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SHRM Foundation Research 

An Extended Model of Social Embeddedness:  Applying Social Network Theory to Enrich Job Embeddedness Theory

Funded: November 2008  Completed: February 2012 

Peter Hom, Ph.D., Dept. of Management, Arizona State University
Kristie Rogers, Doctoral Student, Arizona State University
David Allen, Ph.D., SPHR, Dept. of Management, University of Memphis
Mian Zhang, Ph.D., Tsinghua University
Cynthia Lee, Northeastern University & Hong Kong Polytechnic University
Hailin (Helen) Zhao, Master’s Student, Hong Kong Polytechnic University

Executive Summary
Organizational scholars and managers are increasingly concerned about why employees stay, which is poorly understand by the prevailing research focus on why employees quit. In 2001, Terence Mitchell and Thomas Lee introduced the theory of job embeddedness to clarify the psychology of staying. They asserted that employees stay because they fit the job and community (“fit”), have multiple connections to people inside and outside the firm (“links”), and would lose valued benefits if they quit or relocate (“sacrifice”). Many research studies have since shown that job embeddedness can predict employee turnover for different occupations, demographic subgroups, and cultures (improving upon traditional turnover models). 

Despite its growing validation, Peter Hom, Kristie Rogers, David Allen, Mian Zhang, Cynthia Lee, and Helen Zhao sought to better clarify why people stay by considering additional facets of “embedding links” to extend Mitchell and Lee’s (2001) preeminent theory of staying.  The standard view of links underscores the number of social ties binding employees to jobs. More ties translate into greater staying. Yet links can conceivably embed employees in other ways, namely, delivering social capital, expressing social pressures (to stay or leave), and remaining in the job and community (maintain existing relationships).  

To enrich prevailing embeddedness theory, Peter Hom, Kristie Rogers, David Allen, Mian Zhang, Cynthia Lee, and Helen Zhao carried out network surveys with nearly 900 employees in various occupations from America, Hong Kong, and China to capture these additional dimensions of embedding links. They also measured job embeddedness and quit propensity (quit intentions and turnover behavior).  Their statistical analyses demonstrated that these new link dimensions can explain additional variance in quit propensity beyond that explained by job embeddedness. Their findings regarding an extended embeddedness theory and implications for HR policies are detailed below.

KEY FINDINGS AND PRACTICAL IMPLICATIONS
1. Strong emotional bonds to colleagues can reduce quit propensity and improve upon the predictive validity of job embeddedness (and number of links).  Employees stay not only because they have many workplace and community ties; they also stay if they have close friends at work.  Having lots of connections is not enough build job loyalty.

2. Closed, cohesive friendship networks reduce quit propensity.  Job embeddedness theory focuses on how many links an employee has, but whether or not those links are themselves interconnected also matters.  If one’s friends are themselves friends, one’s network becomes more cohesive and fosters stronger friendships.  Employers should strive to build cohesive workgroups or teams as mutually affiliated team or group members can further embed employees.

3. While job embeddedness theory stresses how links can bind employees to a job, we found that their disappearance, such as colleagues quitting or spouses relocating, can conversely induce employees to quit. Thus, employers should be mindful of whether “turnover contagion” is occurring as high rates of turnover among employees may prompt others in the organization to follow suit.

4. An employee’s position within communication and friendship networks in a workplace matters, not simply how many contacts he or she has.  If an employee befriends a few popular employees (rather than many coworkers) or is a liaison (or go-between) between separate subgroups or parties within a workplace, his or her “centrality” in the network increases his or her propensity to stay. Managers should thus focus on the whole communication network in their units and their employees’ positions within that network. Are employees central or peripheral to that network? 

5. Social pressures vary and can conflict across different links, though standard embeddedness theory suggests that links speak with one uniform voice.  External links—notably, family, outside friends, and external professional contacts—have greater say in whether or not employees should stay or leave. Employers might monitor how employees’ families view their organization as well as their external reputation and attempt to cultivate more favorable impressions (e.g., extend benefits to families). 

BOTTOM LINE:
HR professionals must attend to other ways that embedding links can promote employee retention.  Standard embeddedness theory offers valuable prescriptions about how firms can strengthen employees’ propensity to stay, such as fostering more connections to on- and off-the-job contacts. Our research suggests that firms can do more than simply expanding employees’ links.  Greater attention to fortifying links, building relationships among links, insuring that links are stable (rather than vanish), and promoting social pressures to stay (rather than leave) may yield greater dividends in terms of stronger job embeddedness.

Study Methods
The investigators surveyed nearly 900 employees from America, China, and Hong Kong on why they stay in their jobs. They assessed job embeddedness (assessing conventional embedding forces) and social networks to learn how social ties can further embed employees. They sought to build on job embeddedness theory by determining whether emotional closeness to links, connectivity among links, centrality in the network structure of links, potential link defections, and social pressures emanating from links can improve upon the accuracy of job embeddedness (and its notion of links as number of social ties) for predicting quit propensity.

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