Every year, Fortune magazine conducts an annual survey of the “100 Best Companies to Work For.” Firms strive to be named to this list because they receive twice as many applications as firms that are not on the list, and they enjoy employee turnover levels that are less than half those of their competitors.[1] In short, people want to work at places where they are treated well. If satisfied employees really do fuel corporate profits, one would expect “100 Best” employers to outperform broad indexes of firms that are publicly traded—and they do, as shown in Figure 6-1.
Figure 6-1 shows that investors who bought, say, 100 shares of each of the publicly traded firms on the Fortune “100 Best” list at the beginning of 1998, and held them for four years, until December 31, 2001, would almost have doubled the returns of the Standard & Poor’s 500 and the Russell 3000 indexes over the same time period. Years 1998 and 1999 were boom times for the stock market, whereas years 2000 and 2001 were busts.[2]
In another study, researchers compared the organizational performance of Fortune’s “100 Best Companies to Work For” with two sets of other companies, a matched group and the broad market of publicly traded firms, over a six-year period.[3] They found that organization-level employee attitudes of the “100-Best” firms were both highly positive and stable over time. They also found that the Return on Assets and market-to-book value of equity of publicly traded companies included on the “100-Best” list were generally better than those of a matched comparison group. That finding established an important link between employee attitudes and organization-level financial performance.
As for stock returns, the same study found that the “100 Best” companies outperformed the broad market when considering cumulative (longer-term) returns (82 percent versus 37 percent from 1998–2000), although not consistently for annual returns. The authors concluded: “At the very least, our study finds no evidence that positive employee relations comes at the expense of financial performance. Firms can have both.”[4] Similar results have been reported in the accounting and finance literature.[5]
Of course, finding a correlation between financial performance and employee attitudes does not mean that enhancing employee attitudes caused the superior financial performance of the organizations in the study. Chapter 2, “Analytical Foundations of HR Measurement,” showed that correlation is not the same as causation. For example, people like to work for companies that are financially successful. It is just as plausible that when companies become financially successful, their employees display positive attitudes. For an investor, the link between employee attitudes and financial performance of the firm is a valuable signal, and the direction of causality is irrelevant. From a manager’s perspective, however, “what causes what” is extremely important because it affects decisions about talent. Unfortunately, research has not been able to point the causal arrow unequivocally in one direction or the other. It is likely that it goes both ways.
With financial results like these, it is perhaps not very surprising that measuring attitudes such as satisfaction, engagement, and commitment has become big business. There are many consulting products and internal organizational processes to define and track employee attitudes and to relate those attitudes to a variety of operational and financial results. Yet, the working models of most business leaders are often no more sophisticated than a belief that “happy employees are productive employees” or that “becoming a great place to work will create superior financial results.” Of course, a valuable logic and measurement system would do better, by articulating the connections between attitudes and organizational outcomes and directing measures to the areas that best articulate those connections.
This chapter presents frameworks that HR and business leaders can use to collect and interpret relevant measures to make better decisions about such programs, even if the decision is not to invest in them. Such systems can certainly identify where attitude-assessment or employee-engagement programs are valuable, but our purpose is not simply to provide tools to sell such investments, but to enhance decisions about employee attitudes.
Attitudes are internal states that are focused on particular aspects of or objects in the environment. They include three elements: cognition, the knowledge an individual has about the focal object or employment aspect; the emotion an individual feels toward the object or aspect; and an action tendency, a readiness to respond in a predetermined manner to the object or aspect.
One reason that it is important to have a clear and logical framework for under standing how attitudes connect to organizational success is that attitudes are often multidimensional. Job satisfaction is a multidimensional attitude. In its 2006 survey of employees from small, medium, and large companies in a wide range of industries, the Society for Human Resource Management found that the top five drivers of job satisfaction were compensation, benefits, job security, work/life balance, and feeling safe in the workplace.[6]
Likewise, organizational commitment is a bond or linking of an individual to the organization that makes it difficult to leave.[7] It is the emotional engagement that people feel toward a firm.[8] Commitment can be to the job or the organization and can take the form of a commitment to contribute, to stay, or both.
Commitment is closely related to the concept of employee engagement. Engagement is a positive, fulfilling, work-related state of mind that is characterized by vigor, dedication, and absorption.[9] Vigor refers to high levels of energy and mental resilience while working, the willingness to invest effort in one’s work, and persistence even in the face of difficulties. Dedication is characterized by a sense of significance, enthusiasm, inspiration, pride, and challenge at work. Absorption consists of being fully concentrated, happy, and deeply engrossed on one’s work whereby time passes quickly, and one has difficulty detaching oneself from work.[10] Engagement fuels discretionary efforts and concern for quality. It is what prompts employees to identify with the success of their companies, to recommend them to others as good places to work, and to follow through to make sure problems get identified and solved.
