The Center for Hospitality Research Hospitality Leadership Through Learning The Contagion Effect: Understanding the Impact of Changes in Individual and Work-unit Satisfaction on Hospitality Industry Turnover Cornell Hospitality Report th Vol. 12 No. 9, August 2012 01992 - 2012 by Timothy Hinkin, Ph.D., Brooks Holtom, Ph.D, 2 and Dong Liu, Ph.D. ANNIVERSARY All CHR reports are available for free download, but may not be reposted, reproduced, or distributed without the express permission of the publisher. Cornell Hospitality Reports Vol. 12, No. 9 (August 2012) © 2012 Cornell University. This report may not be reproduced or distributed without the express permission of the publisher. Cornell Hospitality Report is produced for the benefit of the hospitality industry by The Center for Hospitality Research at Cornell University. Robert J. Kwortnik, Academic Director Jennifer Macera, Associate Director Glenn Withiam, Director of Publications Center for Hospitality Research Cornell University School of Hotel Administration 489 Statler Hall Ithaca, NY 14853 Phone: 607-255-9780 Fax: 607-254-2922 Advisory Board www.chr.cornell.edu Niklas Andréen, Group Vice President Global Hospitality & Partner Marketing, Travelport GDS Radhika Kulkarni, Ph.D., VP of Advanced Analytics R&D, Ra’anan Ben-Zur, Chief Executive Officer, French Quarter Holdings, Inc. SAS Institute Scott Berman, Principal, Real Estate Business Advisory Services, Industry Gerald Lawless, Executive Chairman, Jumeirah Group Leader, Hospitality & Leisure, PricewaterhouseCoopers Steve Levigne, Vice President, U.S. Strategy & Insights, McDonald’s Raymond Bickson, Managing Director and Chief Executive Officer, Taj Corporation Group of Hotels, Resorts, and Palaces Mark V. Lomanno Executive Board Member, newBrandAnalytics Stephen C. 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Kenneth Kahn, President/Owner, LRP Publications Adam Weissenberg, Vice Chairman, Global and U.S. Travel, Hospitality & Keith Kefgen, Chief Executive Officer, HVS Executive Search Leisure Leader, Deloitte & Touche USA LLP Kirk Kinsell, President, The Americas, InterContinental Hotels Group Thank you to our generous Corporate Members Senior Partners ASAE Foundation Carlson Hotels Hilton Worldwide National Restaurant Association SAS STR Taj Hotels Resorts and Palaces Partners Davis & Gilbert LLP Deloitte & Touche USA LLP Denihan Hospitality Group eCornell & Executive Education Expedia, Inc. Forbes Travel Guide Four Seasons Hotels and Resorts Fox Rothschild LLP French Quarter Holdings, Inc. HVS Hyatt InterContinental Hotels Group Jumeirah Group LRP Publications Maritz Marriott International, Inc. Marsh’s Hospitality Practice McDonald’s USA newBrandAnalytics priceline.com PricewaterhouseCoopers Proskauer ReviewPro Sabre Hospitality Solutions Sathguru Management Consultants (P) Ltd. Schneider Electric Thayer Lodging Group Thompson Hotels Travelport WATG Wyndham Hotel Group Friends 4Hoteliers.com • Berkshire Healthcare • Center for Advanced Retail Technology • Cleverdis • Complete Seating • Cruise Industry News • DK Shifflet & Associates • ehotelier.com • EyeforTravel • Gerencia de Hoteles & Restaurantes • Global Hospitality Resources • Hospitality Financial and Technological Professionals • hospitalityInside.com • hospitalitynet.org • Hospitality Technology Magazine • HotelExecutive.com • International CHRIE • International Hotel Conference • International Society of Hospitality Consultants • iPerceptions • JDA Software Group, Inc. • J.D. Power and Associates • The Leading Hotels of the World, Ltd. • Lodging Hospitality • Lodging Magazine • LRA Worldwide, Inc. • Milestone Internet Marketing • MindFolio • Mindshare Technologies • PhoCusWright Inc. • PKF Hospitality Research • Questex Hospitality Group • Resort and Recreation Magazine • The Resort Trades • RestaurantEdge.com • Shibata Publishing Co. • Synovate • UniFocus • Vantage Strategy • WageWatch, Inc. • WIWIH.COM The Contagion Effect: Understanding the Impact of Changes in Individual and Work-unit Satisfaction on Hospitality Industry Turnover by Timothy Hinkin, Brooks Holtom, and Dong Liu ExECuTivE SuMMAry his report describes a two-year longitudinal study examining the effects on employee turnover Tof the change in individual and unit levels of satisfaction. Analyses of data collected from 5,270 employees in 175 business units of a hospitality company demonstrate that changes in an individual’s level of satisfaction affect that person’s turnover decisions. More important, unit- level job satisfaction change and its dispersion jointly affect the individual’s satisfaction change and the overall turnover rate in a unit, in what can