld et al.’s (2003) rst planning stage of the review process by conducting a preliminary search of the literature in this eld and found that despite many partial ndings on various factors of interorganizational employee mobility, almost all existing studies still focus on answering the question of why employees move and what happens when they do (Lee, 2020). Therefore, for the main objective of our study, we decided to develop a comprehensive multilevel typology of the de- terminants of interorganizational employee mobility across different sectors and industries and to properly classify the mobility effects. In addition, we identied our key data source: ISI Web of Knowledge Social Sciences Citation Index (SSCI) with all years available (1955–2022) at the time of the search (up to October 2022). This database is considered the most widely used and authoritative database of research publica- tions and citations in the world (Birkle et al., 2020), as it includes approximately 34,000 journals covering leading research in a wide range of scientic elds. In the second stage of Traneld et al.’s (2003) review process, we identied a list of the most im- portant keywords for database searching and used it as a selection criterion for the target topic (ti- tle, keyword, or abstract). The list included “in- terorganizational mobility” as the main term, along with its synonyms and their spelling variants: in- terorganizational mobility OR inter-organizational mobility OR interorganisational mobility OR inter- organisational mobility OR interrm mobility OR inter-rm mobility OR intercompany mobility OR inter-company mobility. The initial search of the database for these keywords yielded 294 papers. Af- ter rening this initial result using the “Languages” and “Web of Science Index” categories (“English” and “SSCI” were selected), 235 papers remained as the basis for further analysis. The “Web of Sci- ence Categories” lter was intentionally not used so that the papers from elds other than management, economics, and business would also be considered. Papers from sociology, psychology, and multidisci- plinary sciences were also benecial to provide a ECONOMIC AND BUSINESS REVIEW 2025;27:50–79 53 Fig. 1. Systematic literature review process. Source: adapted from Traneld et al. (2003). 54 ECONOMIC AND BUSINESS REVIEW 2025;27:50–79 more comprehensive insight into the determinants of interorganizational employee mobility across dif- ferent levels. Since the number of papers (235) was manageable and attainable, we started with the read- ing of each paper. This served to gain a deeper understanding of the content of each paper in order to know which of them to exclude from further analysis. Ultimately, we excluded 77 papers because they were not written in English (although we had specied the “Language” category at the outset) or because they did not focus precisely on interorganizational mobil- ity of employees. Instead, they focused on one of the following topics: customer and student mobility; in- formation and data mobility; mobility of rm assets and resources; knowledge mobility in an open-source context; mobility as a service; technological mobil- ity solutions for customers; mobility of computing and communication services; institutional mobility; green and sustainable mobility; mobility of calcula- tions in management accounting standards; mobility in transportation systems; geographic and physical mobility, including change of residence rather than change of employer; and rm mobility to new mar- kets or new partnerships, including rm relocation. Thus, the nal total number of papers for another round of thorough and in-depth reading and further analysis was 158. This repeated reading by each of us was used for the data-extraction process, which re- sulted in a spreadsheet le (ours was created in Excel) containing the following information for each of the papers we analyzed: author(s), journal, year of pub- lication, digital object identier (DOI) of the paper, type of paper (e.g., empirical, review, etc.), keywords, abstract, methodology used, main results/ndings, overall theoretical and practical contribution, and limitations of the paper and directions for future re- search. Since our primary focus was to examine the determinants of interorganizational employee mobil- ity, we began our data synthesis by creating another spreadsheet that contained detailed information on each determinant mentioned in each of the papers we read. At the outset, it was noted that the papers analyzed addressed these determinants only partially and in general terms, without specically classify- ing them at the appropriate levels (i.e., economic, organizational, and individual). Moreover, most of these studies dealt with different mobility effects, so the inclusion of these effects in our analysis seemed inevitable. However, there was no evidence of a struc- tured and comprehensive overview of these factors (i.e., determinants and effects of interorganizational employee mobility), which additionally reafrms the main purpose of our paper. The third and nal stage of Traneld et al.’s (2003) review process began with a descriptive analysis of the selected papers (158) and their authors (Table 1). The papers were published in 88 different journals and other publications. Among them, Organization Science is slightly ahead with 7% of the papers published in this eld. These journals belong to 24 publishers, ve of which published almost 70% of all the papers: Wiley (32), Elsevier (30), Sage (18), Taylor & Francis (14), and Informs (13). More than half of the papers were from business and economics (55%), followed by psychology (11%), environmental studies (6%), sociology (6%), and information science (3%). Among them, empirical papers represented the largest group (89%). In this group, authors applied different study designs and methods, such as surveys, interviews, experiments, qualitative and quantitative case studies, econometrics, and ethnographies. The remaining papers were literature reviews or meta-analyses (14 papers) and theoretical/conceptual papers (4 papers). Although the oldest paper was published in 1978 (Fig. 2), there has been continuity of publications in this eld since 1991 (with the exception of 1997), with about 5 papers per year. This average number almost tripled in 2010 (13 papers), which is a kind of turning point, as more than half of the papers (65%) have appeared since 2010 to date. However, the most cited paper dates back to 1999 and counts 1,412 citations, while the top 10 most cited papers (Table 2) together have 6,075 citations, accounting for more than half (55%) of the total number of citations (11,170). Most of these top 10 papers were published between 2003 and 2007, while the most recent among them is from 2015. Almost 70% of all the authors (296) of the analyzed papers have European afliations, with England, the Netherlands, Germany, Italy, and Sweden taking the top 5 places. However, when looking at country-level afliations, the United States dominate with a total of 154 afliations (65%) among all the countries. Among these U.S. afliations, the following universities lead: University of Illinois (8), University of Michigan (8), University of Texas (8), University of Pennsylvania (7), and California State University (6). More detailed results obtained in the third stage of Traneld et al.’s (2003) review process (reporting and dissemination) are described in the following sections, which examine in detail both the determinants and effects of interorganizational employee mobility as an antecedent and consequence of the same phenomenon. 3 Multilevel determinants of interorganizational employee mobility Our review of 158 selected papers in the eld of interorganizational employee mobility reveals ECONOMIC AND BUSINESS REVIEW 2025;27:50–79 55 Table 1. Descriptive analysis of 158 papers in the eld of interorganizational employee mobility. TOTAL NO. OF PAPERS: 158 Journals and other publications 88 Organization Science (11), Research Policy (7), Strategic Management Journal (7), Journal of Vocational Behavior (6), Administrative Science Quarterly (5), etc. Publishers 24 Wiley (32), Elsevier (30), Sage (18), Taylor & Francis (14), Informs (13), etc. Research elds 20 Business & economics (87), psychology (17), environmental studies (10), sociology (9), information science (4), etc. Paper types 3 Empirical papers (140), reviews and meta-analyses (14), theoretical papers (4) Active years of publication 33 Average no. of papers (per year) 4.8 TOTAL NO. OF CITATIONS: 11,170 No. of citations of the most cited paper 1,412 Almeida, P ., & Kogut, B. (1999). Localization of knowledge and the mobility of engineers in regional networks. Management Science Average no. of citations (per paper) 70.7 Average no. of citations (per year) 338.5 TOTAL NO. OF AUTHORS: 296 Countries/regions 32 Europe (202), United States (68), Asia (12), Australia and New Zealand (7), Canada (4), Latin America (3) Afliations 234 University of Illinois (8), University of Michigan (8), University of Texas (8), University of Pennsylvania (7), California State University (6), etc. No. of papers of the most productive author 4 Almeida, Paul Fig. 2. Number of papers and citations on interorganizational mobility per year. Source: Web of Science. that the targeted phenomenon has been studied increasingly since the beginning of the 21 st century. Until then, most of the literature on employee mobility dealt with intra-organizational mobility or exclusively with employee turnover. However, since its inception and up to the present day, one of the established themes related to employees moving from one organization to another has been the conditions and contexts that promote or hinder employee mobility (Checkley & Steglich, 2007; Tzabbar & Cirillo, 2020). To properly structure these determinants of interorganizational employee mobility, various authors have attempted to classify them into different groups or categories. Very early on, the renowned scholars March and Simon (1958) proposed two groups of mobility determinants: 56 ECONOMIC AND BUSINESS REVIEW 2025;27:50–79 Table 2. Top cited authors in the eld of interorganizational employee mobility. Author(s) and publication year Title Publication year Journal Total no. of citations Best citation year (no. of citations) Almeida, P ., & Kogut, B. Localization of knowledge and the mobility of engineers in regional networks 1999 Management Science 1,412 2013 (102) Rosenkopf, L., & Almeida, P . Overcoming local search through alliances and mobility 2003 Management Science 887 2020 (82) Perkmann, M., & Walsh, K. University–industry relationships and open innovation: Towards a research agenda 2007 International Journal of Management Reviews 726 2020 (85) Sampson, R. C. R&D alliances and rm performance: The impact of technological diversity and alliance organization on innovation 2007 Academy of Management Journal 656 2016 (74) Arthur, M. B., Khapova, S. N., & Wilderom, C. P . M. Career success in a boundaryless career world 2005 Journal of Organizational Behavior 630 2019 (62) Song, J., Almeida, P ., & Wu, G. Learning-by-hiring: When is mobility more likely to facilitate interrm knowledge transfer? 