Employee satisfaction, commitment, and engagement affect organizational performance through employee behaviors. Employees with lower attitudes may be absent, late for work, or quit more often, or place less emphasis on customer satisfaction than those with more positive attitudes. Evidence indicates that this is often the case.[11] These ideas are shown graphically in Figure 6-2.
Figure 6-2 shows that enhancing employee attitudes can affect a firm’s financial performance. Changing employee attitudes can have direct effects on employee turnover and absence, with the associated effects on the costs of absence and turnover (see Chapters 3 and 4). Having a reputation as a satisfying place to work may enhance the ability to recruit more or higher-quality applicants (see Chapters 8 and 10). In addition, there is some evidence that employee attitudes directly affect employee performance, in particular, the tendency for employees to do tasks that are beyond their formal job description (often called “citizenship behaviors”), and to convey positive emotions to customers. These latter connections show up in productivity or service costs and in sales and revenue levels (see Chapter 9, “The Economic Value of Job Performance”).
It is also important to note that the relationships shown in Figure 6-2 vary depending on the nature of the talent pool and the work. For jobs whose contributions depend significantly on customer interactions and conveying positive emotions, the effects of attitudes on service performance may be paramount. For jobs that seldom encounter a customer, but where teamwork and cooperation are key, citizenship behaviors may be the vital connection. Or, for jobs where the costs of absence and turnover are very significant, the effects of attitudes on these behaviors may be the vital measurement question. Just as with all measurement, employee attitudes have different effects depending on what elements of employee behaviors are pivotal.
Of course, employee attitudes also relate to important outcomes that are less tangible or measurable by traditional financial systems, including individual growth and well-being, organizational adaptability, and goodwill. Many organizations measure employee attitudes not only because they provide leading indicators of tangible financial performance, but because they are a signal about more subtle nonfinancial results. In other words, they see improving employee attitudes as a worthy goal in and of itself. We recognize the nonfinancial outcomes of employee attitudes, and their independent value as an organizational goal, but we focus in this chapter on the connections between financial outcomes and employee attitudes.
A recent study suggests that employee engagement promotes a positive service climate (shared perceptions of practices and behaviors that are expected and rewarded by the organization with regard to customer service)[12] and customer loyalty.[13] The researchers selected a sample of 3 employees and 10 customers from each of 120 hotel and restaurant work units. They demonstrated that organizational resources (for example, training, supervisor support, performance feedback) and employee engagement predict service climate, which, in turn, predicts employee performance, and then customer loyalty.
Loyal customers, in turn, tend to do two things:
Recommend your organization to others
Generate repeat business
Both of these have been shown to lead to changes in revenue growth, lagged about one fiscal quarter.[14] Figure 6-3 illustrates graphically these logical connections.
Note in the exhibit that the relationship between employee engagement and organizational resources is multiplicative, not additive. That is, it is represented as employee engagement X organizational resources, not plus organizational resources, because if either of those is low or, in theory, zero, the other element cannot compensate enough to affect service climate and the remaining elements of the model in a positive manner.
At a broader level, the Corporate Leadership Council found that every 10 percent improvement in commitment can increase an employee’s level of discretionary effort by 6 percent and performance by 2 percent, and that highly committed employees perform at a 20 percent higher level than noncommitted employees. Another study by Hewitt Associates reported that double-digit growth companies have 39 percent more highly engaged employees and 45 percent fewer highly disengaged employees than single-digit growth companies.[15] These studies provide very useful examples that connect employee attitude measures to intermediate processes, and ultimately to customer behaviors and financial results.
Still, these results do not allow us to say “what causes what.” Although employee engagement may cause double-digit financial growth in companies, it is equally plausible that double-digit-growth companies are fun, exciting places to work, and, as a result, employees are highly engaged. Academic researchers, consulting firms, and the in-house research departments of large companies conduct studies like these regularly, and their findings are often extracted in media reports. To be better consumers of measures and correlations between attitudes and financial performance, it is important that readers be aware of key qualifications and limitations of study findings. The next sections of the chapter present a summary of common ways to measure attitudes, and then analytical principles that help ensure that the conclusions from the data are valid.