be termed a “contagion effect.” As the work environment becomes more positive (employees are satisfied) and overall satisfaction in the unit increases over time, for example, fewer individuals leave their jobs. Even unhappy employees are lifted by a coherently positive environment. We further find evidence of a multilevel three-way interactive effect of unit-level job satisfaction change and its dispersion, and individual job satisfaction change on individual turnover. When attitudes in a work unit vary substantially, a general increase in satisfaction has little effect on an individual’s satisfaction or turnover plans. Put differently, when an employee is out of step with prevailing trajectory in unit-level attitudes, the discrepancy of attitudes appears to alter the relationship between his or her job satisfaction trajectory and turnover propensity. The findings emphasize the importance of tracking changes in employee satisfaction and the impact of changes in group attitudes on individual attitude and behavior. 4 The Center for Hospitality Research • Cornell University ABouT ThE AuThorS Timothy hinkin, Ph.D., is the Georges and Marian St. Laurent Professor of Applied Management at the Cornell University School of Hotel Administration, where he also serves as the Richard J. and Monene P. Bradley Director of Graduate Studies. Hinkin served as the school’s Director of Undergraduate Studies for six years. He also teaches in the school’s Professional Development Program. Hinkin’s primary research focus is in leadership, employee retention, supervisor-subordinate relationships, and managing service quality. He recently published, Cases in Hospitality Management: A Critical Incident Approach (2nd edition, New York: John Wiley , 2006), Hinkin has also written many articles published in journals such as Human Relations, Journal of Applied Psychology, Hospitality Research Journal, and the Cornell Hospitality Quarterly. Prior to attending graduate school he worked for Hyatt Hotels and Sysco Corporation. He has provided training and consulting for a wide range of enterprises, including IBM Corporation, Israeli Hotel Managers Association, Institute for Hotel Management, Accor of North America and ClubCorp USA, Inc. He was awarded a Fulbright Fellowship in 2005. Brooks holtom, Ph.D. is an associate professor at the McDonough School of Business, Georgetown University (bch6@georgetown.edu). His research focuses on how organizations acquire, develop and retain human and social capital. His work has appeared in the top journals in management (Academy of Management Journal, Journal of Applied Psychology, International Journal of Human Resource Management and many others). In the 2007 AACSB Report on the Impact of Business School Research, his work was specifically cited as having made an important intellectual contribution to policy or practice (along with Michael Porter of Harvard, Peter Senge of MIT and Nobel Prize winner James March). He was named the 2005 Ascendant Scholar of the Year for the Western Academy of Management and has twice received the Professor of the Year award for the Georgetown University Executive Masters of Leadership Program. He has performed research in or served as a consultant to many organizations including Booz Allen Hamilton, Capital One, Citibank, International Monetary Fund, Northwestern Mutual, the Korean Ministry of Finance and Economy, Rolls Royce, POSCO, SK Group, United States Air Force, U.S. Chamber of Commerce, and the World Bank. Dong Liu, Ph.D., is an assistant professor at the Ernest Scheller Jr. College of Business, at the Georgia Institute of Technology. His research interests include creativity, leadership, teams, international entrepreneurship, and turnover, with particular focus on exploring the multilevel interface between individuals and organizational context. His research has been published in the Academy of Management Journal, Journal of Applied Psychology, Journal of Occupational and Organizational Psychology, Academy of Management Best Paper Proceedings, and Ivey Case Publishing. He has won several research and teaching awards from the Academy of International Business, the Human Resources Division and the International Management Division of the Academy of Management, the International Association for Chinese Management Research, the Society for Industrial and Organizational Psychology, the Entrepreneurship/Innovation/IT Division and the HR Management/ Careers Division of the Southern Management Association, the Center for Creative Leadership, the Michael G. Foster School of Business at the University of Washington, the Chinese Government, and Ivey Publishing. Cornell