Striving for Balance [1 ed.] 9781681233062, 9781681233048

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Striving for Balance

A Volume in Research in Careers Series Editors S. Gayle Baugh, University of West Florida Sherry E. Sullivan, Bowling Green State University

Research in Careers S. Gayle Baugh and Sherry E. Sullivan, Series Editors Striving for Balance (2016) edited by S. Gayle Baugh and Sherry E. Sullivan Searching for Authenticity (2015) edited by S. Gayle Baugh and Sherry E. Sullivan Maintaining Focus, Energy, and Options Over the Career (2009) edited by S. Gayle Baugh and Sherry E. Sullivan

Striving for Balance Edited by

S. Gayle Baugh

University of West Florida and

Sherry E. Sullivan Bowling Green State University

INFORMATION AGE PUBLISHING, INC. Charlotte, NC • www.infoagepub.com

Library of Congress Cataloging-in-Publication Data

Names: Baugh, S. Gayle, editor. | Sullivan, Sherry E., 1961- editor. Title: Striving for balance / edited by S. Gayle Baugh and Sherry E. Sullivan. Description: Charlotte, NC : Information Age Publishing Inc., [2016] | Series: Research in careers Identifiers: LCCN 2015036089| ISBN 978-1-68123-304-8 (paperback) | ISBN 978-1-68123-305-5 (hardcover) | ISBN 978-1-68123-306-2 (ebook) Subjects: LCSH: Work and family. | Work-life balance. Classification: LCC HD4904.25 .S865 2016 | DDC 306.3/6--dc23 LC record available at http://lccn.loc.gov/2015036089

Copyright © 2016 I nformation Age Publishing Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the publisher. Printed in the United States of America

Contents Introduction to Research in Careers Series................................................. vii Introduction to the Volume....................................................................... xi 1. Beyond Policy Adoption: Factors Influencing Organizational Support for Reduced-Load Work Arrangements Alyssa Friede Westring, Ellen Ernst Kossek, Shaun Pichler, and Ann Marie Ryan.................................................... 1 2. Do Organizational Efforts to Help Employees Achieve Balance Matter? An Empirical Study of Organizational Support Initiatives on Worker Attitudes Yasmin S. Purohit, Claire A. Simmers, Sherry E. Sullivan, and S. Gayle Baugh............................................. 25 3. Can Managers of Every Generation Have It All? Examining The Relationship Between Work-Life Balance and Promotability for Baby Boomers and Generation X Sarah A. Stawiski, William A. Gentry, and Lisa E. Baranik................. 47 4. A Qualitative Exploration of Reactions to Work-Life Conflict Events Elizabeth M. Boyd, Jessica Keeney, Ruchi Sinha, and Ann Marie Ryans.................................................... 73



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5. Spillover and Crossover Processes: Consequences for Work-Life Balance Hetty van Emmerik, Arnold B. Bakker, Mina Westman, and Maria C. W. Peeters............................................ 97 6. Beyond Conflict and Enrichment: A Review, Reconceptualization and Research Agenda for Studying Work-Family Balance Christine D. Bataille..........................................................................113 About the Authors................................................................................... 135

Introduction to the Research in Careers Series Welcome to volume three of Research in Careers! This series is designed in five volumes to provide scholars a unique forum to examine careers issues in today’s changing, global workplace. What makes this series unique is that the volumes are connected by the use of Mainiero and Sullivan’s (2006) kaleidoscope career model (KCM) as the organizing framework and the theme underlying the volumes. To understand how this series is organized requires a brief overview of the KCM (Mainiero & Sullivan, 2005). Just as rotating the tube of the kaleidoscope produces changing patterns when its glass chips fall into new arrangements, individuals change the patterns of their career by rotating the varied aspects of their life in order to arrange their relationships and roles in new ways. Individuals evaluate the choices and options available through the lens of the kaleidoscope to determine the best fit among work opportunities, constraints, and demands as well as relationships and personal values and interests. It is a dynamic model; each decision an individual makes will affect his or her kaleidoscope career pattern. Like a kaleidoscope, which uses three mirrors to create an infinite number of patterns, individuals focus on three key parameters when making decisions, thus creating the kaleidoscope pattern of their career. These key parameters are (a) authenticity, whereby the individual’s internal values are aligned with his or her external behaviors; (b) balance, such that

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the individual strives to reach an equilibrium between personal investments in work and nonwork pursuits; and (c) challenge, which is an individual’s need for stimulating work (e.g., responsibility, autonomy) as well as career advancement. Over the course of the life span, as a person searches for the fit that best matches the character and context of his or her life, the kaleidoscope’s parameters shift in response, with one parameter moving to the foreground and taking priority at that time. The other two parameters lessen in intensity and recede to the background, but are still present and active, as all three parameters are necessary to create the current pattern of an individual’s life/career (Sullivan, Forret, Carraher, & Mainiero, 2009). The KCM is based on the results of five different studies (interviews, focus groups, and three surveys) of over 3000 U.S. professionals (Mainiero & Sullivan, 2006). Other independent studies have also supported the basic tenets of the KCM (Cabrera, 2007, 2009; Godshalk, Nobel, & Line, 2007; Segers, Inceoglu, Vloeberghs, Bartram, & Henderickx, 2008; Smith-Ruig, 2009). Using the KCM as the foundation, we have organized the five volumes in this series to recognize the key points of the theory. The first volume, Maintaining Focus, Energy, and Options Over the Life Span, centers on how individuals enact their career and keep their career vital over the course of their life. The authors in volume one examined current theories and research within the context of change over the life span, while acknowledging potential obstacles to career growth, transitions to new career phases, and renewal. Volume two, Searching for Authenticity, focused on a person’s quest for authenticity, defined as an individual’s need to be genuine to himself or herself and to do meaningful work. Within the context of an organization, authenticity includes the need for one’s values to match the values of the employing firm. The authors in volume two, have examined the intrinsic enjoyment of one’s career, alternative career paths (especially those that are pursued “for love, not money”), and career changes and transitions that are made in order to pursue something more important than money. In this volume, Striving for Balance, we consider how individuals seek a healthy alignment between work and nonwork. In addition to building upon the established literature on work/family conflict, the chapters in this volume also examine the reciprocal positive influences between work and nonwork, considering such issues as balancing work with commitments to others, including spouse/partner, children, elderly relatives, friends, and the community. In the fourth volume we will focus on Seeking Challenge, looking at why individuals need simulating work, what work and nonwork factors influence challenge, and the role played by others (e.g., leaders, mentors), who may contribute to an individual’s career success. We will also explore how

Series Introduction  ix

employer and individual needs can be matched so as to produce both personal challenge and organizational profitability. In the fifth volume of the series we will examine “threats and opportunities.” The great opportunities offered by new career patterns as well as the possible losses and problems associated with nontraditional careers will be discussed. In this volume we will also look at how organizations are managing in this new work era and how nontraditional careers can be both a boon and a bane to them. In sum, each volume represents an in-depth examination of a major theme within the field of careers. As such, each is independent of the others, providing the reader with original and varying perspectives on that volume’s theme. Additionally, each volume will provide the novice and the established scholar alike with numerous ideas for future research. The five volume series, considered in its entirety, should provide the reader with a deeper understanding of the changing nature of careers as well as the factors that influence how individuals enact their careers within and outside of the context of organizations. By organizing the series using the framework of the KCM, we hope to provide a detailed and realistic examination of the increasingly complex nature of careers in the 21st century. References Cabrera, E. F. (2007). Opting out and opting in: Understanding the complexities of women’s career transitions. Career Development International, 12, 218–237. Cabrera, E. F. (2009). Protean organizations: Reshaping work and careers to retain female talent. Career Development International, 24, 186–201. Godshalk, V. M., Noble, A. M., & Line, C. (2007, August). High achieving women: an exploratory study of the differences between kaleidoscope career types. Paper presented at the annual meeting of the Academy of Management, Philadelphia, PA. Mainiero, L. A., & Sullivan, S. E. (2005). Kaleidoscope careers: An alternative explanation for the opt-out generation. Academy of Management Executive, 19(1), 106–123. Mainiero, L. A., & Sullivan, S. E. (2006). The opt-out revolt: How people are creating kaleidoscope careers outside of companies. New York, NY: Davies-Black. Segers, J., Inceoglu, I., Vloeberghs, D., Bartram, D., & Henderickx, E. (2008). Protean and boundaryless careers: A study on potential motivators. Journal of Vocational Behavior, 73, 212–230. Smith-Ruig, T. (2009). Mapping the career journey of accountants in Australia. In S. G. Baugh & S. E. Sullivan (Eds.), Research in careers (pp. 163–196), Charlotte, NC: Information Age. Sullivan, S. E., Forret, M. L., Carraher, S. M., & Mainiero, L. A. (2009). Using the kaleidoscope career model to examine generational differences in work attitudes. Career Development International, 14, 284–302.

Introduction to the Volume Although there has been a great deal of research on the topic of worknonwork balance, workers today face many different obstacles in striving for balance than in previous decades (Sullivan & Baruch, 2009). Rapidly evolving technology has blurred the boundaries between work and other aspects of life as laptops and smart phones tether employees to their work 24/7. For example, 58.8% of the participants of the American Life Panel reported working during their vacation, including checking their e-mail (40.2%), checking voice mail (22.1%), taking calls (23.9%) and doing the work they would normally be doing if in the office (12.3%) (Carman & Pollard, 2015). Increased globalization has brought changes to many businesses as well (Al Ariss, 2014; Carraher, & Welsh, 2015; Dickmann & Baruch, 2011; Reis & Baruch, 2013). For instance, brokers once traded on the New York Stock exchange from the ringing of the opening bell at 9:30 a.m. Eastern Standard Time (EST) to its close at 4:30 p.m. Today, New York brokers (and others around the world), are also trading on other markets, such as the Tokyo market which opens at 6:45 p.m. EST and the London market which opens at 3:00 a.m. EST. Even very traditional, slow-to-change industries are doing business much differently because of technology. For example, with over 6.7 million U.S. university students taking at least one course online (Allen & Seaman, 2013), professors who once taught undergraduate students in face-to-face traditional classroom settings are now responding to e-mails and interacting electronically with students around the clock.

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In addition to changes in technology and increased globalization, the workforce itself has also changed. What employees value and what they want from their employers and careers have changed. Research has documented how many employees want to be authentic in their career choices (Hall & Mao, 2015; Leroy, Verbruggen, Forrier, & Sels, 2015; Liu, Perrewé, & Magnusen, 2015; Murphy, & Volpe, E, 2015), with this quest for authenticity impacting college graduates as they make the transition from school to employment (Blenkinsopp, Scurry, and Hay, 2015) and employees in mid- and late-career as they make the transition to unemployment in the face of lay-offs (de Janasz & Kenworthy, 2015).). With more women, parents, and those caring for elderly relatives in the workplace than in previous decades as well as younger generations of employees who are “working to live not living to work,” individuals are looking for organizations that will support their chosen life style (Sullivan, Forret, Carraher, & Mainiero, 2009). A recent survey of 1,087 professional workers, however, reported that 45% perceived their work-life balance as lacking (Salomon, 2015). While some organizations are offering innovative programs to meet their employees’ need for balance, other organizations are still struggling to keep up with the changing work context. The chapters in this volume examine how individuals are striving for balance within the context of our changing workplace. Chapters 1 and 2 focus on macroissues surrounding work-nonwork balance, specifically studying the effectiveness of organizational policies. In Chapter 1, Westring, Kossek, Pichler, and Ryan explore if there is a gap between an organization’s adoption of work-nonwork policies and its offering of a supportive environment for the employees use of such policies. Surveying human resource managers from 46 North American companies that were early adopters of reduced-load work arrangements, they found organizations offering a greater number of work-nonwork policies were no more supportive of reduced-load work arrangements than firms offering fewer policies. Westring et al.’s study highlights the importance of organizations creating a supportive context for the implementation of policies to help employees achieve balance. In Chapter 2, Purohit, Simmers, Sullivan, and Baugh draw from social exchange theory and the compensation literature to examine how employees’ satisfaction with their organization’s discretionary (i.e., not legally required) support initiatives influences their work-related attitudes and personal well-being. They investigated the relationship between professional workers’ satisfaction with three types of discretionary benefits which support work-life balance—(a) time-related benefits, (b) career-related benefits, and (c) family-related benefits—and the three attitudes of job satisfaction, organizational commitment, and quality of work life using survey

Volume Introduction  xiii

data from 156 workers. The surprising results of Purohit et. al.’s study offer a number of avenues for future research in this understudied area. Chapters 3 and 4 examine balance from a microperspective, focusing on generational differences in balance as well as how individuals’ reactions to work-nonwork conflicts influence career outcomes. In Chapter 3, Stawiski, Gentry, and Baranik study balance using the lens of generational differences. Using assessment data collected from 664 managers who attended a Center for Creative Leadership development course, they explore the relationship between work-life balance and promotability for members of the Baby Boom generation and Gen X. Stawiski et al. found that a manager’s self-rating of work-nonwork balance was positively related to their boss’s rating of their promotability, regardless of which generation the manager or boss belonged. In Chapter 4, Boyd, Keeney, Sinha, and Ryan discuss their qualitative analysis of how 1,359 university alumni’s reactions to work-life conflict events shaped their career choices, including entry, participation, and attrition decisions. Their approach offers a different lens to examine work-life conflict for two reasons. First, instead of relying on global assessments of work-life conflict, they studied reactions to specific conflicts, which occur on a daily basis. Second, they examined conflicts across multiple domains, including education, health, leisure, friendships, romantic relationships, family, household management, and community involvement. Scholars researching balance should consider Boyd et al.’s methodology when designing their own studies. Chapters 5 and 6 provide two perspectives on where scholars should focus their future research efforts in studying work-nonwork balance. In Chapter 5, van Emmerik, Bakker, Westman, and Peeters provide a conceptual examination of the processes that affect work-family conflict, family-work conflict, and the overall resulting work-nonwork balance or imbalance. They focus specifically on two transmission processes: (a) spillover, defined as an event in either an individual’s work or the home domain has consequences for the other domain, and (b) crossover, defined as the bidirectional transmissions of positive and negative affect between intimately connected persons (e.g., significant others, family members, important work associates). In Chapter 6, Bataille reviews the work-family literature, a task made more difficult by the wide range of conceptualizations, measures, and approaches used to study balance. Based upon her extensive review of the literature, she offers a multidimensional definition of work-family balance and develops a framework, which recognizes the dominant dimensions of work-family balance. Bataille’s and van Emmerik et al.’s chapters provide scholars with fresh and compelling insights into the study of work-nonwork balance.

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In sum, the chapters in this volume provide an in-depth examination of what balance is, how balance impacts individual career decisions, attitudes and outcomes, and what organizations should do to more effectively help their employees achieve balance. We thank each of the authors for contributing his or her interesting scholarship to this volume. In addition to thanking the authors, we would like to recognize a number of other individuals who contributed to making this volume possible. First, we thank our anonymous reviewers who offered meaningful, developmental feedback that the authors used to further enhance their research. Each chapter was blind-reviewed by at least two independent reviewers. Second, we express great appreciation and thanks to our publisher George Johnson. He recognized the value of research on the changing nature of careers and provided tremendous support, understanding, and guidance to us throughout the publishing process for volume three. Third, we thank you, our readers. We hope this volume increases your understanding of careers and provides you with ideas to fuel your own research and enhance your own search for balance. References Allen, E. I, & Seaman, J. 2013. Changing course: Ten years of tracking online education in the United States. Retrieved from http://www.onlinelearningsurvey. com/reports/changingcourse Al Ariss, A. (2014). Global talent management: Challenges, strategies, and opportunities. New York, NY: Springer Science & Business. Blenkinsopp, B., Scurry, T., & Hay, A. (2015). Exploring issues of underemployment and authenticity in early career. In S. G. Baugh & S. E. Sullivan (Eds.), Searching for authenticity (pp. 43–65 ). Charlotte, NC: Information Age Publishing. Carraher, S. M., & Welsh, D. H. (2015). Global entrepreneurship. New York, NY: Kendall Hunt. Carman, K. G., & Pollard, M. (2015). The great American working vacation. Retrieved from http://www.newsweek.com/great-american-working-vacation-300463 de Janasz, S. C., & Kenworthy. A. L. (2015). Toward authenticity or defeat: The Jolting effect 0f layoff. In S. G. Baugh & S. E. Sullivan (Eds.), Searching for authenticity (pp. 67–88), Charlotte, NC: Information Age Publishing. Dickmann, M., & Baruch, Y. (2011). Global careers. New York, NY: Routledge. Hall, D. T., & Mao, J. (2015). Exploring authenticity in careers: Implications for research and practice. In S. G. Baugh & S. E. Sullivan (Eds.), Searching for authenticity (pp. 1–23). Charlotte, NC: Information Age Publishing. Leroy, H., Verbruggen, M., Forrier, A., & Sels, L. (2015). Career authenticity: On being true to oneself at work. In S. G. Baugh & S.E. Sullivan (Eds.), Searching for authenticity (pp. 25–41). Charlotte, NC: Information Age Publishing. Liu, Y., Perrewé, P. L., & Magnusen, M. (2015). Selling your soul to the devil: Political behavior, the pursuit (or discard) of authenticity, and career success.