2003 Management Science 574 2018, 2020 (47) Whittington, K. B., Owen-Smith, J., & Powell, W. W. Networks, propinquity, and innovation in knowledge-intensive industries 2009 Administrative Science Quarterly 334 2014, 2020 (35) Beaverstock, J. V . Transnational elites in the city: British highly-skilled inter-company transferees in New York City’s nancial district 2005 Journal of Ethnic and Migration Studies 311 2019 (31) Campbell, B. A., Ganco, M., Franco, A. M., & Agarwal, R. Who leaves, where to, and why worry? Employee mobility, entrepreneurship and effects on source rm performance 2012 Strategic Management Journal 277 2019 (49) Balland, P . A., Boschma, R., & Frenken, K. Proximity and innovation: From statics to dynamics 2015 Regional Studies 268 2022 (49) Total no. of citations: 6,075 the perceived desirability of movement and the perceived ease of movement from an organization. Almost half a century later, Knight and Yueh (2004), in their empirical study of interorganizational mobility in urban China, distinguished several subgroups of determinants of interorganizational employee mobility: personal and household characteristics, occupation, employer ownership, business relation- ships, and job search type. In their research in the higher education sector, Fernández-Zubieta et al. (2016) distinguished between mobility determinants related to the probability of receiving a job offer and those related to the probability of accepting it. Nowadays, however, scholars have found that the whole phenomenon of interorganizational employee mobility cannot be adequately studied or understood without taking into account the whole context that exists at different levels, as they call it, which means that interorganizational employee mobility should be considered as a multilevel phenomenon (Tzabbar & Cirillo, 2020). Such a multilevel classication was previously known mainly from the literature on employee turnover, where different factors inuencing an employee’s decision to leave their current job are divided into economic or environmental, organizational or work-related, and individual levels (Mobley et al., 1979; Moynihan & Landuyt, 2008; Muchinsky & Morrow, 1980; Selden & Moynihan, 2000). However, since an employee’s decision to move from one organization to another differs from and goes beyond mere turnover behavior, it has been necessary to explore the entire context of the target phenomenon of interorganizational employee mobility and classify its determinants ECONOMIC AND BUSINESS REVIEW 2025;27:50–79 57 Table 3. Economic determinants of interorganizational employee mobility. Broader economic determinant Direction of the relationship Sector-specic determinant Direction of the relationship Industry/sector Labor market characteristics (job opportunities and unemployment rate) Mixed (job opportunities: +; unemployment rate: ) Structural characteristics (private vs. public sector) Mixed (private: +; public: ) Healthcare, high technology, IT consulting, manufacturing, nontradable Business activity or economic cycles (prosperity vs. recession) Mixed (prosperity: +; recession: ) Competitive and rapidly changing environment Positive Airline, healthcare, IT, semiconductors, theater, TV Gig-economy characteristics Negative Sector-specic conditions and requirements for employment and career advancement Positive Higher education, media production, theater Government policies and public services (educational and social welfare systems) Mixed Strategic alliances Positive Hard disk drive, telecommunications equipment Type of market economy (liberal vs. coordinated) Mixed (liberal: +; coordinated: ) Note. “Mixed,” “negative,” and “positive” represent the direction of the relationship between the economic-level determinant and interorganizational employee mobility. according to the same proposed levels—that is, economic, organizational, and individual levels, also known as macro, meso, and micro levels. Although there are many partial ndings about the different determinants at these levels, recent authors state that interorganizational employee mobility is still a major challenge for knowledge management, especially at the organizational and individual levels (Shujahat et al., 2020). This means that a comprehensive overview of mobility determinants and the proposed multilevel typology is still missing (Tzabbar & Cirillo, 2020). Therefore, the following subsections show the results of our systematic literature review, that is, the development of the multilevel typology of determinants of interorganizational employee mobil- ity, starting with the economic and followed by the organizational and individual mobility determinants. 3.1 Economic determinants of interorganizational employee mobility Economic level in the broader sense usually refers to the state of an economy (March & Simon, 1958), whereas in the narrower sense it refers to specics of economic sectors. Of all the papers we included in our analysis (158), 38 papers (24%) mentioned at least one mobility determinant at this level. For a brief summary of all these economic-level determinants of mobility, see Table 3. In a broader sense, authors mostly examine labor market characteristics (Doering & Rhodes, 1996; Ituma & Simpson, 2009; Mano-Negrin & Kirschenbaum, 2000; Sacchi et al., 2016), which are usually related to another determinant, that is, the level of business activity or economic cycles (Depew et al., 2017; Greenhalgh & Mavrotas, 1996; Huang et al., 2006; Ituma & Simpson, 2009; Kattenbach et al., 2014). When business activities strengthen in an economy, the movement of individuals between organizations increases. On the other hand, in times of recession, characterized by uncertainty, insecurity, high unemployment (Ituma & Simpson, 2009), and fewer opportunities in the labor market (Depew et al., 2017; Knight & Yueh, 2004), employees tend to stay in their jobs (Huang et al., 2006). Kost et al. (2020) go a step further by introducing the framework of a gig economy, or a labor marker char- acterized by short-term contracts and freelance work as opposed to permanent jobs, noting that many of the workers who depend on platform-mediated work are increasingly less mobile because the nature and opportunities of these platforms dictate the frequency, rewards, and context of their gig work. Some authors focus more on how differences between labor markets are based on government policies and public services (e.g., education and social welfare; Casper, 2007; Ceccagno, 2015; Ituma & Simpson, 2009; Kalleberg & Mastekaasa, 2001; Korpi & Mertens, 2003; Ugarte, 2017), which is usually related to another economic-level mobility determinant, which is the type of market economy to which the institutional system belongs—that is, liberal vs. coordinated market economy (Bos & Vannoorenberghe, 2018; DiVito, 2012; Ivanˇ ciˇ c, 2000; 58 ECONOMIC AND BUSINESS REVIEW 2025;27:50–79 Kattenbach et al., 2014; Mueller & Schweri, 2015; Storz et al., 2015; Ugarte, 2017). In this regard, in contrast to the coordinated market economy, the liberal market economy offers lower employment protection, lower wage bargaining, and less investment in rm-specic knowledge (i.e., higher investment in general skills with low specicity that can be used in different rms in the same or different industries), leading to higher labor market exibility and employee mobility between rms (DiVito, 2012). Storz et al. (2015) illustrate these differences by comparing the U.S. and Japanese labor markets in the video game industry, with the latter having stricter safety laws that limit organizations’ ability to hire and re employees and consequently lead to less employee mobility between organizations. These differences also stem from the different education and social security systems in these countries: while in the United States the focus is on general skills that are transferable in an open labor market, Japan emphasizes training within companies and the responsibility of companies to provide social security for their employees (Storz et al., 2015). Ugarte (2017) offers insight into the Argentine labor market, which is characterized by insecurity, a weaker social welfare system (maternity and childcare policies), and strong union bargaining, which strongly ties employees to their organizations and reduces their interorganizational mobility. On the other hand, the Swiss labor market is lightly regulated and therefore offers few collective labor agreements and little employment protection, leading to higher interorga- nizational mobility of employees (Mueller & Schweri, 2015). An interesting case is that of Norway, which is considered a coordinated market economy and yet offers high unemployment compensations, which encourages employees to change jobs more frequently as they are also protected in case of failure and possi- ble future dismissal (Kalleberg & Mastekaasa, 2001). Economic determinants in the narrower sense tend to be sector-specic in the form of specic norms and incentives (Balland et al., 2015). This usually implies differences between the private and public sectors (Donnelly, 2009; Greenhalgh & Mavrotas, 1996; Kalleberg & Mastekaasa, 1998; Knight & Yueh, 2004; Mano-Negrin & Kirschenbaum, 2000; Sacchi et al., 2016; Yamaguchi, 1992). For example, workers in healthcare, high technology, IT consulting, manufacturing, and nontradable industries who work in the private sector tend to change employers more than workers who work in the public sector because the latter offers more protected jobs and better retirement benets (Donnelly, 2009; Greenhalgh & Mavrotas, 1996; Mano-Negrin & Kirschenbaum, 2000; Sacchi et al., 2016). Yamaguchi (1992), however, nds somewhat different results, noting that interorganizational mobility in Japan is lower in the private sector due to the higher salaries offered, especially early in the career, while Knight and Yueh (2004) nd that urban residents in China generally prefer to work in the public sector, whereas rural residents tend to prefer the private sector. In addition, employee mobility rates differ signicantly due to the unique characteristics of some sectors. The semiconductor, TV , and airline industries, for example, are characterized by highly competitive environments, so courting by competing rms or so-called war for talent is quite common in these sectors (Almeida & Kogut, 1999; Amankwah- Amoaha & Debrahb, 2011; Bidwell & Briscoe, 2010; Haunschild, 2003; Mascia & Piconi, 2013; Sørensen, 1999). In the healthcare sector, which is characterized by a rapidly changing environment, the mobility of chief executive ofcers (CEOs) has increased as the policies of the sector push managers toward careers in which they gain experience in different organizations. This is to help them develop a broad and diversied set of skills, knowledge, and abilities and have the oppor- tunity to build interorganizational networks that are benecial to the sector as a whole (Mascia & Piconi, 2013). A similar situation can be seen in the IT sector, where the constant demand for skill development and change drives people to move to organizations that provide what they need (Bidwell & Briscoe, 2010). Due to the fastest growth rates that provide the most employment opportunities, the same IT sector also attracts many employees from other sectors (Ivanˇ ciˇ c, 2000; Sgobbi & Suleman, 2015), so there is a positive impact not only on interorganizational but also on intersector employee mobility (i.e., employees moving between organizations from different sectors). Some other sectors are characterized by specic conditions and requirements for employment and career advancement. For example, because of the common xed-term contracts and standardized basic qualications of actors, interorganizational mobility is common in the theater industry, and actors’ careers are seen as typically boundaryless (Haunschild, 2003). In the media production industry, the employment structure is entirely shaped by its specic hiring requirements, which stipulate hiring only employees with whom organizations have worked before (Baumann, 2002). The higher education sector, on the other hand, is characterized by a limited number of positions and high academic requirements such as scholarly publications, postdoctoral experience, and the time required for career advancement (Smith- Doerr, 2005). Such sector-specic circumstances lead to employee mobility outside the academic ECONOMIC AND BUSINESS REVIEW 2025;27:50–79 59 Table 4. Organizational determinants of interorganizational employee mobility. Organizational determinant Direction of the relationship Industry/sector Interorganizational network/market ties Mixed Advertising (negative), agriculture (positive), biotechnology (positive), footwear (positive), higher education (positive), IT (positive), micro- & nanotechnology (positive), services (positive), TV (positive), video games (positive) Organizational status Negative Advertising, biotechnology, high technology, higher education, TV Opportunities for new learning and research Negative Higher education, theater Opportunities for on-the-job training Negative Engineering, public service Opportunities for promotion Mixed Construction (positive), public service (mixed) Organizational support Negative Engineering, fashion & textiles, higher education, IT, micro- & nanotechnology, public service Income/salary Negative Banking, construction, fashion & textiles, IT, legal services, public service Organizational size Mixed Agriculture (negative), banking (negative), construction (negative), distribution (negative), engineering (negative), fashion & textiles (negative), food (negative), high technology (negative), insurance (negative), IT (mixed), manufacturing (negative), nontradable (negative), petroleum & petrochemicals (negative), publishing (negative), road transport (negative), services (negative) Organizational age Mixed Not specied Organizational location Mixed (rural: +; urban: ) Banking Type of employment Mixed (part-time: +; full-time: ) High technology, manufacturing, nontradable Note. “Mixed,” “negative,” and “positive” represent the direction of the relationship between the determinant of the source organization and interorganizational employee mobility. sector to other organizations, which is another example of the aforementioned intersector employee mobility. Finally, technology-intensive industries are known for their strategic alliances (e.g., R&D alliances) that exist between organizations and encourage employees to move from one organization to another within the same alliance (Hoetker & Agarwal, 2007; Sampson, 2007). 