Hospitality Report • August 2012 • www.chr.cornell.edu 5 CornELL hoSpiTALiTy rEporT The Contagion Effect: Understanding the Impact of Changes in Individual and Work- unit Satisfaction on Hospitality Industry Turnover by Timothy Hinkin, Brooks Holtom, and Dong Liu Despite everyone’s best efforts, employee turnover continues to plague the hospitality industry. Recent U.S. Bureau of Labor Statistics data show that the voluntary turnover rate in the lodging and food-service industry is 58.8 percent, which is 24 percent higher than retail trade and 54 percent higher than health-care services, which also employ a large number of low paid hourly workers.1 Although high turnover rates are accepted by many as “endemic to the industry,” we contend that turnover does not have to be so excessive. By implementing best human resource practices such as flexible scheduling, career development, and performance management systems some hospitality organizations have reduced turnover rates to approximately one-third of the industry average.2 There is a monetary payoff to these practices as well, as our research has found that turnover has significant negative impact on profitability in two ways: increasing expenses and reducing revenue.3 Reduced service quality is also a byproduct of high employee turnover. But even those reduced turnover rates are high and costly. 1 Bureau of Labor Statistics, “Job Openings and Labor Turnover Report,” January 2012. 2 T.R. Hinkin and J.B. Tracey, “What Makes Them So Great? An Analysis of Human Resources Practices among Fortune’s Best Companies to Work For,” Cornell Hospitality Quarterly, Vol. 51, No. 2 (May 2010), pp. 158-170. 3 T.R. Hinkin and J.B. Tracey. The Cost of Turnover: Putting a Price on the Learning Curve,” Cornell Hotel and Restaurant Administration Quarterly, Vol. 41, No. 3 (June 2000) pp. 14-21; T. Simons and T.R. Hinkin. The Effect of Employee Turnover on Hotel Profits: A Test across Multiple Hotels,” Cornell Hotel and Restaurant Administration Quarterly, Vol. 42, No. 4 (August 2001), pp. 65-69; J.B. Tracey and T.R. Hinkin, “Contextual Factors and Cost Pro- files Associated with Employee Turnover,” Cornell Hospitality Quarterly, Vol. 49, No. 1 (February 2008), pp. 12-27. 6 The Center for Hospitality Research • Cornell University One source of turnover intentions is a person’s satisfac- tion with his or her job. High individual job satisfaction has long been thought to be essential to reducing an employee’s likelihood of leaving.4 Yet the average correlation between job satisfaction and turnover is a relatively modest -0.19.5 If the relationship between satisfaction and turnover is so low, one might ask “Why should we be concerned so much about hav- ing happy employees?” One answer is, there are a lot of factors that affect turnover, and a worker’s satisfaction is just one of them. Assessing changes in A better answer to this question points to a more fine- grained view of job satisfaction. The fact is that job satisfac- satisfaction over time tion is dynamic (i.e., not constant over time) and relative (i.e., increases management’s the influence of one’s own job satisfaction may be subject to coworkers’ job satisfaction). In addition, job satisfaction is ability to predict turnover. multidimensional, comprising factors such as satisfaction with the work itself, co-workers, supervisor, working condi- tions, compensation, and benefits. So how satisfaction is measured and what is measured with regard to individual job satisfaction is important for accurately predicting turnover. Historically, turnover research has focused on measur- ing job satisfaction at one point in time to predict subsequent turnover. For example, many hospitality organizations take an annual employee satisfaction survey and then correlate the re- sults of the survey with subsequent turnover. While this static measure provides some helpful information, it usually taps into the average of workers’ satisfaction levels at the moment of measurement. Some of the individuals being surveyed may be pleased with their job, while others are miserable, and an average can hide those extremes. As a result, precision in predicting employee turnover is hard to achieve. Recent research by Chen and his colleagues, however, has shown that assessing changes in satisfaction over time increases one’s ability to predict turnover beyond that of a static measure.6 Individual levels of satisfaction are perhaps more subject to change over even short periods of time than managers may think. Indeed, Chen’s group found that job satisfaction levels changed for nearly every person across four different samples (including consultants, military, and MBA students) and across time periods ranging from just a few weeks to six months. Building on this research, we sought 4 P.W. Hom and R.W. Griffeth, Employee Turnover (Cincinnati, OH: South- Western College Publishing, 1995); W.H. Mobley, “Intermediate Linkages in the Relationship between Job Satisfaction and Employee Turnover,” Journal of Applied Psychology, Vol. 62 (1977), pp. 237-240. 