Volume Introduction  xv In S. G. Baugh & S.E. Sullivan (Eds.), Searching for authenticity (pp. 25–41). Charlotte, NC: Information Age Publishing. Murphy, W. M., & Volpe, E. H. (2015). Enacting authentic careers: An toward authenticity or defeat: The jolting effect of layoff. In S. G. Baugh & S. E. Sullivan (Eds.), Searching for authenticity (pp. 89–109). Charlotte, NC: Information Age Publishing. Reis, C., & Baruch, Y. (2013). Careers without borders: Critical perspectives. New York, NY: Routledge. Salomon, S. (2015). Employers, employees don’t agree on work-life balance. Retrieved from http://www.boston.com/jobs/news/2015/02/04/study-employersemployees-don-agree-work-life-balance/OqUBVdR1K4V7YYqP2T174O/ story.html Sullivan, S. E., & Baruch, Y. (2009). Advances in career theory and research: A critical review and agenda for future exploration. Journal of Management, 35, 1542–1571. Sullivan, S. E., Forret, M. L., Carraher, S. M., & Mainiero, L. A. (2009). Using the kaleidoscope career model to examine generational differences in work attitudes. Career Development International, 14, 284–302.

chapter 1

Beyond Policy Adoption Factors Influencing Organizational Support for Reduced-Load Work Arrangements Alyssa Friede Westring, Ellen Ernst Kossek, Shaun Pichler, and Ann Marie Ryan

Work-life policies (e.g., flextime, telework, reduced-load work) have become a commonplace feature in the portfolio of human resource (HR) offerings in the majority of organizations today (Matos & Galinsky, 2012). For instance, according to the 2012 National Study of Employers, 77% of U.S. companies allowed flextime options and 63% offered telework options for at least some of their employees (Matos & Galinsky, 2012). Several strategic reasons for the adoption of such policies have been noted, including compliance with legal regulations, enhanced employee commitment, ability to attract as well as retain a diverse workforce, being seen as an employer of choice, and fostering employee well-being (Kossek & Friede, 2006). Despite the proliferation of these policies and their intended benefits, there is mounting evidence that having these policies on the books may not be sufficient to fully address employee and organizational needs. The organizational context in which these policies are offered may inhibit or enhance their effectiveness (Allen, 2001; Eaton, 2003; Ryan & Kossek,

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2008; Thompson, Beauvais, & Lyness, 1999). Therefore, the purpose of this chapter is to examine the relative and interactive effects of factors that may influence the supportiveness of an organizational context for implementing a work-life policy. To explore this issue, we focus on a particular work-life policy: the reduced-load work arrangement (RLWA), which is important for advancing understanding of contextual influences on policy implementation (Kossek, Ollier-Malaterre, Lee, Pichler, & Hall, in press). RLWAs are defined as a reduction in work load for a commensurate decrease in salary (Lee, MacDermid, Williams, Buck, & Leiba-O’Sullivan, 2002). In a sample of early-adopting organizations that all offer some form of RLWA, we investigate organizational support for policy usage. Thus, our sample can be viewed as employers who were trying to be innovative and rapidly respond to the changing labor market when the need to attract and retain women was becoming a major corporate issue in the past 10 to 15 years. We explore other features of the organizational environment that enhance or impede organizational support for use of RLWAs. The remainder of the chapter is organized as follows. First, we describe RLWAs in greater detail and discuss evidence regarding the effectiveness of RLWAs in meeting employee and organizational goals. In particular, we highlight several facets of organizational support for RLWAs. We then describe factors that are expected to influence organizational support for RLWAs. To support our arguments, we present an empirical study of 46 organizations that offer RLWAs and present our findings regarding those factors that contribute to support for RLWAs. Reduced-Load Work Arrangements Following Lee et al. (2002), we employ the term reduced-load to highlight the fact that not only are work hours reduced in this policy, but so are the total responsibilities assigned to that employee. However, terms such as part-time and reduced-time may also refer to instances when employees work less than full-time. RLWAs have been used by employees at all organizational levels, including senior managers and high-level professionals (Lee et al., 2002). RLWAs may be negotiated on a short-term or long-term basis and users are often able to maintain full benefits for the duration of their arrangement. According to the 2012 National Study of Employers, 41% of companies allow at least some employees to “move from full-time to parttime work and back again while remaining in the same position or level” (Matos & Galinsky, 2012, p. 14). Organizations vary in their approach to offering RLWAs. Some organizations reluctantly allow RLWAs under special circumstances to retain exceptional employees (i.e., an accommodation

Beyond Policy Adoption   3

culture), whereas others embrace it as a new and valuable way of working (i.e., a transformation culture; Lee, MacDermid, & Buck, 2000). RLWAs are unique from many other work-life policies because they actually change the amount of work completed, which is a critical form of preventing work-family conflict for professionals who often face rising workloads, work intensification, and overwork as their key challenges (Kossek, Valcour, & Lirio, 2014). This type of policy is distinct from other policies that only change when and where work is completed (e.g., telework, flextime), but maintain the same level of workload (Kossek et al., in press). As such, RLWAs are a particularly interesting policy to explore because they challenge the professional career cultures and talent management systems that are based on the hegemony of habitually placing career over personal life, where long hours are needed to advance the corporate ladder (Wharton & Blair-Loy, 2002). In essence, RLWAs are a key strategy for promoting a sustainable workforce as they allow employees to pursue career success while sustaining personal and family well-being (Hall, Lee, Kossek, & Las Heras, 2012). Although it may seem counterintuitive for organizations to support RLWAs in times of global economic distress and increasing competition, such policies may help organizations recruit and retain top talent. In particular, the retention of older workers and high talent women professionals (who might otherwise leave the workforce) may be enhanced through RLWAs (Kossek & Lee, 2008). RLWAs also can be an effective way to manage labor costs, use staff effectively, and motivate workers who want not only to have a career, but also to devote time to other life interests from family life to volunteering to being involved in the community or church (Hall, Kossek, Briscoe, Pichler, & Lee, 2013). Overall, additional research is warranted to improve our understanding of this relatively underutilized career management practice. Effectiveness Research to empirically determine the effectiveness of work-life policies suffers from several shortcomings that make drawing conclusions about their effectiveness elusive (Kelly et al., 2008). For example, outcomes that scholars can use to define effectiveness can range from organizational-level return on investment (ROI) to individual-level work-family conflict or job satisfaction. Further, some studies explore the impact of policy availability, whereas others focus on policy usage (Kossek, 2005). Overall, research on the effectiveness of work-life policies is quite limited and, in the cases where evidence exists, the results are mixed (Ryan & Kossek, 2008).

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With regard to RLWAs, more specifically, our ability to draw conclusions regarding the effectiveness of these policies is thwarted by the same limitations described above: varied definitions of effectiveness and variance in whether policy availability or use is the target of study. Further, researchers often study work-life policies in “bundles,” thus making it difficult to extract the unique effects of RLWAs (Perry-Smith & Blum, 2000). Evidence is growing that employees utilizing RLWAs are at least as effective as their full-load counterparts, particularly when managers and employees both benefit from the arrangement. For example, Kossek and colleagues (in press) have found that managers think employers benefit from RLWAs when used as a talent management tool with high performers, in conducive jobs and with employees who are flexible on using this form of flexibility—that is, willing to give and take with the organization to ensure work gets done. Several other longitudinal studies of reducedload employees found that the majority experienced both personal and professional success as a result of their RLWA (Lee et al., 2006; Hall et al., 2013). The 2006 study showed that over one third of reduced-load participants had been promoted while working a reduced-load and another third of the sample was expected by the supervisor to be promoted within the year. Further, over 90% of the sample reported a positive impact of the RLWA on their children, felt more satisfied with their balance between work and life, and were happier (Lee et al. 2006). However, in a meta-analysis comparing full-time and part-time workers, Thorsteinson (2003) found no significant differences between these groups in terms of their job satisfaction, organizational commitment. or intention to turn over. This lack of differences was true for both professional and nonprofessional employees (Thorsteinson, 2003). Supportive Context for RLWAs Despite these limited and mixed findings regarding the impact of work-life policies, there is ample evidence that they are more likely to be effective when implemented within a supportive organizational context (Allen, 2001; Anderson, Coffey, & Byerly, 2002; Hammer, Kossek, Yragui, Bodner, & Hansen, 2009; Kossek, Lewis, & Hammer, 2010; Ryan & Kossek, 2008; Thompson et al., 1999). In other words, when work-life policies are only offered to present a “family friendly” image of the organization, but are not offered in a context of support, they are less likely to be effective (Blair-Loy & Wharton; 2002; Konrad & Linnehan, 1995). Effective implementation of RLWAs requires deep integration into the strategic and social fabric of organizational life.

Beyond Policy Adoption   5

Scholars have identified several factors that constitute a supportive context for the implementation of work-life policies (Allen, 2001; Kossek et al., 2010; Kossek, Pichler, Bodner, & Hammer, 2011. First and foremost, a supportive organizational environment is inclusive and fair in its communication of work-life policies, access to policies, and negotiation of policies (Ryan & Kossek, 2008). Additionally, when organizations support the use of work-life policies, employees will not fear a career backlash for utilizing such policies (Eaton, 2003). In other words, when organizations support their work-life policies, employees can experience career development and promotion while utilizing them (Anderson et al., 2002; Eaton, 2003; Ryan & Kossek, 2008; Thompson et al., 1999). Further, in a supportive context, leadership, human resource managers, and supervisors are all informed and aligned to support the use of work-life policies in the ways described above (Anderson et al., 2002; Hammer et al., 2009; Kossek et al., 2011; Thomas & Ganster, 1995; Thompson et al., 1999). With regard to RLWAs, there is reason to expect that those factors that constitute a supportive context for work-life policies in general are important for the success of RLWAs. Note that Friede, Kossek, Lee, and MacDermid (2008) analyzed the HR manager perspective on the factors that are critical for the success of RLWAs. The HR managers in the Friede et al. study cited the importance of several of the factors described above, including organizational communication, support from top leadership, and fairness in the negotiation and evaluation of the arrangement (Friede et al., 2008). Lirio, Lee, Williams, Haugen, and Kossek (2008) analyzed the managerial perspective on the factors impacting the success of RLWAs (using data from the same larger study as Friede et al. (2008). Their results highlight the importance of managers in the creation and maintenance of an inclusive and supportive organizational context for reduced-load workers. In some cases, managers played the role of “defending” employees against a broader, unsupportive organizational environment. A more recent study on RLWAs, Kossek and colleagues (in press) found that RLWAs were seen as more effective when the organizational context included strategic support from senior managers, low career penalties for utilizing arrangements, adaptive HR structures and systems to support policy usage, and relatively few organizational silos for access. In sum, research does suggest that the effectiveness of work-life policies and of RLWA, specifically, is affected by the supportiveness of the organizational context. If organizations implement a work-life policy such as RLWA, they certainly would do so with the goal of having it as an effective practice. Thus, one may wonder why they might not have a supportive organizational environment for policy implementation. In the next section, we discuss what influences whether the organizational context is supportive.

6  A. F. Westring, E. E. Kossek, S. Pichler and A. M. Ryan

Predictors of a Supportive Context for RLWAs One of the main purposes of this chapter is to identify aspects of the organization that influence whether employers provide a supportive context for the implementation of RLWAs. Below, we discuss two critical organizational factors that are expected to influence the degree of support for RLWAs: organizational commitment to human resource management (HRM), in general, and organizational commitment to work-life management (WLM), more specifically. These macro HR factors have had relatively limited empirical investigation, despite their obvious importance for implementation of new ways of working. Organizational Commitment to HRM When organizations view their employees as a rare and valuable source of competitive advantage, they are more likely to be committed to the adoption of human resource management (HRM) practices that treat them as such (Becker & Gerhart, 1996). These high performance work practices include performance-based pay, team-based work design, training, and employee participation (Becker & Gerhart, 1996; Combs, Liu, Hall, & Ketchen, 2006; Huselid, Jackson, & Schuler, 1997). Such practices have been shown to have a positive impact on overall organizational performance, particularly when bundled together (Combs et al., 2006; Subramony, 2009). It is important to note that we define commitment to HRM as a strategically embedded commitment to the implementation of impactful human resource practices, as opposed to simply having the policies on the books for appearance or legal reasons. Less is known about the relationship between an organization’s commitment to HRM and its approach to work-life policies (Batt & Valcour, 2003). A study by Berg, Kalleberg, and Appelbaum (2003) found that a commitment to HRM increased perceptions of work-family support among a pooled sample of workers in the steel, apparel, and medical electronics industries. Using national survey data collected in Britain, White, Hill, McGovern, Mills, and Smeaton (2003) found decreased negative work-family spillover in organizations committed to HRM. In a sample of white-collar, dual-earner couples, Batt and Valcour (2003) found that autonomy in decision making (a “high performance” HRM practice) was significantly related to perceptions of work-family support. Given the strategic perspective underlying “high performance” HRM practices, we expect that organizations that place a high value on their employees would be more likely to provide a supportive environment for the use of RLWAs. Therefore, we propose that:

Beyond Policy Adoption   7

Hypothesis 1. Organizations that are more committed to human resource management will provide a more supportive context for reduced-load work arrangements. Organizational Commitment to Work-Life Management While most organizations claim a commitment to employee work-life balance, not all organizations are deeply committed to supporting the work-life needs of employees. Some organizations may simply have the policies on the books to provide an image of family friendliness, but there is no deeper strategic integration of work-life issues into the vision or culture of the organization. Therefore, we define organizational commitment to work-life management (WLM) as the extent to which the organization has a deep cultural and strategic commitment to meeting employee work-life needs. Similar to the previously discussed definition of commitment to HRM, this construct is differentiated from an organizational approach to WLM that offers work-life policies is name only, but is not fully committed to their implementation. Implicit in our definition is the assumption that supporting employee work-life needs will benefit multiple constituencies, such as different employee groups as well as the organization (Kossek et al., in press; Kossek, 1989; Tsui & Milkovitch, 1987). In organizations with a high commitment to WLM, work-life policies are treated as an important component of talent management and as a way to show that management places a high value on its workforce (cf. Lobel & Kossek, 1996; Kossek et al., 2010). We expect that such organizations will provide a more supportive context for the RLWAs that they offer. Hypothesis 2: Organizations that are more committed to work-life management will provide more supportive contexts for reduced-load work arrangements. Relationship Between Factors Influencing Support for RLWAs Although we expect that when organizations are committed to HRM and, more specifically, to work-life management, they will offer a more supportive context for RLWAs, it is unclear whether commitment to HRM versus work-life management will differentially or interactively contribute to the creation of a supportive context for RLWAs. For example, a commitment to HRM may be more important for organizations without a strong record of commitment to work-life initiatives and practices in order to successfully

8  A. F. Westring, E. E. Kossek, S. Pichler and A. M. Ryan

implement any work-life policy. Alternatively, it is possible that a high commitment to WLM may be sufficient to create a supportive context for RLWAs, even in instances when the organization does not show a broader commitment to high performance HRM. To investigate this issue in greater depth, we therefore propose the following two exploratory research questions. Research Question 1: What is the relative impact of commitment to HRM and commitment to WLM on the extent to which organizations provide a supportive context for RLWAs? Research Question 2: Is there an interaction between commitment to HRM and commitment to WLM in the extent to which organizations provide a supportive context for RLWAs? Method Procedure Target organizations were identified by their representation in at least one of the following categories: previous participation in an Alfred P. Sloan Foundation Study on reduced-load work, recognition in the “2004 Working Mother” list, commendation by the National Association for Female Executives (NAFE), membership in the Boston College Work Family Roundtable, representation on the Michigan State University School of Labor & Industrial Relations Human Resources Advisory Board, or membership in the College and University and Work and Family Association group. A total of 108 organizations were contacted for participation in the study among which 56 (52%) attempted the survey. Some firms were not included in the final analyses if more than a third of their data were missing. All organizations with missing data were contacted several times by phone and e-mail to complete the survey. All participants were assured that the answers they provided would not be directly linked back to themselves or their organization. Within each target organization, we identified a high-level HRM to participate in our survey. HR managers were recruited by e-mail, phone, or post, often using multiple methods. We targeted upper-level HR managers for the survey, because they were expected to be more knowledgeable about their organization’s approach to WLM. They were invited to participate in a web-based survey about their organization’s employees, HRM practices, and work-life policies. The survey was administered via a secure web site. Survey instructions indicated that they should provide “an overall