3.2 Organizational determinants of interorganizational employee mobility The organizational level captures the internal environment of organizations as well as the connections and relationships they establish with external parties such as customers, suppliers, competitors, and so forth. Of all the papers included in our analysis, 52 papers (33%) identify at least one determinant of employee mobility at this level. Table 4 summarizes all these determinants of mobility at the organizational level. The interorganizational network or market ties are the most common of these determinants (Amarante et al., 2019; Andersson & Thulin, 2013; Balland et al., 2015; Broschak, 2004; Casper, 2007, 2013b; Culié et al., 2014; Donnelly, 2009; Eiriz, 2020; Fernández-Zubieta et al., 2016; Lahdelma, 2022; López et al., 2020; Kuusk, 2021; Petruzzelli et al., 2010; Piezunka & Grohsjean, 2022; Sørensen, 1999; Wang, 2015; Whittington et al., 2009), and their importance is particularly relevant for knowledge-intensive industries. The closer the ties between organizations, especially within a region or cluster to which they belong, the greater their positive effect on interorganizational mobility (Culié et al., 2014; Donnelly, 2009; Eiriz, 2020; López et al., 2020; Sørensen, 1999). Geographic proximity and spatial density (i.e., the number of organizations per square kilometer) also play an important role in moderating the relationship between the interorganizational network/ties and employee mobility (Amarante et al., 2019; Andersson & Thulin, 2013; Balland et al., 2015; Lahdelma, 2022; Wang, 2015). Thus, when organizations’ geographic locations are closer together, they form a common active area, which strengthens the shared interorganizational network and consequently positively inuences employee mobility (Wang, 2015; Whittington et al., 2009). The same is true for so-called organizational proximity or strategic and capability similarities between 60 ECONOMIC AND BUSINESS REVIEW 2025;27:50–79 organizations (Balland et al., 2015; Wang, 2015). These similarities also have a positive effect on the creation of an interorganizational cooperation network and consequently on employee mobility, as people are more likely to move between rms that are already cooperating (Kuusk, 2021). On the other hand, greater differences between source and destination organizations increase the skills-adjustment costs, which discourages employees from moving to another organization and reduces their mobility (Fernández-Zubieta et al., 2016). Moreover, due to the close relationship that consulting rms have with their clients, it is quite common for a consultant to move from their source organization to a client organization (Donnelly, 2009). This has a positive effect not only on interorganizational mobility, but also on intersector mobility (i.e., moving from the consulting industry to the, for example, IT sector). In the video game industry, many organizations share partners with their competitors within the same net- work, which allows their own employees to establish connections with and potentially move to these com- peting organizations (Piezunka & Grohsjean, 2022). However, there are also cases that demonstrate a negative relationship between the interorganizational network and employee mobility (i.e., the weaker the network between organizations, the more likely it is that employees will move from one organization to another). For example, in the advertising industry, an exchange manager is more likely to leave their source organization if the organization itself is unable to maintain rm market relationships with others (e.g., customers, services, etc.; Broschak, 2004). Simply put, in such a case, the decision to leave is positively inuenced by the lack of good market relationships rather than their abundance. Thus, it is fair to say that this interorganizational network determinant can promote interorganizational employee mobility, but it can also prevent it. An organization’s status or reputation is another important organizational-level mobility determinant (Kiefer et al., 2022), especially in advertising (Broschak et al., 2020), biotechnology (Casper, 2007, 2013b), higher education (D’Aveni, 1996; Fernández-Zubieta et al., 2016; Loy & Sage, 1978), and TV industry (Sørensen, 1999). Biotechnology startups measure their status by the number of so-called star scientists who decide to take a risk and move there (Casper, 2013a, 2013b). Other high-technology organizations measure it by the production of new technologies and investment in research and devel- opment (R&D; Greenhalgh & Mavrotas, 1996), as well as by the success of a region or cluster to which the organization belongs (Casper, 2007). The latter also applies to commercial broadcasters, whose position in the interorganizational network increases their organizational rank (Sørensen, 1999). In the higher education sector, organizational status is usually measured by the organization’s performance in four areas: teaching, research, knowledge transfer, and international outlook (Times Higher Education, 2022). Thus, academic employees will move to an institution with higher status because of direct benets, such as more time for research and increased funding, but also because of the positive externalities associated with these positions, which can contribute to their individual standing (Fernández-Zubieta et al., 2016). Various opportunities that a source organization can provide, such as developing additional skills and im- proving competencies (Haunschild, 2003), new learning and research (Arthur et al., 2005; Fernández-Zubieta et al., 2016; Smith-Doerr, 2005), on-the-job training (Harper, 1995; Rahman, 2012; Wynen et al., 2013), and promotions (Campbell et al., 2017; Doering & Rhodes, 1996; Huang et al., 2006; Kost et al., 2020; Lee, 2018; Wynen et al., 2013) tend to have a negative effect on individuals’ decisions to move beyond the bound- aries of their source organization. However, Huang et al.’s (2006) study reached the opposite conclusion when it examined only promotion. It found that em- ployees who are promoted faster than their colleagues are more likely to move to another organization be- cause such promotion speed is evidence of the quality of their competencies and skills, which make them more attractive in the labor market. A study in the U.S. public service found mixed results with the same determinant. Performance-based promotions were found to have a negative effect on mobility within the same sector but a positive effect on mobility outside the public service. Thus, when a promotion is based on performance, employees are more likely to stay in their current organization and less likely to accept an- other job in the public service. However, if they decide to move, they are more likely to go to an organization outside that sector (i.e., intersector mobility). One ex- planation could be that employees who are promoted frequently also have greater skills and capacity and are therefore more likely to nd a job outside their sector (Wynen et al., 2013). Several authors mention organizational support as a factor that may inuence an individual’s decision to stay in or leave the source organization (Ali et al., 2018; Arthur et al., 2005; Ceccagno, 2015; Culié et al., 2014; Doering & Rhodes, 1996; Fernández-Zubieta et al., 2016; Gambardella et al., 2009; James, 2014; Rahman, 2012; Wynen et al., 2013). Culié et al. (2014) dene it as the nancial and human resources needed and the importance the organization places on the individual’s work, while Arthur et al. (2005) see it as support from supervisors or afrmation from other ECONOMIC AND BUSINESS REVIEW 2025;27:50–79 61 respected individuals. Several other authors see or- ganizational support in the context of a collective bargaining union (Ali et al., 2018), as well as in the autonomy granted and the opportunity to partici- pate in decision-making processes and performance appraisals that directly affect one’s career (Doering & Rhodes, 1996; Gambardella et al., 2009; Rahman, 2012). Finally, some authors place organizational sup- port in the context of work–life balance (Ceccagno, 2015; James, 2014; Wynen et al., 2013), which typically includes exible work arrangements, reduced work hours, personal leave, and practical help with child- care (James, 2014). Income level also emerges as an important mobil- ity determinant at the organizational level (Doering & Rhodes, 1996; Gruetter & Lalive, 2009; Harper, 1995; Kacperczyk & Balachandran, 2018), especially in industries such as banking (Sgobbi & Suleman, 2015; Ugarte, 2017), construction (Huang et al., 2006), fashion and textiles (Ceccagno, 2015), legal services (Campbell et al., 2012), IT (Depew et al., 2017), and public services (Ali et al., 2018; Lee, 2018). It is neg- atively related to interorganizational mobility, that is, employees with higher income/salary are less likely to leave their source organizations (Gruetter & Lalive, 2009). Kacperczyk and Balachandran (2018) have given even more attention to the relationship between income and mobility by including the in- come dispersion which occurs both between levels (vertical dispersion) and within the same level (hor- izontal dispersion) in an organization. While vertical income dispersion (i.e., the differences between the incomes of employees at different hierarchical levels) has a negative effect on interorganizational mobility, horizontal dispersion (i.e., the differences between the incomes of employees at the same hierarchical level) has the opposite effect. This result is also conrmed by Ali et al. (2018), who claim that mail employees are more likely to switch employers if they believe they are paid less than their colleagues. The same was shown in the study by Sgobbi and Suleman (2015), who found that employees in retail banking who be- lieved they were paid less than their colleagues were more likely to switch to a competitor (intra-sector mobility) that might value them better or, even more radically, to an organization from a completely differ- ent sector (intersector mobility). Organizational size, age, and location are also im- portant determinants of employee mobility in most industries/sectors (Amarante et al., 2019; Balachan- dran & Wezel, 2020; Bidwell & Briscoe, 2010; Czaller et al., 2021; Dobrev, 2012; Doering & Rhodes, 1996; Greenhalgh & Mavrotas, 1996; Leggatt, 1979; Sgobbi & Suleman, 2015; Yamaguchi, 1992). The larger a rm is in construction, distribution, banking and insurance, agriculture, retail, and services, the more likely an employee is to stay in the rm. Interestingly, Bidwell and Briscoe (2010) reach a slightly different conclusion in IT. They claim that early in their careers, employees prefer to work in larger organizations be- cause these organizations provide more opportunities to develop their skills, but later, as they gain more experience, their propensity to leave these large or- ganizations increases. Dobrev (2012), on the other hand, focused more on organizational structure and concluded that the inexibility and slowness of bu- reaucratic structures, as opposed to at structures, have a positive effect on interorganizational em- ployee mobility. He also nds that organizational age slightly weakens the relationship between organiza- tional size and employee mobility, in the sense that the likelihood of a manager leaving is higher in a larger organization founded a century ago than in an organi- zation that is ten times smaller but ve times younger (Dobrev, 2012). The recent study by Czaller et al. (2021) shows that the mobility of the Swedish labor force is biased toward larger cities and metropolitan areas rather than some rural areas. Similar ndings are conrmed in the study by Sgobbi and Suleman (2015) for the Portuguese banking sector. They nd that the metropolitan area of a large city hinders intra- sector mobility but drives intersector mobility (i.e., switching outside the banking sector). However, both intra- and intersector mobility is much higher in sub- urban areas because they are more heterogeneous and thus make different organizations more visible, which helps employees to pursue alternative employment opportunities and consequently to leave their current employer (Doering & Rhodes, 1996). Different types of employment are also determinants of mobility worth mentioning at this level of analysis. While full-time employees are less likely to leave their employer, part-time employees will seek alternative opportunities (Greenhalgh & Mavrotas, 1996). There are also various agreements such as noncompete agreements and the inevitable disclosure doctrine that are commonly used to protect organizations’ intellectual property, particularly in the area of research and development (Campbell et al., 2017; Seo & Somaya, 2021). Because they typically limit employees’ alternative employment options, they have a direct negative effect on interorganizational employee mobility. 