5 R.W. Griffeth, P.W. Hom, and S. Gaertner, “A Meta-analysis of Anteced- ents and Correlates of Employee Turnover,” Journal of Management, Vol. 26, No. 3 (2000), pp. 463-488. 6 G. Chen, R.E. Ployhart, H.C. Anderson, and P.D. Bliese, “The Power of Momentum: A New Model of Dynamic Relationships between Job Satisfac- tion Change and Turnover Intentions,” Academy of Management Journal, Vol. 54 (2011), pp. 159-181. Cornell Hospitality Report • August 2012 • www.chr.cornell.edu 7 to assess the impact of changes in levels of satisfaction over time. Consequently, we conducted a longitudinal study of a type that is rarely completed. As we indicated above, turnover research has focused typically on individual workers’ attitudes and intentions, ignoring the potential impact of the work group. But this ignores the importance of the work environment. People in organizations are continually consciously and unconsciously Research is needed on the looking for cues from coworkers about appropriate and in- effects of a work group’s appropriate feelings and behaviors. This is particularly true when one is new to an organization.7 When an individual attitude over time on an joins an organization he or she initially has positive attitudes about the job and the company and, over time, these at- individual’s decision to leave. titudes will undoubtedly change. When a person’s satisfaction level declines, it creates dissonance, as expectations about the job are not met. This in turn could produce uncertainty that triggers the search for alternatives to the current employment situation. It is at this time that an individual most likely would look to others in the workplace for cues. Coworkers might have a wide diversity of satisfaction levels (high dispersion), or they may all have similar and improving satisfaction levels (conver- gence). In situations where there is high variance or disper- sion of satisfaction change among co-workers, the individual worker’s uncertainty would likely increase, which would strengthen the person’s search for alternatives (i.e., another job). Strong convergence can go in either direction. When the convergence involves positive change in unit satisfaction, the individual’s uncertainty would be lowered, and indi- vidual search behavior would likely be reduced. Alternatively, if there is high convergence and negative change in unit satisfaction, search behavior would likely increase. Thus, the less dispersion in attitudes among a group of workers, the greater the influence of that group and tendency for others to follow. For example, if I hear increased grumbling among a large group of my coworkers, I am more likely to grumble. If only a few grumble, I am less likely to do so. Indeed, Felps and colleagues’ turnover contagion research demonstrates that when co-workers conduct job searches, this behavior will encourage an employee’s leaving above and beyond the employee’s own job search behavior.8 To explore the dynamic and relativistic nature of indi- vidual job satisfaction discussed above, this study takes a unique approach by measuring change in individual satisfac- 7 J. Thomas and R.W. Griffin,” The Social Information Processing Model of Task Design: A Review of the Literature,” Academy of Management Review, Vol. 8 (1983), pp. 672-682. 8 W. Phelps, T.R. Mitchell, D.R. Hekman, T.W. Lee, B.C. Holtom, and W. S. Harman, “Turnover Contagion: How Coworkers’ Job Embeddedness and Job Search Behaviors Influence Quitting,” Academy of Management Journal, Vol. 52 (2009), pp. 545-561. 8 The Center for Hospitality Research • Cornell University tion over time, change in work group satisfaction over time, responses per unit was 31 people (s.d. = 5.8). Their average and the dispersion of satisfaction within the work group. tenure was 98.6 weeks (s.d. = 87.7) at the beginning of our We then analyze the relationship of these measures with data collection; the average age was 41.8 years (s.d. = 13.9); actual turnover levels of the work groups we surveyed. We and 3,267 were male (62%). We did not find any significant make the argument that the more dispersion there is among difference between respondents and non-respondents in the workgroup’s attitudes, the less the coworkers’ attitudes terms of turnover