Beyond Policy Adoption   9

perspective of your company regarding how reduced-load (working less than full-time and accordingly being paid less), and other related worklife policies are evolving and fit into your business and human resource environment.” Sample The final sample of organizations for this study consisted of 46 organizations from multiple sectors of the economy. About half (48%) of the organizations in our sample were professional service firms, but organizations from high-technology manufacturing (15%), consumer goods (13%), and durable manufacturing (13%), as well as government and nonprofits (11%) were also represented. Organizations ranged in size from between 500-2,000 employees to more than 50,000 employees. For each company, we contacted the individual who directly oversaw the work-life programs and practices or supervised these individuals and asked them to complete the survey. One HR manager from each company completed the survey. The vast majority of the respondents (75%) were at the HR manager level or higher. Approximately 40% of the respondents were managers, 22% were directors, 7% were vice presidents, and 7% were senior vice presidents or higher. Measures Supportive context for RLWAs. In order to measure supportive practices related to RLWAs, we created a scale that contained items representing the facets of organizational support described above (e.g., access to training and development, pay and promotion opportunities). The items for this scale are shown in the Appendix. Participants indicated the extent to which they agreed or disagreed with the statements using a scale from 1 (Strongly Disagree) to 5 (Strongly Agree). An exploratory factor analysis using principal axis factoring was conducted to determine the latent factor structure of the items. An examination of the scree plot (Cattell, 1966) and the factor loadings indicated that a single factor best described the underlying covariance structure. After removing one poorly performing item, this factor explained 40% of the variance in the data. Factor loadings ranged from .417 to .816. The estimated reliability for this scale was α = .83. Organizational commitment to HRM. We used an adapted form of the Huselid et al. (1997) scale to measure commitment to HRM. Respondents indicated the extent to which each of seven items described their organization using a scale from 1 (Strongly Disagree) to 5 (Strongly Agree). All seven

10  A. F. Westring, E. E. Kossek, S. Pichler and A. M. Ryan

items reflect practices designed to enhance HRM. Higher scores indicate a greater organizational commitment to HRM. The reliability estimate for the current study is α = .81. Organizational commitment to WLM. To assess the extent to which the organization was committed to WLM, we developed a measure of the extent to which organizations integrated work-life practices and values into their overall vision and strategy based on our review of the literature described above (see Appendix). These items were measured on a fivepoint scale from 1 (Strongly Disagree) to 5 (Strongly Agree). An exploratory factor analysis using principal axis factoring was conducted to determine the dimensionality of these four items. Only the first factor had an eigenvalue above 1.0; this factor explained 59% of the variance in the data. Factor loadings ranged from .641 to .824. The estimated reliability for this scale was α = .84. Control variables. In light of prior research, we controlled for both organizational size and sector. Because organizational size has consistently been found to predict the adoption of innovative human resource management programs (Kochan, McKersie, & Chalykoff, 1986; Osterman, 1994) as well as the adoption of work-family programs (Goodstein, 1994; Konrad & Mangel, 2000; Milliken, Martins, & Morgan, 1998), we controlled for the potential confounding effects of size. Organizational size was measured by asking respondents how many full-time employees worked for their organization, using a 5-point scale ranging from under 200–500 to greater than 50,000. Because industry has been found to predict the extent of workplace innovation in organizations (Kochan et al., 1986), and has also been related to extent of work-family benefit adoption (Milliken et al., 1997; PerrySmith & Blum, 2000), we also controlled for industry effects by dummy coding organizations as either manufacturing (coded as 1) or nonmanufacturing (coded as 0). An additional control variable was the total number of work-life policies offered by the organization. Because we were interested in strategic and cultural factors that impact support for RLWAs, we decided to control for the total number of policies on the books of the organization. The policies that were included in the measure were as follows: job-sharing, flextime, flexplace, modified/compressed work week, company-sponsored dependent care (on or near site), dependent care referral services, paid personal or family care leave, maternity leave, paternity leave, lactation program, company-sponsored health and wellness program (on or near site), health and wellness referral services, continuing education, phased retirement, and adoption aid. RLWAs were not included in this index because all organizations in the sample offered this policy. This index is

Beyond Policy Adoption   11

similar to the indices used by Konrad and Mangel (2000) and Osterman (1995). The work-life policies index (WLPI) was created by summing yes/no responses for each program. The reliability estimate for this index was α = .73, which is consistent with the alpha estimate of Osterman’s index, which was .75. Analyses Hypotheses were tested using hierarchical and moderated multiple regression. Hierarchical regression was used to explore the impact of commitment to HRM and WLM above and beyond the control variables. To facilitate understanding of the unique implications of these two variables, two hierarchical regressions were conducted in which the order of their entry into the regression was reversed. To investigate the interaction between commitment to HRM and WLM, we conducted a third hierarchical regression in which the two types of commitment were entered in Step 2 and the cross-product of the two variables was entered in Step 3. The interaction term was calculated as the cross-product of mean-centered variables (Cohen, Cohen, West, & Aiken, 2003). Significant interactions are interpreted according to procedures described by Cohen and Cohen (1983). Results Means, standard deviations, and intercorrelations of all variables are reported in Table 1.1. The zero-order correlation between commitment to HRM and commitment to WLM, while positive and significant, clearly indicates that separate constructs were being assessed (r = .37, p < .05). Relationships at the bivariate level indicate that the control variables had nonsignificant effects on the outcome variables. At the bivariate level, both commitment to HRM and commitment to WLM were significantly related to the supportive context for RLWAs (p < .01 for both). These results provide initial support for Hypotheses 1 and 2. Multiple regressions were used to further explore Hypotheses 1 and 2. As can be seen in Table 1.2 (Step 2), commitment to HRM explains an additional 16.5% of the variance (p < .01) in the supportive context for RLWAs above and beyond the effects of the control variables. This finding provides further evidence in support of Hypothesis 1. Similarly, in Table 1.3 (Step 2), commitment to WLM explains an additional 23.8% of the variance (p < .01) in supportive context for RLWAs above and beyond the control variables. These findings support Hypothesis 2.

12  A. F. Westring, E. E. Kossek, S. Pichler and A. M. Ryan Table 1.1. Means, Standard Deviations and Intercorrelations for all Study Variables Variable

Potential Range

Mean

SD

1

1.

Industry

0,1

  .48

 .51

–

2.

# of Employees

1–5

  4.00

  .93

–.12

–

3.

# of WorkLife Policies

0–15

11.82

2.63

–.05

–.27

(.75)

4.

Commit. to HRM

1–5

  4.21

  .46

–.09

–.00

(.10

(.81)

5.

Commit. to WLM

1–5

  3.70

  .78

–.14

–.23

(.29

(.37*

(.84)

6.

Support for RLWAs

1–5

  3.34

 .59

–.11

–.01

(.01

((.41**

((.43**

2

3

4

5

6

(.83)

Notes. * p < .05 ** p < .01. Scale reliabilities in parentheses; SD = standard deviation; Commit. to HRM = Commitment to Human Resource Management; Commit. to WLM = Commitment to Work-Life Management; RLWA = Reduced-Load Work Arrangement. For industry, 0 = nonmanufacturing, 1 = manufacturing.

To investigate the incremental and interactive influence of the two types of commitment (for HRM and WLM), we conducted additional analyses. To address Research Question 1, we included a third step in our hierarchical regressions (Tables 1.2 and 1.3). As can be seen in Step 3 of Table 1.2, commitment to WLM explains an incremental 14.6% of variance in the supportive context for RLWAs (above and beyond commitment to HRM; p < .01). In Table 1.3 (Step 3), we can see that commitment to HRM does not explain a significant amount of incremental variance above and beyond commitment to WLM. In the analysis of Research Question 2, we explore the interactive effects of these two types of commitment. In Table 1.4 (Step 3), we add the interaction term of these two variables above and beyond their main effects. The interaction between commitment to HRM and WLM is significant in the prediction of a supportive context for RLWAs. The addition of the interaction term explains an additional 11.2% of the variance in this outcome (p < .01). Figure 1.1 displays the plot of the interaction term. Figure 1.1 indicates that when commitment to WLM is high, level of commitment to HRM does not impact the supportive context for RLWAs. It is only when commitment to WLM is low that the regression slope is noticeably positive. These findings indicate that a high commitment to HRM, in general, can essentially overcome a lower commitment to WLM in creating a supportive context for RLWAs. The least supportive context, not surprisingly, occurs when the organization has low commitment to both HRM and WLM.

Beyond Policy Adoption   13 Table 1.2.  Incremental Impact of Commitment to Work-Life Management in Predicting Supportive Context for Reduced-Load Work Arrangements  STEP 1 Variable

STEP 2

STEP 3

B

SE

B

SE

B

SE

(Constant)

3.411**

.565

1.370**

.907

.935*

.848

Industry

–.123**

.188

–.168**

.174

–.125**

.161

# of Employees

.005

.109

.023*

.101

.089*

.096

# of Work-Life Policies

–.002*

.039

–.018**

.036

–.057**

.036

.522**

.189

.365v

.183

.347**

.123

Commitment to HRM Commitment to WLM .143

2.021**

3.509**

–.065*

.089*

.230*

∆R2

.011

.165**

.146**

F for ∆R2

.143

7.583**

7.977**

F Adjusted R2

Notes. *p < .05. **p < .01. HRM = Human Resource Management. WLM = Work-Life Management.

Table 1.3.  Incremental Impact of Commitment to Human Resource Management in Predicting Supportive Context for Reduced-Load Work Arrangements STEP 1 Variable

B

STEP 2 SE

B

SE

STEP 3 B

SE

(Constant)

3.411**

.565

2.136**

.620

.935*

.848

Industry

–.123**

.188

–.087*

.166

–.125*

.161

.092..

.099

.092

.099

.089

.096

–.002**

.039

–.055*

.038

–.057*

.036

# of Employees # of Work-Life Policies Commitment to WLM

.421**

Commitment to HRM F Adjusted R

1.43***

3.144*

–.065**

.121

.347**

.123

.365

.183

3.509*

.170

.230

∆R2

.011*

.238**

.073

F for ∆R2

.143*

12.027**

2

3.981*

Notes. *p < .05. **p < .01. HRM = Human Resource Management. WLM = Work-Life Management.

14  A. F. Westring, E. E. Kossek, S. Pichler and A. M. Ryan Table 1.4.  Interaction between Commitment to Human Resource and Work-Life Management in Predicting Supportive Context for Reduced-Load Work Arrangements STEP 1 Variable

STEP 2

STEP 3

B

SE

B

SE

B

SE

(Constant)

3.411**

.565

.935

.848

–.8029*

3.441

Industry

–.123

.188

–.125

.161

–.189

3.151

# of Employees

.005

.109

.089

.096

.062

3.089

# of WorkLife Policies

–.002

.039

–.057

.036

–.043

3.034

Commitment to WLM

.347**

.123

2.784**

3.918

Commitment to HRM

.365

.183

2.469**

3.805

–.569*

3.213

WLM x HRM F Adjusted R2

.143

3.509*

4.604**

–.605

.230

.340

∆R

.011

.311**

.112**

F for ∆R2

.143

8.476**

7.157**

2

Notes. *p < .05. **p < .01. HRM = Human Resource Management. WLM = WorkLife Management. WLM x HRM = Interaction between commitment to WLM and commitment to HRM.

Notes. WLM = Work-Life Management. HRM = Human Resource Management

Figure 1.1.  Interaction between Commitment to HRM and WLM in Supportive Context for Reduced-Load Work Arrangements.

Beyond Policy Adoption   15

Discussion The purpose of this chapter was to examine the macro HR factors of commitment to human resource management and work-life strategy that impact the supportiveness of an organizational context for implementing a specific work-life policy that challenges work-life norms for professionals. This investigation is important because, while many organizations may offer work life flexibility policies such as RLWAs, organizations vary in the degree to which they support the use of the policy without penalty (Eaton, 2003). Existing research has shown that the context in which such policies are offered has a profound impact on their effectiveness both generally (Kelly et al., 2008) and specifically for RWLAs (Kossek et al., in press). Indeed, in our study, organizations offering a greater number of work-life policies were no more supportive of RLWAs than those that offer fewer such policies. This finding suggests that merely having lots of work-life policies on the books is a necessary but insufficient condition for organizational support of work life. Having policies on the books can be a sign of awareness of the recruitment and public relations value of work-life policies but not does not necessarily suggest that working in alternative and diverse ways have gained acceptance “moving from the margins to the mainstream” of organizational life (Kossek et al., 2010). We found that the overall organizational approach to HRM does impact the context for RLWAs. When organizations are committed to HRM as a valued organizational strategy, they are more likely to provide support for RLWAs. Perhaps this finding suggests that such firms are more likely to see talent as a resource in which to invest and take a longer term view to retaining and developing people. Those organizations that are dedicated to investing in human capital will be more likely to support their reduced-load workers. When organizations are committed to overall human resource management in their implementation of HR practices, they are building a culture of commitment to supporting human capital that extends to their approach to alternative work arrangements. We also found that a higher commitment to effectively managing employee work-life needs is related to a greater context of support for RLWAs. Again, our findings reinforce the important concept that offering work-life policies on the books is simply not enough to meet employee needs. When organizations are more deeply committed to work-life issues as part of their vision and strategic approach to managing the organization, they will be more likely to support their reduced-load employees. We also explored the incremental and interactive impact of commitment to HRM and WLM. We found that being committed to WLM does explain incremental variance in supportive context for RLWAs above and beyond commitment to HRM. However, the reverse of this finding is not true.

16  A. F. Westring, E. E. Kossek, S. Pichler and A. M. Ryan

Commitment to HRM did not explain incremental variance above commitment to WLM. These differential findings suggest that an organization’s approach to work-life issues may be more central to its support for RLWAs than the broader HRM approach is. In our moderation analyses, we found that when commitment to WLM was high, the organization’s commitment to HRM was not important in predicting support for RLWAs. However, when commitment to WLM was low, a broader commitment to HRM was valuable in creating a context of support. These findings suggest that a strategic commitment to employees as a key organizational resource is an important factor in supporting RLWAs. Although commitment to work-life issues is important, a broader culture that values employees may be just as impactful when it is absent. There are some potential limitations to this study that deserve mention. First, the recruiting strategy was targeted at organizations that are known for offering RLWAs. Thus, the findings are constrained to organizations that already offer some degree of work-life support for employees. Within these organizations, we interviewed one manager per organization, who may have a unique perspective on the commitment of the organization to HRM or WLM. The manager may also be biased toward reporting greater levels of support for RLWAs, based on the role within the organization. Common method variance could also potentially inflate correlations between measures (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). However, our results do not indicate a pattern of extremely high intercorrelations among selfreported variables, relative to the reliability of the scales. This study was also conducted within North America and prior research has demonstrated cross-national differences with regard to work-life issues and the context of support for employees (Kossek & Ollier-Malaterre, 2013; Lyness & Judiesch, 2008). Future research should explore whether our findings generalize to other cultures outside of North America. Implications for Future Research and Practice Despite these limitations, this research has important implications for both research and practice. For researchers, we want to highlight the importance of treating global organizational support for work-life policies as an important dependent variable in its own right. There have already been calls for researchers to differentiate between policy availability and use (Kossek, Baltes, & Mathews, 2011), but these two outcomes still do not paint the full picture of policy implementation. It is important for scholars to further investigate the organizational factors such as the impact of commitment to HR and work-life policies that constitute a supportive context for the

Beyond Policy Adoption   17

effective implementation of specific work-life practices such as RLWAs and cultures and structures that facilitate the creation of that context. In the current study, we focus on an organization’s commitment to both HRM and WLM as predictors of this supportive context. Our findings highlight the importance of both of these variables and suggest that the underlying vision, values, and culture of an organization may play an important role in influencing the degree of support for work-life policies. We hope that other researchers continue to investigate the underlying strategic and cultural goals and values of organizations as predictors of policy support. Further, we hope that researchers move beyond the main effects of these variables to understand the complex interplay between vision, values, strategy, and culture in impacting support for work-life policies. It is also important to emphasize that our focus on RLWAs, a unique type of work-life policy, is very important for advancing professionals’ careers and well-being, as RLWAs are unique in their reduction in actual workload, as opposed to just a change in the time or place of work completion. Utilization of RLWAs challenges traditional notions of what is considered a “good” or “valuable” worker. Thus, we believe that support for such arrangements may be more closely linked to the values of the organization regarding the fostering of a sustainable work force and the reduction of work-family conflict and promotion of employee well-being than other types of policies (Kossek et al., 2014). We suggest that future research include multiple types of work-life policies and investigate linkages to commitment to human resource management and investment in a work-life strategy. Additionally, a comparative study that explores factors influencing support and implementation for different types of policies across cross-national contexts would be valuable for advancing knowledge of how to address the work-family policy and practice implementation gap. We believe that the results of this study will also be valuable to human resource managers. By using support for RLWAs as our outcome (in lieu of policy availability), we hope to turn the attention of practitioners to the context in which work-life policies are offered and implemented. In essence, we hope practitioners will understand the importance of supporting reduced-load workers (or employees using other types of work-life policies). HR managers may also play an important role in educating both managers and employees about the importance of support for RLWAs. Overall, support for work-life policies does not begin or end when employees negotiate their policy usage. Instead, all HR systems (e.g., promotion, training, recruiting) must adapt to the needs of employees using alternative work arrangements. This study also has relevance for organizational leaders, those who play an important role in shaping the vision, values, and culture of their organization. Leaders will benefit from understanding how these “bigger picture”