3.3 Individual determinants of interorganizational employee mobility The determinants of employee mobility at the indi- vidual level refer to various personal and professional factors that inuence employees’ decisions to move 62 ECONOMIC AND BUSINESS REVIEW 2025;27:50–79 to another organization. 68 of the papers from our sample (43%) address at least one determinant at this level. All these individual-level determinants can be found in Table 5. The most interesting among them, and the most frequently studied in different sectors and industries, are social ties (Balland et al., 2015; Baumann, 2002; Broschak, 2004; Broschak et al., 2020; Campbell et al., 2017; Carnahan et al., 2020; Casper, 2007, 2013a, 2013b; Collet & Hedström, 2013; Culié et al., 2014; Di Lorenzo & Almeida, 2017; Dokko & Rosenkopf, 2010; Donnelly, 2009; Haunschild, 2003; Ituma & Simpson, 2009; Knight & Yueh, 2004; Perkmann & Walsh, 2007; Randel & Ranft, 2007; Rider, 2012; Seo & Somaya, 2021; Smith-Doerr, 2005; Waters & Smith, 2008; Whittington et al., 2009). They are dened as relationships between people based on their social exchanges (Ituma & Simpson, 2009). Some authors refer to them as a close-knit network in which employees operate (Donnelly, 2009) or as ties that exist between individuals as a result of their mutual cooperation (Broekel et al., 2014). Waters and Smith (2008) explain these ties as so-called bonding social capital, which implies the strength of the relationships that an individual shares with others in an organization. Very often, an employee’s tacit knowledge is created by and embedded in these rela- tionships, making it more difcult to transfer to other organizations, ultimately reducing the likelihood of switching to another company (Campbell et al., 2017). When social ties arise from professional cooperation and business relationships outside organizational boundaries, they have a different effect on employees’ interorganizational mobility, generally increasing it (Haunschild, 2003; Knight & Yueh, 2004). Waters and Smith (2008) refer to this as bridging social capital, which is more effective in acquiring information and more important for individual improvement (e.g., position, knowledge, skills, etc.). In other words, people cultivate their connections with others initially to secure their current job position, but later new opportunities for career advancement arise outside organizational boundaries, leading to interorganizational movements (Randel & Ranft, 2007). In addition, social ties that exist between people from different organizations are the basis for interorganizational information exchange and potential cooperation (Collet & Hedström, 2013; Randel & Ranft, 2007). When former colleagues stay in touch, they create a basis for cooperation between their current employers (Hjertvikrem & Fitjar, 2020), which consequently form an interorganizational network. This interorganizational network enables new connections between their employees and contributes to building and improving their social ties. In other words, social ties as an individual-level determinant of mobility and the interorganizational network as an organizational-level determinant of mobility are rmly connected and often cannot be examined separately, which gives us a valid reason to study them in more detail. Employees who are well connected to others in a va- riety of organizations are likely to have better insight into job opportunities elsewhere and are better able to identify and evaluate t with other organizations and assess the costs and benets of mobility (Di Lorenzo & Almeida, 2017). On the other hand, employers them- selves prefer to hire individuals who have maintained strong interpersonal connections at their previous employer (Rider, 2012), as these connections often indicate that the employee was involved in a closer working relationship that gave them greater access to organizational knowledge, making them more at- tractive in the labor market, especially to competing rms (Seo & Somaya, 2021). However, Carnahan et al. (2020) found that employees of competing rms in the legal services sector whose managers have social ties based on their acquaintance (not necessarily their co- operation) are less likely to move from one rm to an- other. In other words, social ties shared by executives of two competing legal rms have a negative effect on their employees’ switching from one rm to the other. Another interesting aspect of social ties is that they affect not only the interorganizational employee mo- bility but also the intersector mobility. For example, engineers in the mechanical, biotechnology, pharma- ceutical, and other chemical industries are encour- aged to move between industry and academia by the strong social ties these engineers share with the scien- tic community (i.e., universities; Perkmann & Walsh, 2007; Smith-Doerr, 2005). This is particularly the case when engineers and scientists belong to the same regional economy or cluster (Casper, 2007, 2013a, 2013b), demonstrating once again the importance of the moderating role of geographic proximity. More- over, these intersector movements form a complete interorganizational social network between industry and public research institutions (e.g., universities; Whittington et al., 2009), again conrming the close link between social ties and interorganizational net- works (Casper, 2013a, 2013b). According to Broschak (2004), personal relation- ships between two managers of client and service organizations are the inevitable basis for a solid interorganizational relationship in the advertising industry. These ties create mutual trust between managers, lead to informal commitments, accelerate their communication, and ultimately anchor them in their current work position. As a result, managers are less likely to move to another organization because ECONOMIC AND BUSINESS REVIEW 2025;27:50–79 63 Table 5. Individual determinants of interorganizational employee mobility. Individual determinant Direction of the relationship Industry/sector Social ties Mixed Advertising (mixed), biotechnology (positive), engineering (positive), nance (positive), high technology (mixed), IT (mixed), legal services (positive), media production (positive), micro- & nanotechnology (positive), pharmaceuticals (positive), theater (positive), wireless telecommunications (positive) Age Negative Agriculture, banking, construction, consulting, distribution, electronics, engineering, fashion & textiles, nance, food, healthcare, high technology, higher education, insurance, manufacturing, nontradable, petroleum & petrochemicals, publishing, public service, road transport, telecommunications Gender Mixed Agriculture (female [F]: ; male [M]: +), banking (F: ; M: +), construction (F: ; M: +), healthcare (F: +; M: ), higher education (F: +; M: ), IT (F: ; M: +), manufacturing (F: +; M: ), public service (mixed) Education Mixed Agriculture (negative), banking (positive), high technology (positive), IT (positive), nontradable (positive), manufacturing (positive), public service (positive) Family-related factors: marital status, children, household characteristics (share of household income) Negative Banking, construction, energy, healthcare, higher education, manufacturing, pharmaceuticals, public service, services, telecommunications Length of service Mixed Agriculture (negative), banking (negative), healthcare (mixed), public service (mixed) Organization-specic skills Negative Banking, construction, consulting, distribution, engineering, fashion & textiles, food, insurance, IT, manufacturing, petroleum & petrochemicals, publishing, pharmaceuticals, road transport, telecommunications Work experience Mixed High technology (positive), public service (negative) Position in hierarchy Mixed Banking (positive), higher education (negative), legal services (negative), public service (negative) Individual skills and competences Mixed Consulting (negative), distribution (negative), electronics (negative), fashion & textiles (negative), nance (negative), pharmaceuticals (mixed), telecommunications (negative) Mobility experience Positive IT, healthcare Current job satisfaction Negative IT, micro- & nanotechnology, public service Expectations to nd a better job Positive Higher education Orientation to professional success Mixed Healthcare (positive), higher education (positive), manufacturing (positive), public service (positive), theater (positive), not specied (negative) Personal characteristics Mixed (self-initiative, risk taking, “window dressing,” active, investigative, enterprising, artistic, impulsive, curious personalities: +; committing, conventional, agreeable, anxious, self-conscious personalities: ) Banking, biotechnology, nance, micro- & nanotechnology, public service Mobility costs Negative Higher education Note. “Mixed,” “negative,” and “positive” represent the direction of the relationship between the individual-level determinant and the interorganizational mobility. 64 ECONOMIC AND BUSINESS REVIEW 2025;27:50–79 they are fully committed to the customers of their source organization. That is an example of a negative relationship between social ties and employee mobility. However, if a client organization decides to use services from a new provider, it is likely that an employee of the service organization will follow their ties and move to the client company’s new provider, which is dened in the literature as circulation of social ties (Broschak et al., 2020). Using the same principle (client–service organizations) in wireless telecommunications, Dokko and Rosenkopf (2010) conrm that the social ties between service and client managers are a major reason why they easily decide to move to another organization. They simply assume that their social ties will remain and their cooperation will continue regardless of the destination organization. In addition to social ties, demographic factors such as age, gender, and education are studied the most commonly (Ali et al., 2018; Amarante et al., 2019; Balachandran & Wezel, 2020; Buchinsky et al., 2010; Doering & Rhodes, 1996; Dries & Pepermans, 2008; Farber, 1994; Fernández-Zubieta et al., 2016; Greenhalgh & Mavrotas, 1996; Harper, 1995; Huang et al., 2006; Ituma & Simpson, 2009; Ivanˇ ciˇ c, 2000; Javdani, 2020; Kattenbach et al., 2014; Kidd, 1991; Knight & Yueh, 2004; Leggatt, 1979; Mano-Negrin & Kirschenbaum, 2000; Sacchi et al., 2016; Sgobbi & Suleman, 2015; Stijepic, 2017; Ugarte, 2017; Valcour & Ladge, 2008; Valcour & Tolbert, 2003; Wynen et al., 2013). While age has been shown to have a negative relationship with employee mobility, gender and education show mixed results. Doering and Rhodes (1996) and Valcour and Tolbert (2003) nd that women are more likely to move from one organization to another, consistent with theories of “feminine” personality traits that make women more likely than men to pursue boundaryless careers (Ituma & Simpson, 2009). The same nding is conrmed by Valcour and Ladge (2008) on their sample from manufacturing, healthcare, and higher education. However, men are more likely than women to engage in interorganizational mobility in agriculture (Amarante et al., 2019), construction (Huang et al., 2006), public service (Ali et al., 2018; Wynen et al., 2013), and retail banking (Sgobbi & Suleman, 2015). As Wynen et al. (2013) explain, using the example of the public service, women prefer to work there because the pay gap between women and men is smaller, signicant progress has been made in gender representation, and the opportunity to balance work and family is greater than in the private sector. Ituma and Simpson (2009) found the same results in the ICT sector in Nigeria. However, these could be better explained by the predominantly male society in Nigeria and its traditional gender differences, which dene women primarily in terms of childcare, while giving more priority to men’s careers (Ituma & Simpson, 2009). Most authors have been able to demonstrate a positive relationship between education and mobility, implying that better educated employees are more likely to change employers (Ali et al., 2018; Doering & Rhodes, 1996; Greenhalgh & Mavrotas, 1996; Ituma & Simpson, 2009; Ivanˇ ciˇ c, 2000; Knight & Yueh, 2004; Sacchi et al., 2016; Sgobbi & Suleman, 2015; Stijepic, 2017). However, Buchinsky et al. (2010) come to the opposite conclusion, as their study proves that highly educated individuals are more likely to have a job that suits them better, which means that they are less likely to switch. Family-related factors such as marital status, number of children, and household income share have been shown to be important determinants of employees’ interorganizational mobility (Ali et al., 2018; Eby, 2001; Fernández-Zubieta et al., 2016; Huang et al., 2006; Kidd, 1991; Knight & Yueh, 2004; Mano-Negrin & Kirschenbaum, 2000; Valcour & Ladge, 2008; Valcour & Tolbert, 2003). Those who are married, have children, and earn a larger share of total household income focus more on their family life