rate, age, gender, race, or tenure. Accord- will influence an individual’s attitudes. On the other hand, ingly, non-response bias should not be a serious concern in if a group is cohesive in its beliefs, a more consistent and our study. influential message will be conveyed to individuals, whose Measures attitudes will gravitate toward those of the group. In sum, we sought to answer the following key ques- Individual job satisfaction change. To tap into the mul- tions: tidimensional nature of job satisfaction, respondents were asked to indicate on a 5-point scale the extent to which they • Do changes in an individual’s job satisfaction levels agreed with 20 items assessing satisfaction with ten aspects predict his or her leaving? of their job (e.g., pay, coworkers, promotion).9 Coefficient • Do changes in a work unit’s job satisfaction levels pre- alpha for job satisfaction was .93. We operationalized each dict an individual’s leaving? individual’s job satisfaction change across Phases 1, 2, and 3 as the Bayes slope estimate drawn from hierarchical • Does dispersion in unit members’ job satisfaction linear models. Ninety-eight percent of the employees in our changes influence the degree to which changes in a sample reported changes in job satisfaction. The averages of work unit’s job satisfaction levels affect an individual’s individual job satisfaction are 3.56 at Phase 1, 3.86 at Phase leaving? 2, and 4.37 at Phase 3. The average individual job satisfaction • Do work unit changes in job satisfaction levels influence change from Phase 1 to Phase 3 is .96. the overall rate of turnover in a unit? Unit-level job satisfaction change. Likewise, to calcu- late the unit-level satisfaction change we used hierarchical Method linear models and calculated this variable as the Bayes slope Sample and data collection. The study sample is composed estimate of each unit’s average job satisfaction change across of employees in 175 business units in a leading U.S. rec- Phases 1 to 3. Like the employees themselves, all surveyed reation and hospitality corporation, which operates golf units experienced job satisfaction change. The averages of courses, country clubs, private business and sports clubs, unit-level job satisfaction are 3.13 at Phase 1, 3.49 at Phase and resorts. The surveys were collected through both the 2, and 4.03 at Phase 3. The average unit-level job satisfaction internet and phone over a period of two years. The study change from Phase 1 to Phase 3 is .90. included four phases. In Phase 1, we invited all 14,981 Job satisfaction change dispersion. To calculate the employees within the 175 units to complete employee job dispersion of satisfaction, we measured the extent to which satisfaction questionnaires, along with a variety of other unit members differ in their job satisfaction change across measures. We received responses from 11,457 employees in Phases 1, 2, and 3. To do this we used Chan’s dispersion all 175 units, for a response rate of 76 percent. Six months composition model,10 and operationalized job satisfaction later, in Phase 2, we asked the 11,457 respondents to report change dispersion using the within-unit standard deviation their job satisfaction again. This time we received 9,079 re- in the individual job satisfaction change scores. sponses from the 175 units, for a response rate of 79 percent. Voluntary turnover. In addition to the turnover Another six months later, in Phase 3, we asked the remaining percentages, the organization provided a report containing 9,079 respondents to evaluate their job satisfaction a third identifying information for all departing employees for the time. The final response drew completed surveys from 5,270 12-month period between Phase 3 and Phase 4. “Stayers” employees across all 175 units, which is a response rate of were coded as 0, and voluntary leavers were coded as 1. 58 percent from Phase 2. This meant a respectable overall Control variables. Because our study involved changes response rate of 35 percent from the original sample in in satisfaction, we controlled for individuals’ average job terms of employees, and 100 percent of the 175 units. Finally, after another year, in Phase 4, each of the 175 units provided 9 P.E. Spector, “Measurement of Human Service Staff Satisfaction: Devel- us with voluntary-turnover data for the year just concluded opment of the Job Satisfaction Survey,” American Journal of Community (one year after the Phase 3 survey), and demographics Psychology, Vol. 13 (1985), pp. 693-713. for all employees. This allowed us to statistically compare 10 D. Chan. Functional Relations among Constructs in the Same Content respondents and