18  A. F. Westring, E. E. Kossek, S. Pichler and A. M. Ryan

issues play out in the daily lives of employees. By creating an environment that is committed to HRM and WLM, they may increase the likelihood that employees will be supported when they utilize work-life policies. In order to truly support employee work-life needs, organizational leaders must “walk the talk.” Although many organizations pay lip service to work-life balance and employees as a valuable resource, not all organizations have such values deeply embedded in the way that they operate. Our results demonstrate the importance of moving beyond such “lip service” to a deeper cultural integration of valuing employees and their work-life needs. Acknowledgments This research was funded by the Alfred P. Sloan Foundation (Grant Number 2002-6-10) as part of a study lead by Mary Dean Lee of McGill University and Ellen Ernst Kossek of Purdue University. We are also grateful to Carol Schreiber and Connie J. G. Gersick and Tim Hall for help with data collection. References Allen, T. D. (2001). Family-supportive work environments: The role of organizational perceptions. Journal of Vocational Behavior, 58, 414–435. Anderson, S. E., Coffey, B. S., & Byerly, R. T. (2002). Formal organizational initiatives and informal workplace practices: Links to work-family conflict and job related outcomes. Journal of Management, 28, 787–810. Batt, R., & Valcour, M. (2003). Human resource practices as predictors of workfamily outcomes and employee turnover. Industrial Relations: A Journal of Economy and Society, 42, 189–220. Becker, B., & Gerhart, B. (1996). The impact of human resource management on organizational performance: Progress and prospects. Academy of Management Journal, 39, 779–801. Berg, P., Kalleberg, A. L., & Appelbaum, E. (2003). Balancing work and family: The role of high-commitment environments. Industrial Relations: A Journal of Economy and Society, 42, 168–188. Blair-Loy, M., & Wharton, A. S. (2002). Employees’ use of work-family policies and the workplace social context. Social Forces, 80, 813–845. Cattell, R. B. (1966). The screen test for the number of factors. Multivariate Behavior Research, 1, 245–276. Cohen, J., & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/ correlation analysis for the behavioral sciences. Mahwah, NJ: Erlbaum.

Beyond Policy Adoption   19 Combs, J., Liu, Y., Hall, A., & Ketchen, D. (2006). How much do high-performance work practices matter? A meta-analysis of their effects on organizational performance. Personnel Psychology, 59, 501–528. Eaton, S. (2003). If you can use them: Flexibility policies, organizational commitment and perceived performance. Industrial Relations, 42, 145–167. Friede, A., Kossek, E. E., Lee, M. D., & MacDermid, S. (2008). Human resource manager insights on creating and sustaining successful reduced-load work arrangements. Human Resource Management, 47, 707–727. Goodstein, J. D. (1994). Institutional pressures and strategic responsiveness: Employer involvement in work-family issues. Academy of Management Journal, 37, 350–382. Hall, T., Kossek, E., Briscoe, J., Pichler, S., & Lee, M. (2013). Nonwork relative to career orientations: A multi-dimensional measure. Journal of Vocational Behavior, 83, 539–550. Hall, D., Lee, M, Kossek, E., & Las Heras, M. (2012). Pursuing career success while sustaining personal and family well-being: A study of reduced-load professionals over time, Journal of Social Issues, 68, 742–766. Hammer, L., Kossek, E., Yragui, N., Bodner, T., & Hansen, G. (2009). Development and validation of a multi-dimensional scale of family supportive supervisor behaviors (FSSB). Journal of Management, 35, 837–856. Huselid, M., Jackson, S. E., & Schuler, R. S. (1997). Technical and strategic human resources management effectiveness as determinants of firm performance. Academy of Management Journal, 40, 171–118. Kelly, E. L., Kossek, E. E., Hammer, L. B., Durham, M., Bray, J., Chermack, K., & Kaskubar, D. (2008). Getting there from here: Research on the effects of workfamily initiatives on work-family conflict and business outcomes. Academy of Management Annals, 2(1), 305–349. Kochan, T. A., McKersie, R. B., & Chalykoff, J. (1986). The effects of corporate strategy and workplace innovations on union representation. Industrial and Labor Relations Review, 39, 487–501. Konrad, A., & Linnehan, F. (1995). Formalized HRM structures: Coordinating equal employment opportunity or concealing organizational practices? Academy of Management Journal, 38, 787–820. Konrad, A. M., & Mangel, R. (2000). The impact of work-life programs on firm productivity. Strategic Management Journal, 21, 1225–1237. Kossek, E. E. (1989). The acceptance of human resources innovation by multiple constituencies. Personnel Psychology, 42, 263–281. Kossek, E. E. (2005). Workplace policies and practices to support work and families. In S. Bianchi, L. Casper, & R. King (Eds.), Work, family, health, and well-being (pp. 97–116). Mahway, NJ: Erlbaum Press. Kossek, E. E., Baltes, B. B., & Matthews, R. A. (2011). How work-family research can finally have an impact in the workplace. Industrial and Organizational Psychology: Perspectives on Science and Practice, 4, 352–369. Kossek, E. E., & Friede, A. (2006). The business case: Manager perspectives on work and the family. In M. Pitt-Catsouphes, E. E. Kossek, & S. Sweet (Eds.), The work and family handbook (pp. 661–626). Mahwah, NJ: Erlbaum.

20  A. F. Westring, E. E. Kossek, S. Pichler and A. M. Ryan Kossek, E. E., & Lee, M. (2008). Implementing a reduced-workload arrangement to retain high talent: A case study. Journal of Managerial Psychology, 23, 49–64. Kossek, E. E., Lewis, S., & Hammer, L. (2010). Work-life initiatives andorganizational change: Overcoming mixed messages to move from the margin to the mainstream. Human Relations, 63, 3–19. Kossek, E. E., & Ollier-Malaterre, A. (2013). Work-family policies: Linking national contexts, organizational practice and people for multi-level change. In S. Poelmans, J. Greenhaus, & M. Las Heras (Eds.), New frontiers in work-family research: A vision for the future in a global world (pp. 1–53). Basingstoke, England: Palgrave Macmillan. Kossek, E., Ollier-Malaterre, A., Lee, M. D., Pichler, S., & Hall, D T. (in press). Line managers’ rationales regarding reduced-load work of professionals in embracing and ambivalent organizational contexts. Human Resource Management Journal. Kossek, E., Pichler, S., Bodner, T., & Hammer, L. (2011). Workplace social support and work-family conflict: A meta-analysis clarifying the influence of general and work-family specific supervisor and organizational support. Personnel Psychology, 64, 289–313. Kossek, E. E., Valcour, M., & Lirio, P. (2014). The sustainable workforce: Organizational strategies for promoting work-life balance and well-being. In P. Chen & C. Cooper (Eds.), Work and wellbeing: A complete reference guide (Vol. III, pp. 295–319). Oxford, England: Wiley-Blackwell. Lee, M. D., Lirio, P., Karakas, F., MacDermid, S. M., Buck, M. L., & Kossek, E. E. (2006). Exploring career and personal outcomes and the meaning of career success among part-time professionals in organizations. In R. J. Burke (Ed.), Research companion to working time and work addiction (pp. 284–309). Cheltenham, England: Edward Elgar. Lee, M. D., MacDermid, S. M., & Buck, M. L. (2000). Organizational paradigms of reduced-load work: Accommodation, elaboration, and transformation. Academy of Management Journal, 43, 1211–1226. Lee, M. D., MacDermid, S. M., Williams, M. L., Buck, M. L., & Leiba-O’Sullivan, S. (2002). Contextual factors in the success of reduced-load work arrangements among managers and professionals. Human Resource Management, 41, 209–223. Lirio, P., Lee, M. D., Williams, M. L., Haugen, L. K., & Kossek, E. E. (2008). The inclusion challenge with reduced-load professionals: The role of the manager. Human Resource Management, 47, 443–461. Lobel, S., & Kossek, E. (1996). Human resource strategies to support diversity in work and personal lifestyles: Beyond the “family friendly” organization. In E. E. Kossek & S. Lobel (Eds.), Managing diversity: Human resource strategies for transforming the workplace (pp. 221–244). Oxford, England: Blackwell. Lyness, K. S., & Judiesch, M. K. (2008). Can a manager have a life and a career? International and multisource perspectives on work-life balance and career advancement potential. Journal of Applied Psychology, 93, 789–805. Matos, K., & Galinsky, E. (2012). National Study of Employers. Retrieved August 15, 2012, from http://familiesandwork.org/site/research/reports/NSE_2012. pdf

Beyond Policy Adoption   21 Milliken, F. J., Martins, L. L., & Morgan, H. (1998). Explaining organizational responsiveness to work-family issues: The role of human resource executives as issue interpreters. Academy of Management Journal, 41, 580–592. Osterman, P. (1994). How common is workplace transformation and who adopts it? Industrial and Labor Relations Review, 47, 173–188. Osterman, P. (1995). Work-family programs and the employment relationship. Administrative Science Quarterly, 40, 681–700. Perry-Smith, J. E., & Blum, T. C. (2000). Work-family human resource bundles and perceived organizational performance. Academy of Management Journal, 43, 1107–1117. Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 879–903. Ryan, A., & Kossek, E. (2008). Work-life policy implementation: Breaking down or creating barriers to inclusiveness. Human Resource Management, 47, 295–310. Subramony, M. (2009). A meta-analytic investigation of the relationship between HRM bundles and firm performance. Human Resource Management, 48, 745– 768. Thomas, L. T., & Ganster, D. C. (1995). Impact of family-supportive work variables on work-family conflict and strain: A control perspective. Journal of Applied Psychology, 80, 6–15. Thompson, C. A., Beauvais, L. L., & Lyness, K. S. (1999). When work-family benefits are not enough: The influence of work-family culture on benefit utilization, organizational attachment, and work-family conflict. Journal of Vocational Behavior, 54, 392–415. Thorsteinson, T. J. (2003). Job attitudes of part-time vs. full-time workers: A metaanalytic review. Journal of Occupational and Organizational Psychology, 76, 151–177. Tsui, A., & Milkovich, G. (1987). Personnel department activities: Constituency perspectives and preferences, Personnel Psychology, 40, 519–537. White, M., Hill, S., McGovern, P., Mills, C., & Smeaton, D. (2003). “High-performance” management practices, working hours, and work-life balance. British Journal of Industrial Relations, 41, 175–195. Wharton A., & Blair-Loy, M. (2002). The “overtime culture” in a global corporation: A cross-national study of finance professionals’ interest in working part-time. Work and Occupations, 29, 32–63.

22  A. F. Westring, E. E. Kossek, S. Pichler and A. M. Ryan

APPENDIX New Measures Developed or Adapted for This Study Organizational Commitment to Work-Life Management (WLM) 1. This organization is one of the best employers for people concerned about balancing work and life because of the great policies and programs it offers. 2. This organization is one of the best employers for people concerned about balancing work and life, because of the top management philosophy. 3. The human resource strategy developed by this organization includes consideration of employees’ work and life demands. 4. The business strategy of this organization explicitly incorporates strategy based on the value of employees. Organizational Commitment to Human Resource Management (HRM) Adapted from Huselid, M., Jackson, S.E., & Schuler, R.S. (1997). 1. Working in teams is a core part of the work environment in this organization. 2. This organization engages in quality improvement practices 3. This organization works towards employee empowerment 4. This organization engages in frequent diagnosis of strategic needs 5. This organization engages in talent development in order to achieve its business objectives 6. The HR policies of this organization are designed by individuals with a clear understanding of the strategic business objectives of the company 7. HR serves a supporting role in the implementation of strategic business decisions Supportive Context for Reduced-Load Work Arrangements 1. The performance review process for those working reduced-load adjusts the criteria for evaluation in a fair manner, given the lesser hours of the individual 2. Training opportunities are less for those working on a reduced-load basis, compared to other employees (R) 3. Career development opportunities are better for those employees not working on a reduced-load basis (R)

Beyond Policy Adoption   23

4. There are some opportunities to be hired into the organization from the outside in a reduced-load arrangement 5. Reduced-load work arrangements result in one being less likely to be chosen for special developmental assignments (R) 6. Assuming good performance, advancement opportunities for those working on reduced-load are as good as opportunities for those working full-time 7. Individuals working a reduced-load generally have to return to a full work load in order to receive a promotion (R) (R) = Reverse-scored

chapter 2

Do Organizational Efforts to Help Employees Achieve Balance Matter? An Empirical Study of Organizational Support Initiatives on Worker Attitudes Yasmin S. Purohit, Claire A. Simmers, Sherry E. Sullivan, and S. Gayle Baugh

Since the 1970s, the number of working mothers in the U.S. labor force has been increasing, with the Bureau of Labor Statistics declaring a record number of 19.5 million working mothers in 1984 (Hayghe, 1984). Once seen as exception rather than the rule, working mothers, working women, and dual-career couples have become the norm in many Western countries (Hayghe, 1984; Neault & Pickerell, 2005). In 2012, for example, in 62.4% of U.S. households with children between the ages of 6 and 17, both parents were employed. In single parent households with children between 6 and 17 years old, 67.1% of mothers and 81.8% of fathers were employed (Bureau of Labor Statistics, 2013).

Striving for Balance, pp. 25–45 Copyright © 2016 by Information Age Publishing All rights of reproduction in any form reserved.

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26  Y. S. Purohit, C. A. Simmers, S. E. Sullivan and S. Gayle Baugh

The workforce of many organizations has dramatically changed from once being composed mainly of the male breadwinner with his stay-at-home wife caring for their children to a more diverse workforce characterized by men and women who are working full-time, often while caring for children as well as elderly relatives (Mainiero & Sullivan, 2006). As the workforce has changed, organizations have increasingly offered support initiatives, such as childcare and flexible work schedules, to help employees cope with work and nonwork role demands (Ballout, 2007; Friedman & Greenhaus, 2000; Friedman & Johnson, 1997). Despite the growth of these organizational support initiatives, however, relatively little research has investigated the effectiveness of these programs (Glass & Estes, 1997; Heneman & Judge, 2000; Williams, Brower, Ford, Williams, & Carraher, 2008); specifically, relatively few studies have looked at the relationship between employees’ satisfaction with the support received from their organization and work attitudes (Heneman & Judge, 2000; Payne & Jones, 1987; Tardy, 1985; Wallace, Edwards, Arnold, Frazier, & Finch, 2009; Williams et al., 2008). The purpose of this study is to address this important gap in the organizational support literature by specifically examining how employees’ satisfaction with their organization’s discretionary (i.e., not legally required) support initiatives influences their work-related attitudes and personal well-being. Drawing from social exchange theory (Cropanzano & Mitchell, 2005; Eisenberger, Huntington, Hutchison, & Sowa, 1986; Gibney, Zagenczyk, & Masters, 2009; Homans, 1961) as well as the literature on compensation and work attitudes (e.g., Mello, 2002), we suggest that organizational support initiatives impact work attitudes and that these relationships are moderated by job involvement. The Intersection of Organizational Support Initiatives and Compensation Most of the research on organizational support is based on social exchange theory (Gibney et al., 2009), with the organization and its employees as the two parties involved in the exchange (Eisenberger et al., 1986). According to social exchange theory, organizations offer benefits, such as flexible work schedules and career assistance, and employees reciprocate with positive attitudes, such as high organizational commitment and job satisfaction. These support initiatives are offered by organizations not because they are legally required to do so, but are instead provided at the organization’s discretion. It is believed that employees value benefits more if the benefits are perceived as given by the employer rather than mandated by law (Baran, Shanock, & Miller, 2012).