than on their career advancement, which negatively affects their decision to change current employers (Ali et al., 2018). However, these differences are more likely to apply to women than men, as women are more constrained by family-related factors and therefore seek stable employment rather than changing employers (Mano-Negrin & Kirschenbaum, 2000; Valcour & Tolbert, 2003). There are also special situations in which married individuals are more mobile because one spouse obtains a new job in a new location and their spouse moves with them as well as moves from the source organization to a new destination organization (Eby, 2001). Different employment characteristics also proved to play an important role when deciding about changing the employer (Ali et al., 2018; Amarante et al., 2019; Balachandran & Wezel, 2020; Buchinsky et al., 2010; Campbell et al., 2012, 2017; Dobrev & Merluzzi, 2018; Doering & Rhodes, 1996; Farber, 1994; Fernández- Zubieta et al., 2016; Ganco et al., 2020; Harper, 1995; Huang et al., 2006; Kidd, 1991; Knight & Yueh, 2004; Kost et al., 2020; Leggatt, 1979; Mano-Negrin & Kirschenbaum, 2000; Mueller & Schweri, 2015; Sacchi et al., 2016; Sammarra et al., 2013; Sgobbi & Suleman, 2015; Vilalta-Bu, 2010; Wynen et al., 2013; Yamaguchi, 1992). One of the most important proved to be the length of service in an organization. Dobrev and Merluzzi (2018) nd that the chances of leaving the current employer decrease after ve years on the job. This is quite logical because these ECONOMIC AND BUSINESS REVIEW 2025;27:50–79 65 employees develop organization-specic skills (i.e., knowledge about people, organizational processes, organizational culture, organizational history, business and product specics, etc.) that are not transferable across organizations (Sammarra et al., 2013). Thus, a higher value of organization-specic skills reduces the opportunity for workers to gain economic benets from their accumulated knowledge and skills by changing employers (Doering & Rhodes, 1996; Ganco et al., 2020; Huang et al., 2006; Leggatt, 1979). Employees also feel they have found a “good match” with their employer (Mueller & Schweri, 2015), which ultimately reduces the likelihood that they will leave (Sammarra et al., 2013). This is also suggested in part by research in the public services (Wynen et al., 2013), which states that the longer someone stays in an organization within the same sector, the less likely they are to move to another organization within that sector. However, the same determinant (i.e., length of service within the organization) has a positive effect on the decision to move to a completely different sector (intersector mobility). It is also important to differentiate organization-specic from occupation-specic skills, as the latter are less organization-oriented and easily transferred to other organizations usually within the same sector, thus enabling easier intra-sector mobility (Ivanˇ ciˇ c, 2000; Sammarra et al., 2013). In addition to the length of service in a particular organization, general work experience is also crucial for individual mobility decisions. Buchinsky et al. (2010) nd that more experienced employees tend to move less, which is usually mitigated by their age and length of service in an organization. However, other authors note that experienced employees, especially in high-tech industries, tend to be more mobile because of their accumulated knowledge, well-developed skills, and established connections (Sacchi et al., 2016; Vilalta-Bu, 2010). Usually, the better their position in the organizational hierarchy (e.g., managing partner), the less likely employees are to leave (Doering & Rhodes, 1996; Donnelly, 2009; Kidd, 1991; Yamaguchi, 1992). If they eventually decide to do so, they are more likely to start something of their own than to move to an established organization (Campbell et al., 2012). Regardless of the organization-specic and occupation-specic skills, which are more technical in nature (i.e., expertise and the ability to use the tools and techniques of the specic discipline; Katz, 1974), the human skills (i.e., the ability to work effectively with others; Katz, 1974) and conceptual skills (i.e., the ability to see the organization as a whole; Katz, 1974), as well as other competencies that someone possesses, are something that differentiates them from others and thus affects their mobility decision differently (Ceccagno, 2015; Di Lorenzo & Almeida, 2017; Dries & Pepermans, 2008; Kiefer et al., 2022; Stijepic, 2017). Dries and Pepermans (2008) found that organizations invest more in employees they perceive to be future leaders or more talented than their peers, so these individuals are less likely to be mobile. Some other studies have shown that employees with specialized and unique skills are able to perform a broader range of tasks and adapt to new work demands, so they have a better chance of nding another job (Kost et al., 2020; Stijepic, 2017). On the other hand, those with generalized and repeatable skills will not be able to compete in a diversied labor market, so their mobility ambitions will be lower (Kost et al., 2020). In the pharmaceutical industry, if an inventor performs better than their peers, they can negotiate an appropriate salary and prot sharing, which increases motivation to stay with the organization (Di Lorenzo & Almeida, 2017). What is interesting here, however, is that even inventors who perform worse than the reference group are less likely to leave because they know they can benet from a familiar work environment with relatively higher-performing colleagues who provide them with opportunities to learn. They may also have access to the resources for innovative activities of more successful inventors (in their peer group), so the benets of moving to another company may be unclear. Therefore, the inventor may be willing to accept lower incentives and rewards from the organization (associated with lower relative performance) and still remain in the organization (Di Lorenzo & Almeida, 2017). Another determinant that tends to have a positive impact on employee mobility is the mobility experi- ence itself (Dobrev & Merluzzi, 2018; Farber, 1994; Ituma & Simpson, 2009; Mano-Negrin & Kirschen- baum, 2000). Those who have had experience with interorganizational mobility will be more willing to change employers again (Farber, 1994; Ituma & Simp- son, 2009). This is especially true for male workers (Mano-Negrin & Kirschenbaum, 2000). However, Do- brev and Merluzzi (2018) found that after the third job change, the probability of further changes de- creases. This is explained by the fact that multiple and frequent job changes lead to a perception of lack of commitment and focus, which is perceived as unde- sirable by employers, especially those who tend to employ individuals who have career continuity in- stead of too many job changes (Sacchi et al., 2016). Another mobility determinant mentioned at the individual level mostly relates to individual prefer- ences (Ainsworth et al., 2009; Ali et al., 2018; Culié et al., 2014; Doering & Rhodes, 1996; Fernández- Zubieta et al., 2016; Haunschild, 2003; Kalleberg & Mastekaasa, 2001; Lee, 2018; Mano-Negrin & 66 ECONOMIC AND BUSINESS REVIEW 2025;27:50–79 Kirschenbaum, 2000; Mascia & Piconi, 2013; Ruiz, 2014; Valcour & Ladge, 2008; Valcour & Tolbert, 2003). Satisfaction with the current job, an expectation to nd a better job, as well as individual orientation to professional success are common. Culié et al. (2014) dene cur- rent job satisfaction with meeting career advancement goals and goals for developing new skills, as well as satisfaction with the social context of one’s job. James (2014) focuses more on satisfaction with work–life balance. The better the work–life balance is, the less likely one is to move away (Ali et al., 2018; Doering & Rhodes, 1996; Lee, 2018). However, if someone ex- pects the new job to satisfy them even more or provide them with better resources for higher performance, they will not hesitate to move there (Fernández- Zubieta et al., 2016; Kalleberg & Mastekaasa, 2001; Ruiz, 2014). Some people seek different experiences simply because they want access to broader and more diverse labor markets (Alshahrani & Morley, 2015), and they see interorganizational mobility as an oppor- tunity for challenge and growth, so their departure does not necessarily mean they are tired of their cur- rent job (Ainsworth et al., 2009). Mano-Negrin and Kirschenbaum (2000) found that this is especially true for male employees as they seek to maximize their human capital returns and therefore tend to stay in one position until they nd something better. How- ever, Valcour and Tolbert’s Valcour and Tolbert (2003) study showed the opposite: men who prioritized their career advancement were more likely to stay with the same employer as interorganizational mobility depressed their earnings. There are also differences between female employees as there are women who are more inclined to build their careers and therefore more likely to move between organizations that offer them such an opportunity (Valcour & Ladge, 2008). On the other hand, there are women who are more oriented toward fullling their family responsibilities and therefore are more likely to seek sheltered jobs and settle for what they have. In theater and health- care, those who want to succeed also change environ- ments because routines and entrenched relationships are seen as limiting one’s creativity and skill develop- ment (Haunschild, 2003; Mascia & Piconi, 2013). Some studies examine personal characteristics as signicant in the decision to leave or stay in an organi- zation (Ahn & Ok, 2019; Ali et al., 2018; Alshahrani & Morley, 2015; Casper, 2007; Culié et al., 2014; Doering & Rhodes, 1996; Kalleberg & Mastekaasa, 2001; Wille et al., 2010). Wille et al. (2010) emphasize that employees who are ambitious, impulsive, curious, ex- perimental, imaginative, courageous, and in need of control are more inclined to interorganizational mo- bility. On the other hand, those who prefer to work in a familiar environment and with familiar tasks as well as those more committed (Kalleberg & Mastekaasa, 2001) will tend to stay where they are. There are also individuals who generally show a higher propensity to relocate and seek alternatives, even if that means trading their secure job for a new one (Casper, 2007; Doering & Rhodes, 1996). However, Culié et al. (2014) introduce the notion of “psychological mobility,” which describes individuals’ perceptions of their own ability to move. They nd that the more individuals feel they could move to another organization, the less likely they are to actually do so. In their study of the security analyst market, Ahn and Ok (2019) focused more on the behavior of those employees who chose to move to another organization. They found that these employees tend to engage in what they call “window dressing,” increasing the quantity of tasks they resolve even as they decrease their quality. Finally, the personal mobility costs associated with job search, bargaining, and switching, as well as the opportunity costs associated with leaving the original job (Campbell et al., 2017; Fernández-Zubieta et al., 2016), may negatively affect interorganizational mobility. 