non-respondents. The average number of Domain at Different Levels of Analysis: A Typology of Composition Mod-els,” Journal of Applied Psychology, Vol. 83 (1998), pp. 234-246. Cornell Hospitality Report • August 2012 • www.chr.cornell.edu 9 satisfaction levels at Phases 1, 2, and 3, and also demo- turnover rate is stronger when there is more cohesion in job graphic variables such as age, gender, race, and tenure with satisfaction change. the organization. These controls were needed to rule out the Multilevel three-way interaction. We found a signifi- influences on our findings of employees’ static levels of job cant multilevel three-way interactive effect on individual satisfaction, life experiences, social categories, and career turnover of unit-level job satisfaction change, job satisfac- progress.11 Likewise, at the unit level, we controlled for aver- tion change dispersion, and individual job satisfaction age levels of unit-level job satisfaction at phases 1 through 3, change (γ = 1.27, p < .05). We then compared the four con- as well as average age, gender, race, and tenure of employees. ditions noted below with each other, using slope difference In addition, since the units are located in different regions of tests, as follows: 13 the U.S. and perceived alternatives is a significant precursor (1) High satisfaction dispersion change and negative unit for turnover,12 we controlled for the local unemployment level satisfaction change; rate for each unit. The data were obtained from the Bureau of Labor Statistics for each ZIP code where a unit is located. (2) High satisfaction dispersion change and positive unit Analyses. We first examined the unique effects of unit- level satisfaction change; level and individual job satisfaction change in individual and (3) Low satisfaction dispersion change and negative unit unit turnover. Then, we tested the interactive effect of unit- level satisfaction change; and level job satisfaction change and dispersion in this change on unit turnover. Finally, we examined a possible multilevel (4) Low satisfaction dispersion change and positive unit level three-way interactive effect of unit-level job satisfaction satisfaction change. change, job satisfaction change dispersion, and individual Thus, we compared conditions 3 and 1, 3 and 2, 3 and 4, 4 job satisfaction change on individual turnover. Both hi- and 1, 4 and 2, and 1 and 2. erarchical generalized linear modeling and ordinary least The results revealed that the low satisfaction-dispersion- squares regression were used in conducting the analyses. change effect (that is, high concentration, shown in cases Results 3 and 4) overshadowed the high satisfaction-dispersion Main effects. We found that both individual job satisfac- change in every comparison. In the low dispersion condition tion change (γ = -.17, p < .01) and unit-level job satisfaction 3, a situation where the environment is deteriorating, posi- change (γ = -1. 21, p < .01) were significantly related to tive change in an individual’s job satisfaction is least likely to individual turnover. As people become more satisfied with prevent individual turnover. However, in the low dispersion their job and overall satisfaction in the unit increases over condition 4, an increasingly positive environment, we found time, fewer individuals leave their jobs. Unit-level job satis- that positive change in individual job satisfaction is most faction change (b = -.09, p < .01) was significantly associated likely to prevent individual turnover. These findings show with the overall turnover rate in a unit. Units with increases the critical influence of uniform contextual cues (in this case, in job satisfaction subsequently have less turnover, and, of increased satisfaction) on individual attitudes and behav- course, the reverse is also true. iors. We also learned that a comparison of high dispersion Interactions. The interaction between unit-level job conditions 1 and 2 revealed no significant difference in the satisfaction change and dispersion of job satisfaction change strength of the relationship between individual job satisfac- was significantly related to the overall turnover rate in a unit tion change and individual turnover. This suggests that high (b = .42, p < .05). When the dispersion of job satisfaction dispersion in unit job satisfaction change tends to cancel out change was low (that is, convergence was high), positive the impact of unit job satisfaction change (in either direc- unit-level job satisfaction change was more negatively tion) on individuals’ attitudes and behaviors. related to the overall turnover rate in a unit (b Taking the above findings, our