Organizational Support Initiatives   27

Organizational support has also been conceptualized as an important coping resource that helps individuals deal with workplace stressors and work/life conflicts (Eisenberger et al., 1986; Gibney et al., 2009). In response to their employees’ ever-increasing role demands and resulting heightened stress, organizations are designing support initiatives to help their employees enhance their coping resources and balance role demands (Eby, Casper, Lockwood, Bordeaux, & Brinley, 2005; Friedman & Greenhaus, 2000; Greenhaus & Parasuraman, 1994; Hochschild, 1989; Litzky & Greenhaus, 2007; Litzky, Purohit, & Weer, 2008; Parasuraman, Greenhaus, & Granrose, 1992). Similarly, as part of an organization’s compensation strategy, support policies and benefits are viewed as a means to attract and retain high quality employees, especially in industries facing worker shortages (Friedman & Greenhaus, 2000; Garger, 1999; Glass & Estes, 1997; Lenaghan & Eisner, 2006). Researchers studying organizational support have measured respondents’ perceptions regarding the overall support employees believe they receive from their organization, referred to as perceived organizational support (POS) (Chew & Wong, 2008; Eisenberger, Fasolo, & Davis-LaMastro, 1990; Moorman, Blakely, & Niehoff, 1998; Muse & Stamper, 2007) as well as support from supervisors, colleagues, and support derived from organizational benefits (Greenberger, Goldberg, Hamill, O’Neil, & Payne, 1989). Researchers have consistently found that employees who believe their organization is supportive tend to have positive attitudes and behaviors towards their career (Chew & Wong, 2008; Sturges, Conway, & Liefooghe, 2010; Thompson & Prottas, 2006) and organization (Gibney et al., 2009; Wegge, Schmidt, Parkes, & van Dick, 2007). Variables examined in organizational support research are often the same key variables examined when studying compensation satisfaction. Scholars are increasingly acknowledging that compensation satisfaction includes individuals’ satisfaction not only with pay but also with benefits (Mello, 2002; Miceli & Lane, 1991; Williams et al., 2008) and that examining satisfaction with benefits is critical because it impacts the relationship between compensation system variables and workers’ attitudes and behaviors. Drawing from social exchange theory and the literature on compensation, we suggest that organizational support initiatives are a form of discretionary benefit that can influence employee job attitudes. We also suggest that an employee’s job involvement will moderate the relationship between satisfaction with organization’s support initiatives and employee attitudes. Unlike past research, which has focused on perceptions of organizational support as a global attitude (Baran et al., 2012), this study focuses on specific organizational support initiatives

28  Y. S. Purohit, C. A. Simmers, S. E. Sullivan and S. Gayle Baugh

Hypotheses Figure 2.1 depicts the hypotheses to be examined in this study. As indicated in Figure 2.1, we suggest that an employee’s satisfaction with organizational benefits will be positively associated with three major outcomes: (a) job satisfaction (an individual’s affective reactions to various aspects of his/her job) (Lawler & Hall, 1970), (b) organizational commitment (an employee’s identification with an organization and its goals) (Mowday, Porter, & Steers, 1982; Ngo & Tsang, 1998), and (c) quality of life (an individual’s perception that his or her work contributes to positive or negative consequences for him or her) (Shamir & Solomon, 1985). We also suggest that an employee’s job involvement will moderate the relationship between satisfaction with organization’s support initiatives and worker attitudes of job satisfaction, organizational commitment, and quality of life.

Figure 2.1.  The relationship between satisfaction with organizational support initiatives and worker attitudes.

In previous research it was found that perceived organizational support (Abraham, 1998; Muse & Stamper, 2007; Ngo, Foley, Ji, & Loi, 2014) and actual organizational support (Greenberger et al., 1989; Muse & Stamper, 2007; Thompson & Prottas, 2006) were positively related to job satisfaction. Research by Shin, Wong, Simko, and Ortiz-Torres (1989) suggests that organizational support helps decrease an individual’s experience of job dissatisfaction. Organizational support may also lessen the negative impact

Organizational Support Initiatives   29

of work stressors on employees’ well-being and job satisfaction. Therefore, we propose that: H1: An individual’s satisfaction with organizational support initiatives will be positively related to job satisfaction. Studies have found that organizational support is positively related to organizational commitment (Abraham, 1998; Greenberger et al., 1989; Hutchison, 1997; Leveson, Joiner, & Bakalis, 2009). Organizational support has also been found to play a mediating role between variables; for example, organizational support mediates between perceived justice and organizational commitment (Dawley, Andrews, & Bucklew, 2008; Naumann, Bennett, Bies, & Martin, 1998) and emotional dissonance and organizational commitment (Abraham, 1999). Based upon previous studies of the role of organizational support and perceived organizational support on commitment, we propose: H2: An individual’s satisfaction with organizational support initiatives will be positively related to organizational commitment. Research on the relationship between work-nonwork balance and quality of life has consistently shown that while work-family conflict and life stressors negatively influence quality of life, support enhances individuals’ perception of the quality of their life (Abbey, Abramis, & Caplan, 1985; Fusilier, Ganster, & Mayes, 1986; Md-Sidin, Sambasivan, & Ismail, 2010; Parasuraman, Greenhaus, Rabinowitz, Bedeian, & Mossholder, 1989). Based upon previous research, we propose: H3: An individual’s satisfaction with organizational support initiatives will be positively related to quality of life. In addition to the direct relationships depicted in Figure 2.1, we explore whether job involvement, defined as the psychological identification with one’s work and the degree to which the job situation is central to the person and his or her identity (Lawler & Hall, 1970), moderates the relationship between satisfaction with organizational support initiatives and the employee attitudes of job satisfaction, organizational commitment, and quality of life. Igbaria, Parasuraman, and Badawy (1994) found that those with high psychological job involvement are likely to have heightened affective reactions, both positive and negative, to job experiences. The identity of individuals who are more psychologically involved in work hinges more crucially on work and these individuals are more ego-involved in work (Mihelic, 2014; Mortimer & Lorence, 1989). Individuals with high job

30  Y. S. Purohit, C. A. Simmers, S. E. Sullivan and S. Gayle Baugh

involvement will view job-related outcomes as more important in their own self-evaluation and job-related attitudes (Mannheim, Baruch, & Tal, 1997) and contextual variables will be seen as less influential because these variables are not central to performing the work itself. Thus, satisfaction with the organization’s support initiatives may be expected to have a stronger positive effect on the job attitudes and well-being of individuals who are less job involved compared to those who are highly job involved. Therefore, we propose: H4: Job involvement moderates the relationship between satisfaction with organizational initiatives and job satisfaction, organizational commitment, and quality of life such that the positive relationship between satisfaction with organizational initiatives and each of the outcomes will be stronger for individuals with low job involvement than for those with high job involvement.

Method Surveys were distributed to over 600 full-time employees in a large manufacturing organization in the midwestern United States. Emails were sent to remind individuals to return the survey, with 156 useable surveys being returned for a response rate of 26%. Most of the survey respondents were men (60%) and White (93%). The average age of the respondents was 41 years and 80% of the sample reported attaining at least a college degree. Respondents reported working an average of 52 hours each week and had been with the organization an average of 13 years. Measures Satisfaction with support initiatives was measured using a scale that was created for this study. The organization offered eleven discretionary benefits. Respondents indicated whether they had used the benefit and their level of satisfaction with benefits that had actually been used on a five-point scale ranging from very dissatisfied (1) to very satisfied (5). Given that employees’ satisfaction with benefits could vary depending on whether they had had a chance to use the situational benefits, the career-related (n = 85) and family-related (n = 43) benefits were calculated for only those employees who had used them. A factor analysis was completed and resulted in a three-factor solution. The first factor was labeled time-related benefits and included flexible work

Organizational Support Initiatives   31

arrangements, vacation days, and personal days (α = .76). The second factor was labeled career-related benefits and included career assistance, tuition reimbursement, employee assistance program, and leave of absence (α = .88). The third factor was labeled family-related benefits and included emergency child care, near site daycare, child and elder care resource and referral service, and adoption assistance (α = .52). Job satisfaction was measured using Hackman and Oldham’s (1980) three-item scale. Scale items included: “Generally speaking, I am very satisfied with my job,” “I frequently think of quitting my job” and “I am generally satisfied with the kind of work I do in my job.” A five-point response scale ranging from very dissatisfied (1) to very satisfied (5) was used (α = .72). Organizational commitment was measured with the nine item version of the Organizational Commitment Questionnaire developed by Porter and Smith (1970). Scale items included: “I am willing to put in a great deal of effort to help my organization be successful,” “I am proud to tell others that I am a part of my current organization,” and “I would accept almost any kind of assignment to keep working at my current organization.” A five-point response scale ranging from strongly disagree (1) to strongly agree (5) was used (α = .88). Quality of life was measured using a scale from Quinn and Shepard’s (1979) larger Quality of Employment Survey (QES) which queried about quality of life. The quality of life component has been extensively employed by researchers (Fusilier et al., 1986; Gaitley, 1996; Ganster & Fusilier, 1986; O’Malley, 1994; Parasuraman et al., 1989; Staines, Pottick, & Fudge, 1986). Respondents described their life in terms of nine semantic differential scales (e.g., boring—interesting, miserable—enjoyable, worthwhile— useless) as well as answering two global questions concerning respondents’ overall satisfaction and happiness with their life (e.g., “How satisfying do you find the ways you’re spending your life these days?”) A seven-point response scale ranging from very satisfying (1) to very dissatisfying (7) was used (α = .88). Job involvement was measured using the five item composite scale that combines Lodahl and Kejner’s (1965) four item scale with Kanungo’s (1982) one item scale. Scholars have used this composite scale to measure job involvement (Frone, Russell, & Cooper, 1992; Parasuraman, Purohit, Godshalk, & Beutell, 1996). Sample items included: “Most of the important things that happen to me involve my work,” “I live, eat, and breathe my job,” and “My work has a great deal of personal meaning to me.” A 5-point response scale ranging from strongly agree (1) to strongly disagree (5) was used (α = .78). Control variables used in this study were the employee’s sex (coded 1 = men, 2 = women) and age (measured continuously in years). Sex is an

32  Y. S. Purohit, C. A. Simmers, S. E. Sullivan and S. Gayle Baugh

important demographic variable for the examination of work-family conflict and work-family balance (Litzky et al., 2008) and may influence the need for organizational support policies and usage (Friedman & Greenhaus, 2000; Parasuraman & Greenhaus, 1992). Previous studies have reported asymmetric sex-role stereotypes and differences in work values and role identity among women and men (Archer & Lloyd, 2002; Cinamon & Rich, 2002; Litzky et al., 2008). Likewise, age is also an important demographic covariate of work and nonwork experiences. Generally, older employees report lower levels of work-family conflict and a keener focus on work-life balance (Martins, Eddleston, & Veiga, 2002; Winslow, 2005). Older individuals may be used to managing the multiple demands at the work-family interface and are, therefore, more likely to have worked out feasible divisions of labor at home (Hardill & Watson, 2004; Sekaran, 1986). Results Table 2.1 presents the means, standard deviations, and zero order correlations of all variables included in the study. We examined the correlations to help in identifying the presence of any multicollinearity that may violate the assumptions of multiple regression analyses. Although there is no definitive answer about the cut-off value in correlation for multicollinearity to be a problem, a correlation of 0.80 or greater is considered high enough to warrant remedial action (Miller, 1991). None of the correlations among the study variables were greater than 0.65, suggesting the multicollinearity is probably not an issue (Berry & Feldman, 1985). Hierarchical moderated regression analyses were used to test the relationships hypothesized in the model. Sex and age were entered in the first step of the regression equations predicting job satisfaction, organizational commitment, and quality of life as controls to avoid confounding the results due to covariation of these background variables with other variables in the model. Satisfaction levels with the three organizational benefits (e.g., timerelated, career-related, and family-related) were entered in the second step. Job involvement was entered in the third step. In the fourth and final step, the three cross-product interaction terms were entered. As can be seen in Table 2.1, satisfaction with the three kinds of organizational benefits (time, career, and family-related benefits) were all related, with correlations ranging from r = .35 (p < .01) for time-related benefits and family-related benefits to r = .50 (p < .01) for career-related benefits and family-related benefits. Of the three benefits, only family-related benefits were significantly correlated to two of the outcome variables. Family-related benefits were significantly and positively correlated with

33

   .76

   .88

  3.32

  3.26

  3.20

  3.31

  3.46

  3.59

Career-related benefits

Family-related benefits

Job involvement

Organizational Commitment

Job Satisfaction

Quality of Life

Correlation is significant at the 0.01 level (2-tailed)

Correlation is significant at the 0.05 level (2-tailed)

*

**

Sex (1 = men; 2 = women)

 .02

–.00

–.08

  .04

–.03

–.04

–.10

  .72

Age

a

   .63

   .73

  1.04

  1.07

   .97

  3.83

Time-related benefits

10.03

  1.40

40.83

SD

Sexa

Age

Mean

.17*

.08*

.08*

.03*

.13*

.29*

–.14**

–.24**

Sex

.11**

.15**

.11**

.12**

.35**

.45**

TimeRelated Benefits

.22**

.25**

.05**

–.04***

.50**

CareerRelated Benefits

.15*

.24*

.22*

–.02**

Family Related Benefits

.21*

.52**

.37**

Job Involvement

.23**

.63**

Organization Commitment

Table 2.1. Mean, Standard Deviation, and Correlations of Study Variables

.40**

Job Satisfaction

34  Y. S. Purohit, C. A. Simmers, S. E. Sullivan and S. Gayle Baugh

job satisfaction (r = .24, p < .05) and organizational commitment (r = .22, p < .05). The proposed moderator, job involvement, was significantly and positively related to job satisfaction (r = .52, p < .01), organizational commitment (r = .37, p < .01), and quality of life (r = .21, p < .01). The control variable of sex was only significantly correlated with age (r = -.24, p < .01). The control variable of age was significantly and positively correlated to satisfaction with career-related benefits (r = .29, p < .05) and quality of life (r = .17, p < .05). Table 2.2 displays the results of the hierarchical moderated regression analyses. The first hypothesis suggested that an individual’s satisfaction with organizational support initiatives would be positively related to job satisfaction. While the respondents’ satisfaction with career-related benefits was a predictor of job satisfaction (β = 2.42, p < .01), satisfaction with time-related and family-related benefits were not. Thus, Hypothesis 1 was only partially supported. The second hypothesis suggested that an individual’s satisfaction with organizational support initiatives would be positively related to organizational commitment. Satisfaction with career-related benefits (β = -2.06, p < .05) and with family-related benefits (β = .62, p < .01) predicted organizational commitment. However, the relationship between satisfaction with career-related benefits and organizational commitment was not in the hypothesized direction. Satisfaction with time-related benefits was not a significant predictor of organizational commitment. Thus, Hypothesis 2 was only partially supported and was partially contradicted. The third hypothesis suggested that an individual’s satisfaction with organizational support initiatives would be positively related to quality of life. Both satisfaction with career-related benefits (β = 4.45, p < .001) and family-related benefits (β = –.69, p < .001) predicted quality of life. However, the relationship between family-related benefits and quality of life was not in the hypothesized direction. Satisfaction with time-related benefits was not a significant predictor of quality of life. Thus, Hypothesis 3 was only partially supported and was partially contradicted. The fourth hypothesis posited that job involvement moderates the relationship between satisfaction with organizational benefits and job satisfaction, organizational commitment, and quality of life and that the relationship would be stronger for those with low job involvement than those with high job involvement. Table 2.2 indicates that the β coefficients for the interaction terms were only significant for satisfaction with careerrelated benefits and not for either satisfaction with time-related benefits or family-related benefits. Job involvement moderated the relationships between career-related benefits and job satisfaction (β = -2.40, p < .01), organizational commitment (β = 2.21, p < .05), and quality of life (β = –4.81, p < .001).

35

*p < .05, **p < .01, ***p < .001

a

sex (1 = men; 2 = women)

Table 2.2. Moderated Hierarchical Multiple Regression Analysis

36  Y. S. Purohit, C. A. Simmers, S. E. Sullivan and S. Gayle Baugh

Given this result, to test the second part of Hypothesis 4 (each of the outcomes will be stronger for individuals with low job involvement than for those with high job involvement), we only performed subgroup analysis on satisfaction with career-related benefits for the three work attitudes. We divided the sample using a mean-deviating technique into high and low job involved individuals (Whisman & McClelland, 2007) and compared the beta coefficients using Arnold’s t-test (Aiken & West, 1991). Lower and higher job involved individuals differed significantly only with respect to the relationship between satisfaction with career-related benefits and organizational commitment. Satisfaction with career-related benefits had a much stronger effect on organizational commitment for individuals with low job involvement than for those with high levels of job involvement, but contrary to expectations, it was in the negative direction. Although a moderating effect was found for job involvement on the relationship between satisfaction with career-related benefits and both job satisfaction and quality of life, significant differences in the slope coefficients between the high and low job-involved groups were not found. Thus, support for Hypothesis 4 is ambiguous. The significant interactions are difficult to interpret in the absence of confirming data with respect to the differences in slopes. While a significant difference in slopes was found between groups of individuals with high and low job involvement with respect to the relationships between satisfaction with career-related benefits and organizational commitment, the difference was opposite of that predicted—the relationship was negative for low job involved individuals. These results are reported in Table 2.4 and depicted in Figures 2.2, 2.3, and 2.4. Discussion The purpose of this study was to examine whether there was a significant positive relationship between satisfaction with discretionary organizational support benefits and job satisfaction, organizational commitment, and quality of life as well as whether job involvement moderates these relationships. The predictions regarding the positive relationship between satisfaction with discretionary work-life benefits and attitudinal responses were only partially supported. Specifically, we examined the relationship between satisfaction with three types of discretionary benefits which support work-life balance—(a) time-related benefits, (b) career-related benefits, and (c) family-related benefits—and three attitudes (job satisfaction, organizational commitment, and quality of work life). Of these nine relationships, only three were statistically significant and in the predicted direction. Surprisingly, two of the relationships were significant, but were in the opposite direction of what was predicted.