4 Multilevel effects of interorganizational employee mobility Tzabbar and Cirillo (2020) developed a life-cycle approach to employee mobility research. They note that in the early phases, research in this area focused primarily on developing and testing theories around the main phenomenon. In the growth phase, it focused on the overall context in which employee mobility occurs, as well as the period after the employee leaves the organization, examining how this affects the organization that loses the employee (i.e., the source organization) and the organization that hires the employee (i.e., the destination organization), or how an individual employee gains or loses through their own mobility decision (Ahn & Ok, 2019). In practice, most mobility research up to its mature phase, which Tzabbar and Cirillo refer to as the current stage, addressed various mobility effects. The data we used for our systematic literature review conrm this fact, with nearly 63% of the sample (99 of 158 papers) focusing on at least one of the mobility effects. However, the same mature phase of research on employee mobility in different disciplines such as economics, organizational behavior, human resource management, and sociology suggests that these mobility effects should be studied again, but this time more comprehensively and across the levels to which they belong (i.e., economic, organizational, and individual), given the growing awareness of ECONOMIC AND BUSINESS REVIEW 2025;27:50–79 67 Table 6. Multilevel effects of interorganizational employee mobility. Economic level Organizational level Individual level Effect Industry/sector Effect Industry/sector Effect Industry/sector Productivity and growth of economic system Biotechnology, nance, IT Transfer of knowledge Biotechnology, construction, cotton textiles, dairy, nance, hard disk drive, furniture manufacturing, high technology, IT, machinery & equipment, manufacturing & services, networks & databases, petroleum & petrochemicals, publishing & life sciences, semiconductors, telecommunications, R&D, wholesale Social ties Advertising, biotechnology, nance, IT, R&D, semiconductors, underwater Organizational productivity, performance, and competitive advantage Airline, agriculture, construction, nance, sheries & quarries, legal services, manufacturing & services, public service, R&D, theater, wholesale Professional success Banking, energy, higher education, manufacturing & services, pharmaceuticals, telecommunica- tions, theater Business innovations Biotechnology, engineering, high technology, pharmaceuticals, video games Replication of organizational routines and processes Accounting, advertising, food, healthcare, legal services, petroleum & petrochemicals, R&D Income/salary Banking, consulting, energy, IT, manufacturing & services, pharmaceuticals, telecommunications Interorganizational network/market ties Advertising, biotechnology, fashion & textile, high technology, higher education, IT, legal services, R&D, semiconductors, underwater, trade, TV , wireless telecommunications Organizational status Legal services Mobility experience Not specied interorganizational employee mobility as a multilevel phenomenon (Tzabbar & Cirillo, 2020). Thus, in our systematic literature review, we also briey classied the effects of interorganizational employee mobility at the economic, organizational, and individual levels and across different sectors and industries (Table 6). The effects of interorganizational employee mobility at the economic level have been studied mainly in the literature on regional learning and innovation and regional development policies, which emphasize the positive aspects of employee mobility for regional economic competitiveness (James, 2014). However, some other authors warn about its negative 68 ECONOMIC AND BUSINESS REVIEW 2025;27:50–79 effects and undesirability, especially when economic success depends on long-term labor relations (Korpi & Mertens, 2003). In our sample, only six papers mention some of the mobility effects that can be attributed to the economic level. They mostly focus on how interorganizational mobility of employees in knowledge-intensive sectors increases business activity and thus affects the productivity and growth of markets, industry clusters, and geographic regions or practically entire economies (Beaverstock, 2005; Campbell et al., 2017; Gianelle, 2014; Kuusk, 2021; Tóth & Lengyel, 2021; Whittington et al., 2009). Moreover, interorganizational mobility often implies international movements, which in turn create global business networks through new knowledge, organi- zational practices, and wealth for the organization and the international space (Beaverstock, 2005). Most of the papers from our sample primarily address the effects of mobility at the organizational level. Knowledge transfer is among the most frequently observed organizational-level mobility effects (Broekel et al., 2014; Collet & Hedström, 2013; Gambardella & Giarratana, 2010; Ganco et al., 2020; Helmsing, 2001; Liu et al., 2020; Kuusk, 2021; Williams, 2007) studied in various industries, such as biotechnology (Casper, 2007), construction (Parrotta & Pozzoli, 2012), cotton textiles (Saxonhouse, 1999), dairy (Tamásy et al., 2008), nance (Cici et al., 2021; Madsen et al., 2003), furniture manufacturing (Hoffmann et al., 2014), hard disk drives (Hoetker & Agarwal, 2007), high technology (Liu et al., 2010; Simonen & McCann, 2010; Vilalta-Bu, 2010; Waters & Smith, 2008), ICT (Ituma & Simpson, 2009; Tóth & Lengyel, 2021), machinery and equipment (Møen, 2005), manufacturing and services (Bos & Vannoorenberghe, 2018; Parrotta & Pozzoli, 2012; Seo & Somaya, 2021), networks and databases (Taylor, 2010), petroleum and petrochemicals (Isaksen & Karlsen, 2012), publishing and life sciences (Bugge & Thune, 2016), R&D (Cantner & Graf, 2006; Cantner et al., 2010; Corsino et al., 2019; Maliranta et al., 2009; Parrotta & Pozzoli, 2012), semiconductors (Almeida & Kogut, 1999; Corredoira & Rosenkopf, 2010; Goossen & Carnabuci, 2020; Rosenkopf & Almeida, 2003; Song et al., 2003), telecommunications (Samp- son, 2007), and wholesale trade (Parrotta & Pozzoli, 2012). Almeida and Kogut (1999) are most often cited for their research in the semiconductor industry. They suggest that the mobility of an employee, also known as a patent holder, spreads ideas, which is a driving force for local knowledge transfer. Some other studies have conrmed that these ndings hold for other knowledge-intensive industries as well (Rosenkopf & Almeida, 2003; Sampson, 2007), where most knowledge is tacit and embodied by highly skilled employees, so its transfer through their mobility is also known as learning-by-hiring (Parrotta & Pozzoli, 2012; Vilalta-Bu, 2010). Some authors have even extended the relationship between mobility and knowledge transfer by examining the nature of the source and destination organizations and the characteristics of the mobile employees. In their sample of R&D laboratories, Maliranta et al. (2009) found that destination organizations unrelated to R&D activities benet more from the employment of former R&D employees than R&D rms. Using a sample of the Portuguese manufacturing and services sectors, Martins (2011) has shown that only foreign organizations that hire employees of domestic organizations succeed in absorbing new knowledge because they can offer high salary increases and are thus able to hire the best workers. On the other hand, domestic organizations tend to hire “underperforming” workers from foreign organizations, which means that there is no greater source of knowledge spillover for them. Organizations may also lack the capacity to absorb external knowledge (Cici et al., 2021) or be reluctant to make novel changes in their established organizational patterns because such changes may be disruptive to their existing staff (Madsen et al., 2003). The educational background of mobile employees and the length of their tenure in the source organization also play an important role. Those with longer tenures were exposed to organizational knowledge for a longer period of time and thus contribute more to knowledge transfer between source and destination organizations (Marino et al., 2016). However, despite the direct relationship between interorganizational employee mobility and knowl- edge transfer, the latter also acts as a mediator or moderator in a relationship between interorganiza- tional employee mobility and other effects such as organizational productivity, performance, and competi- tive advantage (Amankwah-Amoaha & Debrahb, 2011; Brymer & Sirmon, 2018; Campbell et al., 2017; Cici et al., 2021; Haunschild, 2003; Maliranta et al., 2009; Mannix & Loewenstein, 1993, 1994; Marino et al., 2016; Martins, 2011; Parrotta & Pozzoli, 2012; Rocha et al., 2018; Seo & Somaya, 2021; Wang & Cao, 2022), as well as business innovations (Alnuaimi et al., 2012; Broekel et al., 2014; Perkmann & Walsh, 2007; Simo- nen & McCann, 2008; Storz et al., 2015; Whittington et al., 2009). While these effects are positive for the destination organizations, especially when mobility occurs between two sectors (i.e., intersector mobility) or between different regions/clusters, as these types of mobility prove to be one of the most important channels for fostering fresh and innovative ideas for ECONOMIC AND BUSINESS REVIEW 2025;27:50–79 69 existing and new products and processes (Alnuaimi et al., 2012; Isaksen & Karlsen, 2012; Simonen & Mc- Cann, 2008), employee mobility usually has negative effects on the source organizations. It puts them in the precarious position of losing their tacit knowl- edge and expertise (Amankwah-Amoaha & Debrahb, 2011; Shujahat et al., 2020), draining their resources (Mannix & Loewenstein, 1993, 1994), increasing the cost of hiring and training new employees (Brymer & Sirmon, 2018), and demoralizing the remaining or- ganizational members in an employee turnover work environment (Seo & Somaya, 2021). All of this subse- quently leads to lower performance and productivity, followed by the loss of competitive advantage of the source organizations (Amankwah-Amoaha & De- brahb, 2011; Cici et al., 2021; Seo & Somaya, 2021). This is particularly detrimental and occurs when key employees (dened as top decision makers) move to a competitor, where they not only transfer tacit knowledge but also replicate the advantageous routines and processes of their source organization (Bermiss & Murmann, 2015; Brymer & Sirmon, 2018; Campbell et al., 2012; Castellani et al., 2022; Grandinetti, 2022; Inkpen et al., 2019; Park & Belderbos, 2022; Shujahat et al., 2020; Wezel et al., 2006). Not surprisingly, the literature on strategic human capital views employee mobility as a threat and emphasizes the critical role of labor market constraints in limiting it (Campbell et al., 2017; Møen, 2005), especially in knowledge-intensive industries (Williams, 2007). However, when employees move between organizations that share a common knowledge base or common information pool (Gambardella & Giar- ratana, 2010), such mobility strengthens their market ties and positively affects the development of their interorganizational network (Somaya & Williamson, 2008; Somaya et al., 2008), which is another frequently studied effect of employee mobility (Bermiss & Greenbaum, 2016; Broschak, 2004; Broschak & Block, 2014; Carnahan & Somaya, 2013; Casper, 2007; Ceccagno, 2015; Collet & Hedström, 2013; Corredoira & Rosenkopf, 2010; Dokko & Rosenkopf, 2010; Fleming & Frenken, 2007; Hjertvikrem & Fitjar, 2020; Lahdelma, 2022; Liu et al., 2020; Mai et al., 2015; Rafee, 2017; Shimizu & Hirao, 2009; Sørensen, 1999; Sosnovskikh, 2021; Sun et al., 2022; Tóth & Lengyel, 2021; Waters & Smith, 2008). Some authors also refer to it as network capital (Sosnovskikh, 2021) or human capital ow network (Liu et al., 2020). Such a network often occurs in knowledge-intensive industries, espe- cially when employees move between organizations from different sectors (e.g., from higher education to a related industry—intersector mobility), which enables organizations to adopt different innovative resources from each other (Sun et al., 2022) and gives them the opportunity for collective learning (Helmsing, 2001). All these ndings provide evidence that the interor- ganizational network, which we have already noted as a determinant of mobility at the organizational level, also emerges as a mobility effect. The same is true for organizational status, which also acts as both a determinant and an effect of employee mobility. Employees are driven by the organizational status of their preferred destination organization, but when an organization hires an employee from a source or- ganization with higher status, such mobility usually improves the status/ranking of the destination orga- nization (Betancourt & Wezel, 2016). The formation of an interorganizational network as a result of employees moving between organiza- tions is based on social and professional connections between these mobile employees or with colleagues with whom they worked in the source organization but remained in contact after they left. Such net- work formation can therefore be applied to social ties, which also act as a mobility determinant and mobility effect, but at the individual level (Broschak et al., 2020; Cantner & Graf, 2006; Casper, 2007, 2013a; Castellani et al., 2022; Checkley & Steglich, 2007; Corredoira & Rosenkopf, 2010; Fleming & Frenken, 2007; Gianelle, 2014; Goossen & Carnabuci, 2020; Hjertvikrem & Fitjar, 2020; Liu et al., 2020; Tóth & Lengyel, 2021). These social ties that employees share across organizational boundaries in turn lead to new channels between organizations and the formation of a new interorganizational network (Hjertvikrem & Fitjar, 2020). This network enables employees to receive information about new employment opportu- nities and mitigates asymmetric information related to job changes, which in turn triggers new interor- ganizational employee mobility events (Gianelle, 2014). This basically closes the “employee mobility– interorganizational network/social ties” circle. Thus, it is quite difcult to disentangle the complicated relationship between employee mobility and the for- mation of networks/ties, as they have coevolved over time and are closely intertwined (Shimizu & Hirao, 2009). Thus, an organization’s advantageous posi- tion in the interorganizational network can act as a double-edged sword: while it promotes organiza- tional growth and competitiveness (Sørensen, 1999) by attracting skilled and talented employees, it en- ables existing employees to strengthen