research shows that = -.11, p < .01) compared to when job satisfaction change dispersion was managers need to pay attention to the change in job satisfac- high (b = -.07, p < .01). That is, the strength of the relation- tion. Beyond static (average) levels of job satisfaction across ship between unit-level job satisfaction change and unit three points in time at both the unit and individual levels, unit-level and individual-level job satisfaction change have 11 Chen et al., op.cit.; B. Holtom, T. Mitchell, T. Lee, and M. Eberly, “Turn- unique multilevel influences on individual turnover. Fur- over and Retention Research: A Glance at the Past, A Closer Review of the ther, there is a negative effect of unit-level job satisfaction Present, and a Venture into the Future,” Academy of Management Annals, change on the overall turnover rate in a unit after control- Vol. 2 (2008), pp. 231-274; and J.G. March and H.A. Simon, Organizations (New York: John Wiley, 1958). 12 13 C. O. Trevor. Interactions among ease-of-movement determinants J.F. Dawson and W. Richter, “Probing Three-way Interactions in and job satisfaction in the prediction of voluntary turnover. Academy of Moderated Multiple Regression: Development and Application of a Slope Management Journal, 44 (2001), 621-638. Difference Test,” Journal of Applied Psychology, Vol. 4 (2006), pp. 917-926. 10 The Center for Hospitality Research • Cornell University ling for unit-level static (average) job satisfaction. That is, concentrated satisfaction improvement reduces individuals’ turnover. As a third point, remembering that we measured actual turnover and not just intent, our multilevel data also substantiate an integrative three-way interaction model, which highlights that if members in a unit experience an overall increase in job satisfaction and their job satisfaction change levels are similar in strength, an individual’s job satis- faction improvement exerts the strongest negative influence The work unit can have on his or her turnover. Finally, the negative relationship between unit-level job satisfaction change and turnover was a significant impact on mitigated by job satisfaction change dispersion. individual satisfaction and Discussion and Implications While we still advocate tracking employee satisfaction levels turnover. at any given point in time, identifying trends in and the direction of satisfaction changes is particularly useful in pre- dicting and reducing turnover. Moreover, our study shows the influence of “the office.” Individuals are paying attention to and are influenced by the attitudes and behaviors of their co-workers. The work unit can have a significant impact on individual satisfaction and turnover, both positively and negatively. So as the convergence of unit satisfaction in- creases (or dispersion diminishes), coupled with a change in unit satisfaction, the greater will be the impact on changes in individual satisfaction and turnover. When cohesive unit satisfaction increases, turnover diminishes, and the reverse is also the case. This can be thought of as a contagion effect, which has been shown to exist in other contexts.14 Turning to an examination of the three-way interactive effect of individual job satisfaction change, unit-level job satisfaction change, and job satisfaction change dispersion on individual turnover, we were able to tease out which combination of contextual factors is most suited for bring- ing out the effect of individual job satisfaction change on turnover. We found that growth in individual job satisfac- tion is most likely to prevent individual turnover when unit members experienced a uniform increase in their job satisfaction. This finding confirms the value of having high consistency between personal and contextual stimuli in trig- gering one’s behavioral reactions.15 That is, when contextual cues (e.g., uniform job satisfaction change experienced by members within a business unit) are in alignment with one’s personal cues (individual job satisfaction change), one is in- clined to attach more importance to one’s personal cues and allow it to shape one’s behavioral responses. Interestingly, we 14 Felps et al., op. cit. 15 H. Blanton, “Evaluating the Self in the Context of Another: The Three- selves Model of Social Comparison Assimilation and Contrast,” in Cogni- tive social psychology: The Princeton symposium on the legacy and future of social cognition, ed. G. B. Moskowitz (Mahwah, NJ: Erlbaum 2001), pp. 75-87; and J. Thomas and R. Griffin, op.cit. Cornell Hospitality Report • August 2012 • www.chr.cornell.edu 11 also detected that when unit members uniformly encounter a decrease in job satisfaction, even