Organizational Support Initiatives   37 Table 1.4.  Comparison of Beta Coefficients Career-Related Benefits and Worker Attitudes Dependent Variable Job Satisfaction

Organizational commitment

Quality of Life

Job Involvement

Independent Variable

Beta

Standard Error

t   0.6480

High

Career-related benefits (CRB)

–.321

.192

Low

Career-related benefits (CRB)

  .336

.143

High

Career-related benefits (CRB)

  .341

.206

Low

Career-related benefits (CRB)

  .177

.147

High

Career-related benefits (CRB)

–.086

.190

Low

Career-related benefits (CRB)

–.143

.125

  –2.7443

–1.006

t values >1.68 are significant at p < .05.

Figure 2.2.  Job involvement moderating satisfaction with career-related benefits and job satisfaction.

38  Y. S. Purohit, C. A. Simmers, S. E. Sullivan and S. Gayle Baugh

Figure 2.3.  Job involvement moderating the satisfaction with career-related benefits and organizational commitment.

Figure 2.4.  Job involvement moderating the satisfaction with career-related benefits and quality of life.

Satisfaction with organizational support in the form of time-related benefits, which included flexible work arrangements, vacation days, and personal days, was not significantly related to any of the three attitudes. One possible explanation for the lack of significant findings regarding the absence of relationships between these support initiatives and the three attitudinal responses is that while these benefits are not legally required, white-collar and professional employees may have come to expect them. For example, there is a high percentage of full-time U.S. workers who are given a paid leaves for vacations (91%), holidays (90%), illness (75%), attendance at funerals (71%) and personal reasons (44%). If employees perceive this organizational support to be the norm, these benefits may not be viewed as something special their organization is offering beyond what

Organizational Support Initiatives   39

other organizations are doing to provide support for their employees. As advances in technology increasingly permit individuals to do work in many locations other than the office, time-related benefits may be less meaningful and have less impact upon employees’ job satisfaction, organizational commitment, and quality of work life. Additionally, employees may not fully use these benefits. For instance, 40% of workers with paid vacation days do not fully utilize them (U.S. Travel Association, 2014) and, even when on vacation or leave, many workers are still tethered to their office by laptops and smartphones (Lanaj, Johnson, & Barnes, 2014; Lee, Chang, Lin, & Cheng, 2014). In contrast, as expected, career-related benefits were significantly and positively related to job satisfaction and quality of work life. Benefits such as career assistance and tuition reimbursement may be viewed by employees as meaningful ways in which the organization is supporting employees’ desires to enhance their career and engage in self-development, while employee assistance programs and leaves of absence may be viewed as means by which the organization is helping the employee balance work/ nonwork domains in atypical circumstances. Contrary to expectations, however, while satisfaction with career-related benefits was significantly related to organizational commitment, it was in the inverse direction. It may be that benefits such as providing tuition reimbursement, for example, do not increase organizational commitment if these types of benefits are not coupled with opportunities for advancement, increased salary or the chance to use new skills upon completion of a degree. There may be other factors, such as perceived procedural and distributive justice, which have a greater impact on organizational commitment than do career-related benefits. Further research is needed to examine what other factors may influence the relationship between career-related benefits and organizational commitment. The findings regarding the relationship between satisfaction with familyrelated benefits and attitudes were also unexpected. While as hypothesized there was a significant and positive relationship between family-related benefits and organizational commitment, the hypothesized relationship with job satisfaction with not supported. Moreover, while the relationship between satisfaction with family-related benefits and quality of life was statistically significant, it was in the opposite direction than hypothesized. It may be that while employees are satisfied with family-related benefits, such as emergency child care and child/elder care resources, other factors, such as meaningful work and skill development, have a greater impact upon quality of life. Other variables untested in this study, such as supportive supervisors and coworkers, may influence the relationship between satisfaction with family-related benefits and quality of life. Alternatively, it may be that although employees are satisfied with the work-life benefits

40  Y. S. Purohit, C. A. Simmers, S. E. Sullivan and S. Gayle Baugh

provided, such benefits are nonetheless viewed as insufficient. Despite the fact that the work-life benefits included in this study are not legally required, employees may feel that more comprehensive support for worklife balance is needed. This study’s findings may also have been affected by the low usage of the family-related support policies by survey respondents. It may be that individuals want to take care of matters such as child- and elder-care outside of the workplace, in an effort to separate the work and nonwork aspects of their life. It may also be the case that while benefits are available to employees, they are discouraged from using these benefits by superiors or coworkers. Finally, it was hypothesized that job involvement would moderate the relationship between satisfaction with time-, career-, and family-related benefits and the three outcomes. Job involvement, however, moderated only the relationship between satisfaction with the career-related benefits and the three attitudes. It may be that career-related benefits are more salient in the workplace than time- or family-related benefits. It is important to note, as well, that the moderating effect of job involvement on the relationship between career-related benefits and work attitudes was confirmed by analysis of slopes only for organizational commitment and was opposite than predicted, suggesting that organizations should be cautious with respect to the signaling value of such benefits to employees. References Abbey, A., Abramis, D. J., & Caplan, R. D. (1985). Effects of different sources of social support and social conflict on emotional well-being. Basic and Applied Social Psychology, 6, 111–120. Abraham, R. (1998). Emotional dissonance in organizations: Antecedents, consequences, and moderators. Genetic, Social and General Psychology Monographs, 124, 229–246 Abraham, R. (1999). The impact of emotional dissonance on organizational commitment and intent to turnover. Journal of Psychology, 133, 441–455. Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage. Archer, J., & Lloyd, B. (2002). Sex and gender (2nd ed.). Cambridge, England: Cambridge University Press. Ballout, H. (2007). Career success: The effects of human capital, person-environment fit and organizational support. Journal of Managerial Psychology, 22, 741–765. Baran, B. E., Shanock, L. R., & Miller, L. R. (2012). Advancing organizational support theory into the twenty-first century world of work. Journal of Business and Psychology, 27, 123–147. Berry, W., & Feldman, S. (1985). Multiple regression in practice. Newbury Park, CA: SAGE.

Organizational Support Initiatives   41 Bureau of Labor Statistics. (2013). Employment characteristics of families, 2012. Retrieved from www.bls.gov/opub/ted/2013/ted_20130430.htm Chew, Y. T., & Wong, S. K. (2008). Effects of career mentoring experience and perceived organizational support in employee commitment and intentions to leave: A study among hotel workers in Malaysia. International Journal of Management, 25, 692–700. Cinamon, R., & Rich, Y. (2002). Gender differences in the importance of work and family roles: Implications for work-family conflict. Sex Roles, 47, 531–541. Cropanzano, R., & Mitchell, M. S. (2005). Social exchange theory: An interdisciplinary review. Journal of Management, 31, 874–900. Dawley, D. D., Andrews, M. C., & Bucklew, N. S. (2008). Mentoring, supervisor support, and perceived organizational support: What matters most? Leadership & Organization Development Journal, 29, 235–247. Eby, L., Casper, W., Lockwood, A., Bordeaux, C., & Brinley, A. (2005). A twenty year retrospective on work and family research in IO/OB journals: A review of the literature. Journal of Vocational Behavior, 66, 124–197. Eisenberger, R., Fasolo, P., & Davis-LaMastro, V. (1990). Perceived organizational support and employee diligence, commitment, and innovation. Journal of Applied Psychology, 75, 51–59. Eisenberger, R., Huntington, R., Hutchison, S., & Sowa, D. (1986). Perceived organizational support. Journal of Applied Psychology, 71, 500–507. Friedman, S., & Greenhaus, J. (2000). Work and family-allies or enemies? New York, NY: Oxford University Press. Friedman, D. E., & Johnson, A. A. (1997). Moving from programs to culture change: The next stage for the corporate work family agenda. In S. Parasuraman & J. Greenhaus (Eds.), Integrating work and family: Challenges and choices for a changing world (pp. 192–208). West Port, CT: Quorum Books. Frone, M. R., Russell, M., & Cooper, M. L. (1992). Antecedents and outcomes of work-family conflict: Testing a model of the work-family interface. Journal of Applied Psychology, 77, 65–78. Fusilier, M. R., Ganster, D. C., & Mayes, B. T. (1986). The social support and health relationship: Is there a gender difference? Journal of Occupational Psychology, 57, 145–153. Gaitley, N. (1996). The influence of social support and locus of control on the wellbeing of men and women in the work-family domain (Unpublished doctoral dissertation). Drexel University, Philadelphia, PA. Ganster, D. C., & Fusilier, M. R. (1986). Role of social support in the experience of stress at work. Journal of Applied Psychology, 71, 102–110. Garger, E. M. (1999). Holding on to high performers: A strategic approach to retention. Compensation & Benefits Management. 15, 10–17 Gibney, R., Zagenczyk, T. J., & Masters, M. (2009). The negative aspects of social exchange: An introduction to perceived organizational obstruction. Group & Organization Management, 34, 665–697. Glass, J. L., & Estes, S. B. (1997). The family responsive workplace. Annual Review of Psychology, 23, 289–313

42  Y. S. Purohit, C. A. Simmers, S. E. Sullivan and S. Gayle Baugh Greenberger, E., Goldberg, W. A., Hamill, S., O’Neil , R., & Payne, C. K. (1989). Contribution of a supportive work environment to parents’ wellbeing and orientation to work. American Journal of Community Psychology, 17, 755–783. Greenhaus, J. H., & Parasuraman, S. (1994). Work-family conflict, social support, and well-being. In M. J. Davidson & R. J. Burke (Eds.), Women in management: Current research issues. London, England: Paul Chapman. Hackman, J. R., & Oldham, G. R. (1980). Work redesign, Reading, MA: AddisonWesley. Hayghe, H. (1984). Working mothers have become familiar feature of todays workplace. Bureau of Labor Statistics report. Retrieved from www.bls.gov/opub/ mlr/1984/12/rpt1full.pdf Hardill, I., & Watson, R. (2004). Career priorities within dual career households: An analysis of the impact of child rearing upon gender participation rates and earnings. Industrial Relations Journal, 35, 19–37. Heneman, H. G., III, & Judge, T. A. (2000). Compensation attitudes. In S. L. Rynes & B. Gerhart (Eds.), Compensation in organizations: Current research and practice (pp. 61–103). San Francisco, CA: Jossey-Bass. Hochschild, A. (1989). The second shift. New York: Viking. Homans, G. (1961). Social behavior: Its elementary forms. New York, NY: Harcourt, Brace, Jovanovich. Hutchison, S. (1997). A path model of perceived organizational support. Journal of Social Behavior and Personality, 12, 159–174. Igbaria, M., Parasuraman, S., & Badawy, M. K. (1994). Work experiences, job involvement, and quality of work life among information system personnel. MIS Quarterly, 18, 175–201. Kanungo, R. N. (1982). Measurement of job and work involvement. Journal of Applied Psychology, 67, 341–349. Lanaj, K., Johnson, R. E., & Barnes, C. M. (2014). Beginning the workday yet already depleted? consequences of late-night smartphone use and sleep. Organizational Behavior and Human Decision Processes, 124, 11–23. Lawler, E. E., & Hall, D. T. (1970). Relationship of job characteristics to job involvement, satisfaction and intrinsic motivation. Journal of Applied Psychology, 54, 305–312. Lee, Y. K., Chang, C. T., Lin, Y., & Cheng, Z. H. (2014). The dark side of smartphone usage: Psychological traits, compulsive behavior and technostress Computers in Human Behavior, 31, 373–383. Lenaghan, J. A., & Eisner, A. B. (2006). Employers of choice and competitive advantage: The proof of the pudding is in the eating. Journal of Organizational Culture, Communication, and Conflict, 10, 99–109 Leveson, L., Joiner, T. A., & Bakalis, S. (2009). Managing cultural diversity and perceived organizational support: Evidence from Australia. International Journal of Manpower, 30, 377–392. Litzky, B. E., & Greenhaus, J. H. (2007). The relationship between gender and aspirations to senior management. Career Development International, 12, 637–659. Litzky, B. E., Purohit, Y. S., & Weer, C. H. (2008). Beliefs about the success of dual-earner relationships: Toward the development of a normative beliefs measurement scale. North American Journal of Psychology, 10, 603–624.

Organizational Support Initiatives   43 Lodahl, T. M., & Kejner, M. (1965). Definition and measurement of job involvement. Journal of Applied Psychology, 49, 24–33. Mainiero, L. A., & Sullivan, S. E. (2006). The opt-out revolt: How people are creating kaleidoscope careers outside of companies. New York, NY: Davies-Black. Mannheim, B., Baruch, Y., & Tal, J. (1997). Alternative models for antecedents and outcomes of work centrality and job satisfaction of high-tech personnel. Human Relations, 50, 1537–1562. Martins, L., Eddleston, K., & Veiga, J. (2002). Moderators of the relationship between work-family conflict and career satisfaction. Academy of Management Journal, 45, 399–409. Md-Sidin, S., Sambasivan, M., & Ismail, I. (2010). Relationship between workfamily conflict and quality of life: An investigation into the role of social support. Journal of Managerial Psychology, 25, 58–81. Mello, J. (2002). Strategic human resource management. Mason, OH: South-Western. Miceli, M. P., & Lane, M. C. (1991). Antecedents of pay satisfaction: A review and extension. In K. M. Rowland & G. R. Ferris (Eds.). Research in personnel and human resource management (Vol. 9, pp. 235–309). Greenwich, CT: JAI Press. Mihelic, K. K. (2014). Commitment to life roles and work-family conflict among managers in a post-socialist country. Career Development International, 19, 204–221. Miller, D. C. (1991). Handbook of research design and social measurement (5th Ed.). Newbury Park, CA: Sage. Moorman, R. H., Blakely, G. L., & Niehoff, B. P. (1998). Does perceived organizational support mediate the relationship between procedural justice and organizational citizenship behavior? Academy of Management Journal, 41, 351–357. Mortimer, J. T., & Lorence, J. (1989). Satisfaction and involvement: Disentangling a deceptively simple relationship. Social Psychology Quarterly, 52, 249–265. Mowday, R. T., Porter, L., & Steers, R. (1982). Employee-organization linkages: The psychology of commitment, absenteesism, and turnover. New York, NY: Academic Press Muse, L. A., & Stamper, C. L. (2007). Perceived organizational support: Evidence for a mediated association with work performance. Journal of Managerial Issues, 12, 517–535. Naumann, S. E., Bennett, N., Bies, R. J., & Martin, C. L. (1998). Laid off, but still loyal: The influence of perceived justice and organizational support. International Journal of Conflict Management, 9, 356–368. Neault, R. A., & Pickerell, D. A. (2005). Dual-career couples: The juggling act. Canadian Journal of Counselling, 39, 187–198. Ngo, H.-Y., Foley, S., Ji, M. S., & Loi, R. (2014). Work satisfaction of Chinese employees: A social exchange and gender-based view. Social Indicators Research, 116, 457–473. Ngo, H.-Y., & Tsang, A. W. ( 1998). Employment practices and organizational commitment: Differential effects for men and women? International Journal of Organizational Analysis, 6, 251–266 Parasuraman, S., & Greenhaus, J. H. (1992, August). An exchange perspective on support provided by partners in two-career relationships. Paper presented at the Academy of Management Meeting, Las Vegas, NV.