their social ties across organizational boundaries, which may cause them to permanently move elsewhere. There are many situations that show how interorganizational employee mobility can also have a negative effect on the market and social ties that organizations and their employees have with each other (e.g., with customers, 70 ECONOMIC AND BUSINESS REVIEW 2025;27:50–79 buyers, suppliers, etc.). For example, in the adver- tising, legislative lobbying, and telecommunications sectors, where market relationships between service and client rms depend largely on the social ties of their exchange managers, there is a high risk that mar- ket ties between these rms will dissolve once one of the managers (either from the service or client rm) leaves (Bermiss & Greenbaum, 2016; Broschak, 2004; Broschak & Block, 2014; Broschak et al., 2020; Dokko & Rosenkopf, 2010; Rafee, 2017). When a buying rm hires a new employee from a competitor of its usual supplier, the existing ties between buyer and supplier begin to weaken (Carnahan & Somaya, 2013). This basically means that interorganizational mobility al- most always affects interorganizational ties, but the direction of such a relationship usually depends on whose employees move and where to. In addition to social ties, papers addressing the ef- fects of interorganizational mobility at the individual level also emphasize the opportunity for professional success (Buchinsky et al., 2010; Campbell et al., 2017; Czaller et al., 2021; Eby, 2001; Fernández-Zubieta et al., 2016; Haunschild, 2003; Javdani, 2020; Ruiz, 2014; Vinkenburg & Weber, 2012). Acquiring diverse work experiences and improving work attitudes in- crease employees’ performance, productivity, and market value (Buchinsky et al., 2010; Fernández- Zubieta et al., 2016; Lee, 2018), thus contributing positively to their professional success. In the theater industry, mobility is a prerequisite for creative and in- novative theater work and thus a must for those who want to advance professionally (Haunschild, 2003). Various studies have examined how interorgani- zational mobility affects one’s income/salary and have come to different conclusions. Some suggest a posi- tive effect (Javdani, 2020; Knight & Yueh, 2004; Sam- marra et al., 2013; Sgobbi & Suleman, 2015; Valcour & Tolbert, 2003), while others have found the opposite (Eby, 2001; Kidd, 1991; Mueller & Schweri, 2015). Pos- itive effect tends to be applied to strategically more important employees because they are harder to re- place. Martins (2011) found that this increase is about 10% in manufacturing and services, especially when the employee moves from a domestic to a foreign or- ganization within the same country. Buchinsky et al. (2010) found that employees who choose to move elsewhere risk losing the return on their accumulated seniority in their source organization. Accordingly, these losses are higher the longer they worked at the source organization, and they are higher for employ- ees with less education (high school graduates versus college graduates). For those who did not spend much time at their previous job, the lost returns to tenure are offset by the salary increase at their new job, and this increase will be higher for the more educated employees (Buchinsky et al., 2010). There are also some gender differences, as occupations with a larger share of male employees offer the same opportunities to college-educated women and men, regardless of their college degree (Pearlman, 2018). Finally, mobility experience itself, which we have already examined as a mobility determinant, has also been shown to be a mobility effect. The study by López et al. (2020) shows that an employee’s mobility between organizations increases the likelihood of one or more subsequent moves between the same organi- zations. Further, once an individual has experienced interorganizational mobility and its associated bene- ts, such an employee is more likely to move again. However, some other authors point out that excessive mobility can be destructive for individuals, especially in the early stages of their careers, as too frequent changes of employer can lead to loss of human capital and be a sign of limited employability (Korpi & Mertens, 2003). Castellani et al. (2022) emphasize that organizations engaged in patenting innovations perceive employees involved in interorganizational mobility as organizational outsiders, which hinders their ability to develop important relational assets. As a result, mobile employees have difculty forming trusting social ties with colleagues in their new organization (Castellani et al., 2022). Moreover, the highlighted benets of the boundaryless career concept only reect the reality for highly educated and highly skilled professionals, while employees with lower levels of education and easily replaceable skills often perceive this perspective as a burden because they cannot demand and receive fair treatment from their employers (Kost et al., 2020). 5 Concluding remarks and avenues for future research Interorganizational employee mobility corresponds to the transition from organizational to boundaryless careers (Wille et al., 2010), characterized by physical and psychological movement across organizational boundaries and self-determined career planning guided by personal values (Dries & Pepermans, 2008). Moreover, the phenomenon of interorganizational employee mobility represents a fundamental shift in the psychological contract of employment that undermines any assumption that an organization is capable of providing lifetime employment. It is essentially a new contract in which both the employer and the employee know that their relationship is unlikely to last forever (Arthur et al., 2005). Moreover, it is a multilevel phenomenon that connects at least two organizations through one employee, thus every action is determined by and affects all parties ECONOMIC AND BUSINESS REVIEW 2025;27:50–79 71 involved (i.e., source and destination organizations and mobile employee) at all levels (i.e., economic, organizational, and individual). At the same time, both the source and the destination organizations may experience negative consequences, as well as benets of employee mobility. For example, the productivity, performance, and consequently the competitive advantage of destina- tion organizations can increase as a result of the knowledge that mobile employees pass on to them. At the same time, such a direction of knowledge transfer can be detrimental to the source organization. Fur- thermore, the replication of advantageous routines and processes from the source to the destination or- ganization also has negative effects for the former and positive effects for the latter. By losing their so-called star employees, the status of the source organization decreases, while at the same time the status of the destination organization, which employs such a star, increases. However, the results of this paper show that the reality is not all black and white. In other words, all these mobility effects, which can be negative for the source organization and positive for the destination organization, can also go in the opposite direction. For example, individuals who tend to move fre- quently between organizations may also be perceived as a threat to a new employer, so that their knowl- edge, skills, and competencies may not be shared in the destination organization (Castellani et al., 2022) and therefore not have a positive impact on the desti- nation organization’s productivity, performance, and competitive advantage. Due to the social ties that mobile employees maintain with their source orga- nizations, knowledge spillover can also occur in the opposite direction, that is, from the destination to the source organization. This occurs frequently in some patent industries, as inventors continue to ex- change ideas with their former colleagues (Goossen & Carnabuci, 2020). This also contradicts the ndings that knowledge transfer as a mobility effect and its benets are one-dimensional (Cantner et al., 2010). Instead, the transfer takes place reciprocally and bidi- rectionally and thus has a positive effect on both the source and the destination organization (Corredoira & Rosenkopf, 2010). The loss of an employee therefore does not always mean a loss for the source organiza- tion or a gain for the destination organization. Furthermore, these effects of interorganizational employee mobility are not always dichotomous, that is, if one side is negatively affected (e.g., the source organization), the other side benets (e.g., the desti- nation organization). On the contrary, interorganiza- tional employee mobility can have a positive effect for all parties involved—that is, for both the source and destination organizations as well as for the mobile employee. For example, when employee mobility takes place between two organizations that have pre- viously worked together, or when it leads to new interorganizational cooperation based on social ties that employees establish and maintain with each other, it has benets for all parties involved and con- sequently for entire regions, sectors, national or even global economies (Tóth & Lengyel, 2021). Such coop- eration is particularly important when it takes place between organizations from different sectors—for example, between academic and nonacademic sec- tors because academia, as the knowledge gatekeeper, produces scientic knowledge, while the related in- dustry commercializes it (Fleming & Frenken, 2007; Petruzzelli et al., 2010). Such a cooperative nature as a result of interorganizational employee mobility leads to the participation and involvement of many other economic actors, which ultimately leads to the cre- ation of innovative useful products and processes that enrich the lives of members of the entire community and thus have a positive impact at all levels studied (economic, organizational, and individual). Furthermore, the results of this paper prove that the interorganizational network that exists at the organi- zational level and is rooted in the social ties between individuals in these organizations (i.e., at the indi- vidual level) inuences interorganizational mobility, but also results from it (i.e., is its determinant and effect). Organizations benet from being networked with as many other “market participants” as possible (e.g., customers, buyers, suppliers, etc.) or, in other words, from being more deeply rooted in the net- work. This helps them strengthen their market ties in many directions, including those through which they attract and recruit new employees (i.e., the recruit- ment network—Mai et al., 2015). This same network and its connections across organizational boundaries help organizational employees share valuable infor- mation with employees in other organizations, which strengthens their social ties. These social ties, which are practically rooted in the interorganizational net- work or market ties, create new channels through which these employees perceive their employment opportunities elsewhere, or simply put, they de- termine their interorganizational mobility decisions. Once they have left but remained in contact with their former employer, mobile employees essentially cre- ate new market ties (or interorganizational networks) between their current and former employers (i.e., be- tween their source and destination organizations). This again proves how market ties practically emerge from social ties and how these two are mutually in- tertwined, acting as both a determinant and an effect of employee mobility and closing the vicious circle of “market/social ties—employee mobility.” 