when an employee’s job satisfaction is growing, that satisfaction improvement will be less likely to prevent him or her from leaving (condition 3 above) than in any of the other three conditions we tested. Another interesting finding is that in the presence of high job satisfaction change dispersion (that is, little conver- gence), regardless of the general direction of unit-level job The effects of work unit satisfaction change (positive or negative), the strength of the satisfaction on individuals relationship between individual job satisfaction change and turnover remain constant. This result highlights the fact that may explain why “happy” as the cues from organizational context become increas-ingly diverse or inconsistent, the influence of organizational employees leave. context diminishes. The effects of dispersion (or convergence) mean that hu- man resource professionals need to pay attention not only to the average levels of employee satisfaction, but also the vari- ance among employees. The worst possible scenario in terms of turnover likelihood is when unit levels of satisfaction are declining and there is little variance within the group. In this scenario even satisfied employees may be looking for an exit strategy. If levels of satisfaction can be assessed at more frequent intervals than the normal annual survey, thereby generating a job satisfaction trajectory measure, a much more accurate picture of the dynamics of the organization will be revealed. The effects of work unit satisfaction on individuals may explain why “happy” employees leave. In the face of a work unit full of negativity even those who are satisfied with their job may be ready to leave. For the employer this is doubly dangerous. First, you lose a satisfied worker, and, second, the more talented the individual, the easier it will be for that person to leave. Thus, the employer may well be left with unhappy, mediocre workers. One other implication of our study is that managers should be particularly careful about the initial assignments of new employees or even internships with respect to the prevailing attitudes (level and variance) of various work groups.16 Placing a new employee into a work unit whose satisfaction is generally consistent and declining could have serious negative implications for the newcomer. Managers also need to be especially aware of factors that influence group attitudes such as perceived equity17 and organiza- tional justice.18 As we learn from this study, the attitudes of the group can have a strong impact on the attitudes and behavior of individuals. n 16 J. Thomas and R Griffin, op.cit. 17 J.S. Adams, “Inequity in Social Exchange, in Advances in Experimental Social Psychology, Vol. 2, ed. L. Berkowitz (New York: Academic Press, 1965), pp. 267-299. 18 J. Colquitt, R. Noe, and C. Justice, “In Teams: Antecedents and Con- sequences of Procedural Justice Climate,” Personnel Psychology, Vol. 55 (2001), pp. 83-110. 12 The Center for Hospitality Research • Cornell University 0th21992 - 2012 ANNIVERSARY Celebrating 20 Years of Hospitality Research Download our free research at: www.chr.cornell.edu 489 Statler Hall · Ithaca, NY 14853 hosp_research@cornell.edu · 607-255-9780 Senior Partners ASAE Foundation, Carlson Hotels, Hilton Worldwide, National Restaurant Association, SAS, STR, and Taj Hotels Resorts and Palaces Partners Davis & Gilbert LLP, Deloitte & Touche USA LLP, Denihan Hospitality Group, eCornell & Executive Education, Expedia, Inc., Forbes Travel Guide, Four Seasons Hotels and Resorts, Fox Rothschild LLP, French Quarter Holdings, Inc., HVS, Hyatt Hotels Corporation, InterContinental Hotels Group, Jumeirah Group, LRP Publications, Maritz, Marriott International, Inc., Marsh’s Hospitality Practice, McDonald’s USA, newBrandAnalytics, priceline.com, PricewaterhouseCoopers, Proskauer, ReviewPro, Sabre Hospitality Solutions, Sathguru Management Consultants (P) Ltd., Schneider Electric, Thayer Lodging Group, Thompson Hotels, Travelport, WATG and Wyndham Hotel Group Friends 4Hoteliers.com • Berkshire Healthcare • Center for Advanced Retail Technology • Cleverdis • Complete Seating • Cruise Industry News • DK Shifflet & Associates • ehotelier.com • EyeforTravel • Gerencia de Hoteles & Restaurantes • Global Hospitality Resources • Hospitality Financial and Technology Professionals (HFTP) • hospitalityInside.com • hospitalitynet. org • Hospitality Technology Magazine • HotelExecutive.com • International CHRIE • International Hotel Conference • International Society of Hospitality Consultants (ISHC) • iPerceptions • JDA Software Group, Inc. • J.D. 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