44  Y. S. Purohit, C. A. Simmers, S. E. Sullivan and S. Gayle Baugh Parasuraman, S., Greenhaus, J. H., & Granrose, C. S. (1992). Role stressors, social support, and well-being among two-career couples. Journal of Organizational Behavior, 13, 339–356. Parasuraman, S., Greenhaus, J. H., Rabinowitz, S., Bedeian, A., & Mossholder, K. (1989). Work and family variables as mediators of the relationship between wives’ employment and husbands’ well-being. Academy of Management Journal, 32, 185–201. Parasuraman, S., Purohit, Y. S., Godshalk, V. M., & Beutell, N. (1996). Work and family variables, entrepreneurial career success and psychological well-being. Journal of Vocational Behavior, 48, 375–400. Payne, R. L., & Jones, J. G. (1987). Stress and health: Issues in research methodology. In S. V. Kasl & C. L. Cooper (Eds.), Measurement and methodological issues in social support. New York, NY: Wiley. Porter, L. W. & Smith, F. J. (1970). The etiology of organizational commitment (Unpublished manuscript). University of California at Irvine. Quinn, R. P., & Shepard, G. L. (1979). The 1977 Quality of Employment Survey. Ann Arbor, MI.: Survey Research Center. Sekaran, U. (1986). Dual-career families: Contemporary organizational and counseling issues. San Francisco, CA: Jossey Bass. Shamir, B., & Solomon, I. (1985). Work-at-home and quality of working life. Academy of Management Review, 10, 455–464. Shin, M., Wong, N. W., Simko, P. A., & Ortez-Torres, B. (1989). Promoting the wellbeing of working parents: Coping, social support and flexible job schedules. American Journal of Community Psychology, 17, 31–55. Staines, G. L., Pottick, K. J., & Fudge, D. A. (1986). Wives’ employment and husbands’ attitudes toward work and life. Journal of Applied Psychology, 71, 118–128. Sturges, J., Conway, N., & Liefooghe, A. (2010). Organizational support, individual attributes and the practice of career-self management behavior. Group & Organization Management, 35, 108–141. Tardy, C. H. (1985). Social support measurement. American Journal of Community Psychology, 13, 187–202. Thompson, C. A., & Prottas, D. J. (2006). Relationships among organizational family support, job autonomy, perceived control, and employee well-being. Journal of Occupational Health Psychology, 11, 100–118. U.S. Travel Association. (2014, August). Overwhelmed America: Why don’t we use our earned leave? New York, NY: GfK Public Affairs and Corporate Communications. Wallace, C., Edwards, B., Arnold, T., Frazier, M. L., & Finch, D. M. (2009). Work stressors, role-based performance, and the moderating role of organizational support. Journal of Applied Psychology, 94, 254–262. Wegge, J., Schmidt, K. H., Parkes, C., & van Dick, R. (2007). “Taking a sickie”: Job satisfaction and job involvement as interactive predictors of absenteeism in a public organization. Journal of Occupational and Organizational Psychology, 80, 77–89.

Organizational Support Initiatives   45 Williams, M. L., Brower, H. H., Ford, L. R., Williams, L. J., & Carraher, S. (2008). A comprehensive model and measure of compensation satisfaction. Journal of Occupational and Organizational Psychology, 81, 639–668. Winslow, S. (2005). Work-family conflict, gender, and parenthood, 1977–1997. Journal of Family Issues, 26, 727–755. Whisman, M. A., & McClelland, G. H. (2007). Designing, testing, and interpreting interactions and moderator effects in family research. Journal of Family Psychology, 19, 111–120.

chapter 3

Can Managers of Every Generation Have it All? Examining the Relationship Between Work-Life Balance and Promotability for Baby Boomers and Generation X Sarah A. Stawiski, William A. Gentry, and Lisa E. Baranik

Balancing the demands of work, family, and leisure is a daunting challenge for many managers. Specific examples of activities that managers balance with their work responsibilities include caring for young children, maintaining a fitness regimen, and nurturing personal relationships. While the roles managers play outside of work may demand time and energy away from their professional obligations, they may also provide valuable resources that can be applied on the job. One goal of our study is to assess whether balance between work and personal life can help managers in terms of career advancement. Further, this relationship may vary depending on demographic characteristics of the managers themselves as well as the characteristics of the managers’ bosses. Consequently, this chapter will also assess whether the proposed relationship between balance and career

Striving for Balance, pp. 47–71 Copyright © 2016 by Information Age Publishing All rights of reproduction in any form reserved.

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48  S. A. Stawiski, W. A. Gentry and L. E. Baranik

advancement is true regardless of the generational membership of managers or their bosses. Work-life (W-L) balance has been examined in a number of different fields (e.g., organizational sciences, sociology), and studied under a number of different names. Researchers examining how well managers balance the demands of work along with family or life roles interchangeably use the terms work-life (W-L) and work-family (W-F) balance (e.g. Rice, Frone & McFarlin, 1992). We believe that this balancing act can occur regardless of care-giving responsibilities, and adopt W-L balance from this point forward. In this chapter, W-L balance is defined as balancing work priorities with personal life so that neither is neglected (Center for Creative Leadership, 2004; Lyness & Judiesch, 2008) Personal life can include time with family, friends, hobbies, sports, and other leisure activities. Much of the literature on W-L balance has focused on individuals’ health and well-being outcomes. A recent content analysis of 190 studies found that the most commonly studied criteria in work-family studies were work attitudes, work-family interaction, health and wellness, and family attitudes (Eby, Casper, Lockwood, Bordeau, & Brinley, 2005). Research has demonstrated that successfully managing time inside and outside of work is important to managers’ well-being. For example, employees with more balance between work and family are more satisfied with their family, leisure activities, and jobs (e.g., Rice, Frone, & McFarlin, 1992), and have higher reports of quality of life (Greenhaus, Collins, & Shaw, 2003). In contrast, research demonstrating how W-L issues affect career success is sparse (Casper, Eby, Bordeaux, Lockwood, & Lambert, 2007). Employee work behaviors (e.g., work performance, absenteeism, turnover, and tardiness) were only examined in 5% of the articles examined by Eby and colleagues (2005). This lack of research focusing on career outcomes represents a critical gap in the literature, as employers are more likely to support W-L balance initiatives if they are also related to manager work behaviors and if there is evidence that W-L balance is positively related to favorable outcomes such as career advancement, specifically, manager promotability. Promotions within one’s career can provide important benefits to a person, such as a sense of accomplishment, encouragement, and increased pay. Further, by demonstrating this link, individual managers may feel encouraged to make W-L balance a priority. Managerial Promotability Evaluation of managers’ promotability is an important topic to individuals working in organizations and for practitioners working in such fields as talent management, succession planning, or human resources

Baby Boomers, Gen Xers, and Balance   49

(De Pater, Van Vianen, Bechtoldt, & Klehe, 2009). Promotability refers to the perception of how likely a manager is to succeed in performing the new role, if promoted. From the individual worker’s perspective, knowing if he or she is promotable is a key indicator of actual upward mobility and career success (Van Scotter, Motowidlo, & Cross, 2000; Wayne, Liden, Kraimer, & Graf, 1999). From the practitioner’s perspective, knowing who is promotable can help fill the pipeline of talented individuals so that an organization can be better equipped to function and be successful in the future (Till, 2007). Research suggests companies that hire from within can expect better economic performance, increased manager commitment and loyalty, and financial savings by not having to replace talent externally (Friedman, 1991). Empirical research on the predictors of promotability is lacking (De Pater et al., 2009). To take the field a step forward, the purpose of this chapter is to make a theoretical case and present findings from a study that examines whether W-L balance is positively related to perceptions of promotability. Although W-L balance has been the subject of extensive research (see Eby et al., 2005, for a review), few studies of W-L balance have been theoretically or empirically linked specifically to promotability perceptions (for a recent exception, see Hoobler, Wayne, & Lemmon, 2009). Our research also extends the field by examining the aforementioned relationship among the two generational cohorts most prevalent in the workplace: Generation X and Baby Boomers (Beutell & Wittig-Berman, 2008; Cennamo & Gardner, 2008). Because of the perceived differences between these two groups, this research is valuable, as it will examine whether such differences are supported empirically. In this chapter we will first give an overview of the W-L balance literature and explain the theoretical rationale behind the proposed positive relationship between W-L balance and promotability. Next, we will present a brief overview of the generational differences existing in the workplace and build hypotheses about why the aforementioned relationship may differ for the Generation X and Baby Boomer generation. Using data from 664 practicing managers from the United States, we will explain the method used in this research as well as the results, and will end with a discussion of the findings, implications for the field, study limitations, and possible future research directions. The Modern Organizational Context With Work-Life Balance: A Brief Overview Modern organizations are bringing more attention to initiatives, policies, needs, and support systems for W-L balance, such as flexible work

50  S. A. Stawiski, W. A. Gentry and L. E. Baranik

schedules, telecommuting options, and employee assistance programs. However, the gap between attention to and the reality of actual adoption of W-L balance in organizations still exists (Kossek, Lewis, & Hammer, 2010). One of the impediments of total adoption of W-L balance in organizations is the lingering, yet strong, stereotype of the ideal worker in organizations: the belief that, while at work, managers should always put their work roles ahead of their family or personal life roles (Rapoport, Bailyn, Fletcher, & Pruitt, 2002). The ideal worker stereotype has strong roots in workplace norms, in that the workplace was designed as if employees only had the role of work, and did not have family and personal life competing for time and resources during work hours (Kanter, 1977). This belief may make talking about or attending to life outside of work less acceptable, thus making balance more challenging. However, a growing sentiment exists that the traditional stereotype of the ideal worker should shift toward embracing the fact that a worker in contemporary organizations does in fact have multiple roles. Kossek and colleagues (2010) explain that work-life initiatives need to be brought from the fringe into mainstream organizational practice and research needs to be designed to understand those who do not conform to the traditional ideal worker stereotype. They continue that with the technology available today, people need W-L balance initiatives to help separate work and life boundaries, or may need initiatives to help integrate the two roles better, all so that they can work to their utmost effectiveness. How an ideal worker is conceptualized can shape whether or not organizations implement W-L balance initiatives and support systems and whether W-L balance would facilitate or impede career success. If W-L balance is not related to promotability perceptions, or if it is negatively related to promotability, evidence for the traditional image of an ideal worker still remains. If however, W-L balance is positively related to promotability perceptions, thoughts of what an ideal worker is may be shifting towards a worker who can successfully balance roles at work and away from work. We turn now to the theoretical background and literature review on W-L balance. Theoretical Background of Work-Life Balance There are two competing theoretical perspectives that are relevant to the question of whether W-L balance can actually be related to perceptions of promotability. One is the conflict perspective, which focuses on the idea that energy and involvement in one role necessarily involves a depletion of these resources in another role. The most common definition of W-L conflict comes from Greenhaus and Beutell (1985), who state that “participation in the work (family) role is made more difficult by virtue

Baby Boomers, Gen Xers, and Balance   51

of participation in the family (work) role” (p. 77). The rationale behind this perspective is that there are finite hours in the day and that time and energy directed towards one role necessarily take away time and energy from another role. Thus, roles are in conflict with one another. Empirical evidence supports this perspective, showing that W-L conflict relates to variables such as increased stress, dissatisfaction with work and family, and poor physical health (Frone, 2003; Judge & Bretz, 1994; Judiesch & Lyness, 1999; Ng, Eby, Sorensen, & Feldman, 2005; Whitely, Dougherty, & Dreher, 1991). This conflict-based view supports the traditional viewpoint of an ideal worker as one who excels at work at the expense of success and satisfaction in his or her personal life. However, recent evidence is mounting that runs counter to the conflict perspective, such that multiple duties and roles may actually help people to be successful across work and life domains. This second theoretical perspective is called the facilitation perspective (also called work-family enrichment and positive work-family spillover) (Barnett & Hyde, 2001; Rothbard, 2001; Sieber, 1974), which states that involvement in one role results in perceived gains in another role (Graves, Ohlott, & Ruderman, 2007; Stephens, Franks, & Atienza, 1997). Based on the enhancement hypothesis (Marks, 1997; Sieber, 1974), the more roles individuals are involved in, the more resources they gain. These resources can include social support, coping skills, self-esteem, energy, and positive emotions. This perspective stems from Sieber’s (1974) suggestion that participation in multiple roles may provide a buffer for stress and failures in other roles. For example, a mother may bounce back quickly from a set-back at work, in part because she feels positive emotions and self-esteem due to her success at parenting. This perspective fits well with the emerging sentiment that an ideal worker can have and can balance multiple roles. W-L Balance and Career Outcomes Although there has not been much research conducted in this area, there is reason to believe that achieving W-L balance may be related to increased promotability perceptions. The facilitation perspective suggests that positive moods, values, and skills may transfer from the family domain to the work domain, and, as such, there are multiple reasons to expect that W-L balance should relate to promotability. First, greater role variety requires the development of a more adaptable and diverse repertoire of behaviors, which can be helpful in overall work performance. For example, Ruderman, Ohlott, Panzer, and King (2002) found that women in managerial roles reported that having to manage multiple tasks at home was good practice for multitasking on the job. Second, W-L balance leads to positive

52  S. A. Stawiski, W. A. Gentry and L. E. Baranik

outcomes because a person can become tolerant of different views and flexible in adjusting to the demands of diverse roles (Greenhaus & Powell, 2006). Research suggests people holding multiple roles are more respectful of individual differences (Ruderman et al., 2002) and have expanded world views (Kanter, 1977), both of which are considered desirable qualities of a manger. Bosses may notice the skills that their subordinate managers have developed (e.g., time management) because of balancing work and life (Lyness & Judiesch, 2008). Finally, bosses may assume that managers who are successfully balancing work and life have positive characteristics, such as good time management and adaptability. Therefore, bosses may perceive their subordinate managers who have good W-L balance as skilled at managing multiple roles of their lives, and therefore more likely to advance. Empirical evidence exists supporting the idea that W-L balance relates to managerial effectiveness. Managers who are committed to multiple life roles (e.g., occupational, marital, community) receive higher performance ratings by their own boss, peers, and subordinates (Ruderman et al., 2002), as well as composite performance ratings (Graves et al., 2007). In a promising study, Lyness and Judiesch (2008) demonstrated a positive relationship between perceptions of managers’ W-L balance and career advancement potential. Although these studies confirm the positive relationship between W-L balance and positive perceptions at work, there are many empirical gaps that remain unanswered. For one, measures of managerial effectiveness and performance used in studies (e.g., Graves et al., 2007; Ruderman et al., 2002) are weak proxies of promotability; performance in a manager’s current job does not necessarily equate to future performance in a job to which a manager could be promoted (Conger & Fulmer, 2003). Other studies, such as the one conducted by Lyness and Judiesch (2008), may have limitations as well. Their measure of career advancement potential addressed derailment dimensions (e.g., problems with interpersonal relationships), which also is not a direct measure of promotability. Furthermore, moderators of this relationship were not addressed in past studies, and still remain largely unexamined, leaving important questions about when and for whom this relationship holds. For instance, it is unclear whether this relationship would vary as a function of demographic variables such as gender, race, and level in the organization. The purpose of our study is to expand on what is known about the relationship between W-L balance and career outcomes by using the outcome of promotability ratings from a manager’s boss as the focus of our study. Of all the people who know the manager, the manager’s boss may be in the best position to rate the promotability of that manager. In fact, research has shown that boss ratings are the most common and reliable way to measure such outcomes (Conway, 2000; Conway & Huffcutt, 1997; Viswesvaran,

Baby Boomers, Gen Xers, and Balance   53

Ones, & Schmidt, 1996). Further, this study will overcome the limitation of the Lyness and Judiesch (2008) study which relied on a measure of derailment, a proxy for career advancement potential. In their study, ratings of behaviors that are linked to career plateaus or derailment were used rather than ratings of how promotable a manager is perceived. Following the facilitation perspective (Barnett & Hyde, 2001; Rothbard, 2001), managers who are successfully balancing multiple roles may have more resources (e.g., social support and coping skills) than managers who are not successfully balancing these roles. Therefore, we expect: Hypothesis 1: There will be a positive relationship between managerial self-ratings of W-L balance and boss ratings of the manager’s promotability. Generational Differences Managers in the workforce are increasingly diverse, particularly in generational membership. Significant events and other major environmental influences are theorized to impact the development of a generational cohort’s values, beliefs, and expectations (Macky, Gardner, & Forsyth, 2008). Over the past decade, researchers and practitioners have become increasingly aware of and interested in understanding how to best work with, motivate, manage, and develop those of different generational cohorts (Allerton, 2001; Chester, 2002; Lancaster & Stillman, 2002; Smola & Sutton, 2002). The two most prevalent generations in the U.S. workforce are Baby Boomers, those individuals born after the end of World War II until 1963, and Generation X, those born between 1964 and 1986 (Beutell & Wittig-Berman, 2008; Cennamo & Gardner, 2008), together making up more than 82% of the U.S. labor force (Bureau of Labor Statistics, 2009). A detailed description of these generations is beyond the scope this chapter. Several authors (e.g., Deal, 2007; Egri & Ralston, 2004; Sessa, Kabacoff, Deal, & Brown, 2007; Smola & Sutton, 2002) have described each of these groups elsewhere. The Baby Boomer generation includes individuals born between 1946 and 1963 during a major increase in the U.S. birth rate. This generation grew up in a time of dramatic social change, and was impacted by the Vietnam War, the Civil Rights Movement, and the assassination of John F. Kennedy. They also grew up in a time of general economic prosperity, affluence, and rapidly growing consumerism. In the 1980s, Baby Boomers gained a reputation as status-conscious young urban professionals or “yuppies” because they were categorized and stereotyped as materialistic, workaholics, and career-focused (Adler, 1984). Due to these shared life