72 ECONOMIC AND BUSINESS REVIEW 2025;27:50–79 Several other factors also appear as a determinant and effect of employee mobility. Organizational status is one of them. The higher the status of the destination organization, the higher the probability of moving there. When a destination organization hires someone from a higher status source organization, this mobility usually improves the status/reputation of the destination organization (Betancourt & Wezel, 2016). Professional success is also seen as a determinant and effect of a mobility decision. Based on their individual orientation toward career advancement and improvement, employees decide whether their current employer provides them with sufcient learning and promotion opportunities or whether they need to seek them elsewhere. In addition, their movement between organizations affects the success they seek. Since the career management literature generally distinguishes between the individual and organizational levels of managing career success (Baruch, 2022), this factor should be examined at both the individual and organizational levels. The same is true for income/salary, as this factor also appears as both a determinant and an effect of mobility. However, if it plays the role of a determinant, it appears at the organizational level, but if it plays the role of a mobility effect, it is classied at the individual level. Mobility experience is another factor that usually determines a person’s future mobility, as those who have already changed employers tend to do so again. On the other hand, it can also be considered as an effect of mobility, because when employees move from one organization to another, it usually increases the likelihood of one or more future moves, usually between the same organizations (López et al., 2020). Finally, growing business activity, which has been shown to be a determinant of mobility at the economic level, can also be found as a mobility effect at the same level, because in times of prosperity people tend to move more, which contributes to the further growth of the economic system. Fig. 3 summarizes what has been said and highlights factors that occur in different roles and at different levels. Our literature review also revealed interesting re- lationships between different mobility determinants at different levels of analysis. For example, when ex- amining how the size of an organization affects an employee’s mobility decision, this factor usually can- not be considered in isolation, as it interacts with other determinants at the same level, such as the lo- cation and the age of the organization, as well as with some determinants at the economic level, such as government policies and sectoral characteristics (private vs. public sector), and individual-level deter- minants, such as the employee’s gender, age, family relationships, length of service, and position in the organizational hierarchy. In addition, some studies have shown that there may also be a slight hier- archy among mobility determinants, as some may appear more important or have a stronger relation- ship with interorganizational mobility. As Betancourt and Wezel (2016) found in their study in the legal ser- vices sector, employees tend to move to a destination organization despite its lower organizational status because of its higher salary and greater organizational support. This calls for future research to examine the strength of the relationship between interorganiza- tional employee mobility and each of its determinants at different levels. The systematic approach we used in our work en- abled us to produce a comprehensive and structured literature review. Comprehensiveness was achieved by assessing in detail all determinants and effects of employee mobility that researchers in the eld of interorganizational mobility have found to date (Table 7), as well as the exact results of their studies. The structured approach was achieved by ensuring that the entire process of collecting and analyzing the literature and writing the paper followed a series of clear and repeatable steps (Pickering & Byrne, 2014). To our knowledge, this was the rst attempt of its kind to comprehensively examine both the determi- nants and effects of interorganizational mobility and to create their typologies at multiple levels. These typologies summarize all previous research ndings across different sectors and industries, thus enrich- ing the existing mobility literature. They also provide a comprehensive framework for the phenomenon of interorganizational employee mobility, encompass- ing both its direct antecedents and its consequences. Moreover, they shed light on the complex nature of the phenomenon of interorganizational mobility by reecting the fact that some mobility effects act simul- taneously as determinants of mobility and that the same determinants may be located at different levels of analysis. Furthermore, the literature on employee mobility is extended by distinguishing between dif- ferent types of interorganizational mobility: mobility within the sector or intra-sector mobility and mobility between organizations from different sectors or inter- sector mobility. Uncovering the main economic determinants and effects of employee mobility in different sectors and industries also contributes to the labor economics literature. It adds to the knowledge of the economic factors that lead to and result in individuals’ decisions to move between two organizations. Since the mobility of employees between organizations from different countries has been shown to be due to some of these economic factors, uncovering the ECONOMIC AND BUSINESS REVIEW 2025;27:50–79 73 Fig. 3. Closed circle of determinants and effects of interorganizational employee mobility. Table 7. Distribution of observed factors of interorganizational employee mobility. Observed interorganizational mobility factors No. of papers % of sample (158 papers) Economic determinants of interorganizational employee mobility 38 24 Organizational determinants of interorganizational employee mobility 52 32.9 Individual determinants of interorganizational employee mobility 68 43 Economic effects of interorganizational employee mobility 6 3.8 Organizational effects of interorganizational employee mobility 83 52.5 Individual effects of interorganizational employee mobility 32 20.3 determinants and effects of mobility at the economic level also contributes to the migration literature. In contrast, uncovering the critical determinants and effects of employee mobility at the organizational and individual levels contributes to the career development literature, which is theoretically divided between the individual and organizational levels of career management (Baruch, 2022). Emphasizing the personal and organizational factors that lead to and result in individuals’ decisions to move between two organizations therefore enriches the understanding of the whole process of career development and the positive and negative aspects it can have for both the mobile individual and the organizations they move between. The results of this paper also have valuable practical implications. First, the identied multilevel deter- minants and effects of interorganizational employee mobility (Tables 3 to 6) provide business leaders and their human resource management in sectors and industries such as advertising, banking and nance, biotechnology, construction, consulting, energy, engineering, food, healthcare, higher education, IT and high-tech, manufacturing, media, pharma- ceuticals, public and legal services, semiconductors, telecommunications, textiles, and so forth with a clear idea of the main reasons why employees leave their organizations permanently and the effects of this mobility. Secondly, focusing on the organizational level of the determinants of interorganizational mobility in all these sectors and industries provides business leaders with solutions on how to retain their employees. In other words, recognizing that the interorganizational relationships an organization has both with others within its sector and with organizations from other sectors, its status, the opportunities for learning, train- ing, promotion, and so forth that it offers, the support it provides in terms of autonomy, participation in de- cision making, exible working arrangements, work– life balance, and so forth play an important role in the mobility decisions of its employees, automatically 74 ECONOMIC AND BUSINESS REVIEW 2025;27:50–79 gives these organizations ideas on how to prevent their permanent moves. Thirdly, focusing on the organizational level of the effects of interorganizational mobility in all these sec- tors and industries offers solutions to the managers of organizations on how to at least mitigate the negative effects when their employees decide to move else- where. In particular, the nding that knowledge can also be transferred from the destination to the source organization shows that employee mobility does not always have a negative impact on the source organi- zation’s productivity, performance, and competitive advantage. On the contrary, this offers the solution that the source organization can also build interor- ganizational relationships with the new destination organization of their former employee by maintaining the relationship with their former employee. Through this new interorganizational relationship (between source and destination organization), a new coop- eration can also be achieved, which has a positive impact on the productivity, performance, and com- petitive advantage of all parties involved. This sheds new light on the question of how to proceed when valuable employees leave permanently. Finally, as employees are recognized as an integral part of value creation whose value increases with the knowledge intensity of an industry (Campbell et al., 2012), the results of this study are particularly applicable and useful for practitioners in knowledge- intensive industries (e.g., higher education, IT, phar- maceuticals, biotechnology, etc.). A possible bias in nding relevant literature in the employee mobility area is one of the limitations of this paper. This is quite common when writing a lit- erature review, and while systematic approaches tend to reduce such biases, they still cannot be eliminated. Future research should therefore include additional electronic databases when searching the literature, or even relevant research published in other languages. The omission of older papers or papers not available through electronic searches could also be minimized by using the reference lists of the selected papers in the sample. Although the conceptualization of in- terorganizational employee mobility extends beyond just turnover behavior (Wille et al., 2010), broaden- ing the target eld and deepening and integrating it with the turnover literature could also be benecial for future research. Since a larger number of studies can be expected in the eld of employee turnover, a bibliometric, meta-, or other quantitative analysis of the literature review could also be benecial for the eld of interorganizational employee mobility. Another limitation of our paper is that we only captured the determinants and effects of interorgani- zational employee mobility that have been explored up to date. Empirical testing of the results of our study in some knowledge-intensive sectors/industries, for example, through case studies, expert interviews, and so forth, could be useful for a better understanding of the important aspects of the relationships between different determinants of mobility. Moreover, since we have found that interorganizational mobility can be related to inter- or intra-sector mobility, future re- search could pay more attention to uncovering all possible types of interorganizational mobility. Since knowledge-intensive sectors/industries tend to grant different types of sabbaticals to their employees (So- ciety for Human Resource Management, 2018), future research should also consider temporary and perma- nent types of interorganizational mobility. In addition, the relationships between the mobility determinants at different levels should be examined more closely. For example, all the types of interorga- nizational mobility mentioned (inter- and intra-sector, permanent and temporary) can also be international, meaning that employees move between organiza- tions from different countries. In this case, economic determinants play an important role. Even though our study did not show that there are different eco- nomic determinants in different countries—it has shown that these determinants are mostly recipro- cal, that is, that their conditions are different (e.g., comparison of better or worse government policies and public services in two countries; liberal vs. co- ordinated market economy, etc.)—they should be investigated further. More specically, future research should also combine the migration and mobility lit- eratures to uncover all possible determinants at the economic level that inuence interorganizational mo- bility between countries, and how these economic determinants also inuence other organizational and perhaps individual determinants. Moreover, since interorganizational mobility has been shown to have different effects when employees move between competitors rather than between coop- erators, studying and uncovering possible factors that facilitate cooperation even between competing source and destination organizations could be benecial for strengthening existing and building new interorgani- zational networks. These networks will consequently have a positive impact on the development of some countries’ regions and entire economies. Acknowledgments This scientic article was partially supported by the University of Rijeka through the project ZIP- UNIRI-2023-16. It was also partially supported by the public grant of the Slovenian Research and Innova- tion Agency (ARIS) no. P5-0117. ECONOMIC AND BUSINESS REVIEW 2025;27:50–79 75 Declaration of conicting interests The authors declared no potential conicts of inter- est with respect to the research, authorship, and/or publication of this article. References Aguirre, L. 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