54  S. A. Stawiski, W. A. Gentry and L. E. Baranik

experiences, Baby Boomers are often described and stereotyped as wanting self-fulfillment, highly valuing work sometimes at the expense of family, being particularly results driven, intending to stay long-term in organizations, giving maximum effort, and willing to “go the extra mile” (Egri & Ralston, 2004; Kupperschmidt, 2000; Parker & Chusmir, 1990; Smola & Sutton, 2002; Society for Human Resources Management, 2004; Strauss & Howe, 1991; Thau & Heflin, 1997). Generation X includes individuals born between 1964 and 1986. This cohort’s name came from books by Hamblett and Deverson (1964) and from Coupland (1991). These authors predicted that members of this generation (those coming of age at the turn of the century) would be apathetic (Deal, 2007). They grew up in the decade following the Vietnam War, were impacted by the 1973 oil crisis, the 1979 energy crisis, the 1980s economic recession, Black Monday in 1987, the savings and loan crisis, the Cold War, the AIDS epidemic, economic uncertainty, and the fall of communism. Their parents were more likely to divorce and lose their job due to organizational downsizing than any other generation before. This generation was shaped by these influences and, as a result, is regarded as independent, less committed to the organization, and more likely change jobs frequently. Generation X developed a greater sense of economic uncertainty and greater skepticism about the loyalty between employers and employees and see W-L balance as extremely important (Beutell & Wittig-Berman, 2008; Glass, 2007). Those in Generation X are described by others as being individualistic, risk-tolerant, self-reliant, entrepreneurial, and tech savvy as well as valuing informality, diversity, and W-L balance (Craig & Bennett 1997; De Meuse, Bergmann, & Lester, 2001; Deal, 2007; Egri & Ralston, 2004; Jurkiewicz & Brown, 1998; Kupperschmidt, 2000; Society for Human Resources Management, 2004; Tulgan, 1995). Similarities between Baby Boomers and Generation X. Recent research has found more similarities than differences across Baby Boomers and Generation X in certain values, beliefs, and attitudes (e.g., Deal, 2007; Gentry, Griggs, Deal, Mondore, & Cox, 2011). For instance, Jurkiewicz (2000) found many more similarities than differences across the generations in what members wanted in their job. In a study by Davis, Pawlowski, and Houston (2006), Baby Boomer and Generation X information technology professionals were more similar to one another than different in their opinions on work involvement, work attachment, commitment to the organization, and commitment to the profession. Gentry, Griggs, Deal, and Mondore (2009) found that the preferences for learning and development at work were more similar than different across the generations at work. Differences Between Baby Boomers and Generation X. Research suggests that certain generational differences in general do, in fact, exist (Egri & Ralston, 2004; Karp, Sirias, & Arnold, 1999; Smola & Sutton, 2002;

Baby Boomers, Gen Xers, and Balance   55

Twenge, Campbell, Hoffman, & Lance, 2010). However, some researchers are skeptical of attributing differences to generational factors. For instance, observed differences between generational cohorts can be explained by age or career stage (e.g., Rhodes, 1983). Differences between Baby Boomers and Generation X work values may exist simply because Baby Boomers have been in the work force longer. Based on traditional stereotypes and the research previously mentioned, the importance Baby Boomers and Generation X may place on W-L balance as well as work and life roles may be different. Baby Boomers have been described as being more job-focused (oftentimes at the expense of their family) while Generation X values W-L balance more than Baby Boomers and place much more priority and concern on personal freedom and a balance between their work and their life away from work (Burke, 1994; Chao, 2005; Joyner, 2000; Patterson, 2005). Recent empirical research has also reported that Generation X experienced more work-family conflict than Baby Boomers (Beutell & Wittig-Berman, 2008; Tausig & Fenwick, 2001), and had a higher need for balance than Baby Boomers (Sullivan, Forret, Carraher, & Mainiero, 2009). Using data collected at three points in time (1976, 1991, and 2006), Twenge and colleagues (2010) found that those from Generation X value leisure and extrinsic rewards more than Baby Boomers. Further, there is evidence that work values change over time (Smola & Sutton, 2002). Therefore, it is plausible that the definition of the ideal worker may shift, with younger generations adopting a schema that allows for the inclusion of valuing life outside of work. No known studies, however, have directly investigated generational differences in the relationship between W-L balance and perceptions of promotability. We will expand our understanding of the relationship between W-L balance and promotability by examining whether the generation to which a manager belongs (i.e., manager generation), or the generation to which a boss belongs (i.e., boss generation) are viable moderators. Since work is more central and leisure less important to Baby Boomers than Generation X (Twenge et al., 2010), those Baby Boomers who do seek balance between life and work may deliberately choose activities and roles outside of work that will help their work performance. Additionally, they may actively apply resources from their personal lives to their jobs. For example, they may spend time with a spouse or friend discussing work and asking for help in solving a work-related problem. Members of Generation X, on the other hand, may not experience facilitation to the same extent. Hypothesis 2: There will be a moderating effect of managerial generation such that the relationship between self-ratings of W-L balance and boss ratings of promotability should be stronger for managers in the Baby Boomer generation than those in Generation X.

56  S. A. Stawiski, W. A. Gentry and L. E. Baranik

Third, if balance is valued more by bosses in Generation X, they would seek out information about, recognize, and value balance more than bosses from the Baby Boomer generation. Bosses from Generation X would then be cognizant of the resultant facilitation effects in the managers they are rating than would bosses in the Baby Boomer generation. Hypothesis 3: There will be a moderating effect of boss generation such that high W-L balance is a better predictor of promotability for managers whose bosses are members of Generation X than for managers whose bosses are members of the Baby Boomer generation. Figure 3.1 illustrates the hypotheses this chapter is attempting to research.

Figure 3.1.  Illustration showing relationships and hypotheses that are tested.

Method The data presented are from an archival dataset of managers who attended an open-enrollment leadership development course at the Center for Creative Leadership (CCL) between January 1, 2009 and October 1, 2009. Managers most often attend programs at CCL through their employers. Participants and Procedure All 664 participants in this sample are between the ages of 24 and 63 and were both native to the United States and currently living in the United States (at time of the assessment). The participants had been in their current job an average of 4.35 years (SD = 4.95). Approximately 87% of them were White and nearly two thirds (63%) were male. The vast majority (90%) had earned a Bachelor’s degree or higher and nearly twothirds (65%) occupied middle management positions, with the remaining

Baby Boomers, Gen Xers, and Balance   57

managers occupying senior management positions. Just over half (53%) of participants belonged to Generation X with the remaining 47% belonging to the Baby Boomer generation. Finally, among the bosses of participants, just over a quarter (27%) belonged to Generation X and nearly three quarters (74%) belonged to the Baby Boomer generation. Prior to attending their leadership development program, participants were asked to complete several assessments. They were also asked to have others in their organization rate them using an instrument as a means of obtaining multirater assessment data. Only participants who had completed all items of the W-L balance scale and who had only one boss complete all three items of the promotability scale were used for this analysis.,. Measures Promotability and W-L balance were both measured using subscales of BENCHMARKS® (a registered trademark of the Center for Creative Leadership), an instrument developed by the Center for Creative Leadership (Lombardo & McCauley, 1994; McCauley & Lombardo, 1990; McCauley, Lombardo, & Usher, 1989). It is a comprehensive, valid, and reliable assessment tool (Carty, 2003; Spangler, 2003; Zedeck 1995). Promotability. Boss ratings of promotability were measured using three items in section three of the assessment. Bosses of participants were asked “How effectively would this person handle each of the following jobs?” on a scale from 1 to 5 with a 1 representing “Among the worst,” and 5 representing “Among the best.” The three situations are: (a) being promoted into a familiar line of business; (b) being promoted in the same function or division (moving a level up); and (c) being promoted two or more levels. We averaged the items together for a 3-item “promotability” scale, which had high reliability (α =.90). This scale has been used in previous published research to examine promotability perceptions (e.g., Gentry, Gilmore, Shuffler, & Leslie, 2012; Gentry & Sosik, 2010). Based on recommendations by Vandenberg and Lance (2000), we conducted an omnibus test of the equality of the covariance matrices for the Baby Boomer and Generation X bosses. We failed to reject the null hypothesis (χ2[9] = 13.60, p = .14), indicating that Baby Boomer and Generation X bosses have comparable conceptualizations of promotability. Work-life balance. BENCHMARKS (Center for Creative Leadership, 2004; Lyness & Judiesch, 2008) defines W-L balance as “Balances work priorities with personal life so that neither is neglected.” For this analysis, we used a manager’s self-reported ratings using the “Balance Between Personal Life and Work” scale from Section 1 of the instrument. There are four items on this scale: (a) act as if there is more to life than just having a

58  S. A. Stawiski, W. A. Gentry and L. E. Baranik

career; (b) have activities and interests outside of a career; (c) do not let job demands cause family problems; and (d) do not take career so seriously that your personal life suffers. This measure has been used in previous research on W-L balance (e.g., Lyness & Judiesch, 2008). Participants rated the extent to which they displayed each behavior, ranging from 1 (not at all) to 5 (to a very great extent). We averaged the four items together to form the scale, which had acceptable reliability (α = .79). We also conducted an omnibus test of the equality of the covariance matrices for the Baby Boomer and Generation X managers based on recommendations by Vandenberg and Lance (2000), and failed to reject the null hypothesis (χ2[14] = 10.89, p = .69), showing that Baby Boomer and Generation X managers interpreted the W-L balance items in similar ways. Generation. Participants who were 23–45 years old in 2009 were classified as Generation X. Participants who were ages 46–63 at time of completion were classified as Baby Boomers. Those of all other ages were not included in this study. While there is disagreement about exactly which birth years to use, the birth years used in our study to categorize managers and bosses into the Baby Boomer or Generation X cohorts have been used in previous research (e.g., Deal, 2007). Further, while some argue for the need to ask additional questions about influential factors in their lives to properly categorize a person into a generational cohort, birth year only is also commonly used to assign respondents to generational cohorts (e.g., Deal, 2007; Dries, Pepermans, & DeKerpel, 2008; Gentry et al., 2009; Gentry et al., 2011). Generational research often includes other generational groups, such as Silents and Generation Y or Millennials. This study did not include these groups due to the limited number of managers in these generations both in our sample as well as the U.S. workforce. While sometimes the literature differentiates between late and early Boomers, it is also common to see Baby Boomers identified as one group (e.g., Krepcio, 2007; Levant, 2008; Rodriguez, Green, & Ree, 2003). In order to keep the sample of participants split into roughly equal groups, we chose to combine all Baby Boomers into one category. Generation was coded as 0 = Baby Boomers and 1 = Generation X. Control variables. We used several control variables to provide stronger tests of our hypotheses. We controlled for participants’ self-reports of their own race (coded as 0 for White or 1 for Hispanic, African American, Asian, or Other) because previous research has revealed that Caucasians receive higher performance ratings than non-Caucasians (McKay & McDaniel, 2006; Roth, Huffcutt, & Bobko, 2003). Second, we controlled for participants’ self-reports on gender (coded 0 for male and 1 for female), because gender may bias managerial ratings (Lyness & Heilman, 2006). Furthermore, we controlled for participants’ self-reports on education (high school diploma, associate’s degree, bachelor’s degree, master’s degree, or doc-

Baby Boomers, Gen Xers, and Balance   59

torate/professional degree) and years in the current job, because these variables may affect outcomes in upward mobility studies (Ng et al., 2005; Ng & Feldman, 2010; Quiñones, Ford, & Teachout, 1995; Wayne et al., 1999). Finally, we controlled for level in the organization (coded 0 for middle management or 1 for senior management), due to the variety of managers at different levels in our study. Results The correlations and descriptive statistics are presented in Table 3.1. We use hierarchical regression analyses to test all hypotheses, with an alpha level set at .05. We first examine the direct relationship between W-L Balance and Promotability. Next, we look at the manager generation as a moderator, then the boss generation as the moderator of that relationship. Relationship Between W-L Balance and Promotability The first hypothesis tested was whether there is a positive relationship between managers’ self-rating of W-L balance and bosses’ ratings of promotability, with the hypothesis stating that there would be a significant positive relationship between the two variables. To address this question, linear regression analysis was conducted using a two block enter method with the dependent variable defined as promotability. The first block contained all five control variables. In the second block, the independent variable of W-L balance was added to assess the change in R2 as a result of adding W-L balance. With the five control variables included in step one, the results of step two indicated that managerial self-ratings of W-L balance was positively related to boss ratings of promotability (β = .11, p < .01) and accounted for a statistically significant amount of incremental variance over and above the control variables (Δ R2 = .01, p < .01), supporting Hypothesis 1 (see Table 3.2). Manager Generation as a Moderator The second hypothesis tested was whether the relationship between W-L balance and boss ratings of promotability would differ depending on manager generation. It was predicted that there would be a significant interaction between manager generation and W-L balance in predicting promotability. To test this hypothesis, hierarchical regression analysis was

60  S. A. Stawiski, W. A. Gentry and L. E. Baranik Table 3.1. Means, Standard Deviations, and Intercorrelations of Study Variables 1

2

Mean

SD

1. Gender

  .38

  .48

–

2. Levelb

  .36

  .48

 .01

a

3

4

5

6

7

–

3. Job Tenure

4.35

4.95

 .01

  .07

–

4. Educationc

2.46

  .98

 .00

 .01

–.01

–

5. Race

  .13

  .34

 .09

–.02

 .02

 .01

–

6. Generatione

  .53

  .50

  .03

–.02

–.18

–.09

.02

7. W-L Balance

3.82

  .72

–.01

–.05

 .06

  .07

.05

–.07

–

8. Promotability

3.70

 .92

 .05

  .03

–.02

  .04

.00

 .11

.10

d

8

–

–

0 = Male, 1 = Female. 0 = Middle, 1 = Senior Management. 0 = High School, 1 = Associates, 2 = Bachelors, 3 = Masters, 4 = PhD/Professional. d 0=White, 1 = NonWhite. e 0 = Boomer, 1 = Generation X. a

b

C

|r| > .086, p < .05. |r| > .104, p < .01.

Table 3.2. Multiple Regression Analysis Testing Hypotheses 1, 2, and 3 Step 1 β

Step 2 β (H1)

Step 3 β (H2)

Step 4 β (H2)

Step 3 β (H3)

Step 4 β (H3)

Gendera

  .05

   .05

    .04

   .04

  .05

  .05

b

Level

   .03

    .03

    .04

   .04

   .03

   .03

Job Tenure

  –.02

   –.03

   –.00

  –.00

  –.03

  –.03

Education

   .04

    .03

   .04

  .04

  .02

  .02

Raced

  .00

   –.01

   –.01

  –.01

  –.01

  –.01

  .11**

   .08

.11**

   .08

–.08*

  –.08*

Variable

c

Work-Life Balance (H1)

  .11**

  .14**

Manager Generatione

.14**    .03

WLB x Manager Generation (H2) Boss Generatione WLB x Boss Generation (H3)

  –.03   .00

   .01

   .02

  .00

  .01

  .61

7.31**

9.96**

   .07

  4.42

  .07

5,658

  1,657

 1,656

1,656

1,656

1,656

R2

  .01

   .02

    .03

   .03

  .02

  .02

Adjusted R2

  –.00

   .01

   .02

  .02

  .01

  .01

Δ R2 ΔF df

  .01

(Table continues on next page)

Baby Boomers, Gen Xers, and Balance   61 Table 3.1.  (Continued) Variable Overall F df

Step 2 β (H1)

Step 3 β (H2)

Step 4 β (H2)

.61

1.73

2.92**

5,663

6,663

7,663

Step 1 β

Step 3 β (H3)

Step 4 β (H3)

2.57**

2.08

1.83

8,663

7,663

8,663

0 = Male, 1 = Female. b 0= Middle, 1 = Senior Management. c 0=High School, 1=Associates, 2 = Bachelors, 3 = Masters, 4 = PhD/Professional d 0=White, 1 = Non-White. e 0 = Boomer, 1 = Generation X. H1 = Test for Hypothesis 1. H2 = Test for Hypothesis 2. H3 = Test for Hypothesis 3. a

*p < .05. **p < .01.

used, repeating step one and two as previously described. Manager generation was entered in step three. Interestingly, manager generation was significantly and positively related to promotability (β = .14, Δ R2 = .02, p