Measures of employee attitudes are fairly well developed.[16] Job satisfaction is a multidimensional attitude. One can assess how satisfied one is with one’s job as a whole (one’s global feeling about the job) by asking, for example, “Overall, how much enjoyment do you find in your work?” Alternatively, one might assess and sum up satisfaction with facets of the job, such as satisfaction with one’s pay, one’s colleagues, the nature of the work, and supervision. If the purpose is to understand the overall effect of jobs, global ratings are the best choice. If the assessor wants to know how to improve job satisfaction in a particular situation, the facet approach is more diagnostic.[17]
Organizational commitment is also a multidimensional attitude with three distinct components: Affective commitment refers to an employee’s emotional attachment to an organization and a desire to stay. Continuance commitment refers to the extent to which an employee believes that leaving would be costly. Normative commitment refers to an employee’s feelings that staying with the current organization is the right thing to do.[18] There are well-developed measures of each of these components of commitment. For example, here is an item from the Organizational Commitment Questionnaire, a measure of affective commitment: “It would take a lot to get me to leave this organization.”[19]
Employee engagement is closely related to job satisfaction and commitment, for it is a positive, fulfilling, work-related state of mind characterized by vigor, dedication, and absorption. Earlier we referred to a study by the Gallup Organization in which it identified 12 worker beliefs (measures of employee satisfaction-engagement) that play a large role in triggering a profitable, productive workplace. Here are the 12 statements of worker beliefs:[20]
I know what is expected of me at work.
I have the materials and equipment I need to do my work right.
At work, I have the opportunity to do what I do best every day.
In the last seven days, I have received recognition or praise for doing good work.
My supervisor, or someone at work, seems to care about me as a person.
There is someone at work who encourages my development.
In the last six months, someone at work has talked to me about my progress.
At work, my opinions seem to count.
The mission/purpose of my company makes me feel my job is important.
My fellow employees are committed to doing quality work.
I have a best friend at work.
This last year I have had opportunities at work to learn and grow.
Before adopting any particular measure, it is important to consider the logical relationships you wish to examine. The descriptions in this section can help you make better choices. Broad, global measures such as the Gallup items may be appropriate for examining general employee attitudes, but it may often be appropriate to choose measures that focus in on particular work facets, or that more clearly distinguish the elements of satisfaction and commitment. Too often, organizations adopt the most popular or well-known measure, without realizing that decades of research has produced many alternatives.
Unfortunately, there is no consensus in the research literature about what the most appropriate time lag might be for collecting relevant information either on the same variable measured at two different times (for example, attitudes of employees about their supervisors) or when attempting to assess the relationship between two or more variables (for example, aggregated employee attitudes and organizational performance). Indeed, organizational performance may even drop a bit immediately following the implementation of a change in management practices, as the organization adapts.[21] At the very least, such relationships must be relatively stable. Stability is important because if a variable is not stable over time, it cannot be predicted reliably by another variable. Hence, if lagged analyses are the major focus of interest, the stability of those lags is important.
As an example of how different time lags can produce different results, consider the results of a recent longitudinal study.[22] The researchers analyzed employee attitude data from 35 companies over 8 years at the organizational level of analysis relative to financial (return on assets) and market performance (earnings per share) using a variety of lagged analyses. They found consistent and significant positive relationships over various time lags between aggregated attitudes concerned with satisfaction with security, satisfaction with pay, and overall job satisfaction (OJS) with financial and market performance.
The same researchers also examined one-year, two-year, three-year, and four-year lags. They found remarkable stability in employee attitudinal data at the organizational level of analysis. The one-year lags ranged from a low of .66 (satisfaction with work group) to a high of .89 (satisfaction with security). Even the four-year lags revealed substantial stability, ranging from a low of .40 (satisfaction with work facilitation) to a high of .78 (satisfaction with empowerment).
With respect to financial indicators, return on investment (ROI), return on equity (ROE), return on assets (ROA), and earnings per share (EPS) were significantly correlated across time. Median correlations were .57 (ROI-ROE), .73 (ROE-ROA), .94 (ROI-ROA), .38 (ROI-EPS), .48 (ROE-EPS), and .33 (ROA-EPS). However, they were differentially stable over time, with ROI being the least stable (median one-year lag r = .47) and ROA being the most stable (median one-year lag r = .74). Based on these results, the researchers used ROA as the most stable indicator of organizational financial performance. They used EPS as an indicator of market performance, although it was not as stable as ROA (median one-year lag r = .49).
Thus, both attitude measures and organizational performance measures may vary in their stability over different time spans. If possible, it is wise to collect data on attitudes and organizational outcomes (behavioral or financial) at multiple time periods and choose the interval that yields the most stable and representative relationships. It is also useful to consider the logical connections and strategic-decision factors in choosing time lags. In organizations with stable and long-term employment relationships, the relationship between attitudes and financial outcomes spanning several years may be quite relevant and valuable, because such organizations would reap the rewards of attitude change over many years. In organizations where employee tenure or time in a job is less, the relevant strategic issue may be the effect of attitudes on outcomes that occur much sooner.
Studies of the relationship between employee attitudes and customer satisfaction, or turnover, using cross-lagged correlational analyses (that is, correlations between these variables computed at different times) have been inconclusive regarding the direction of causality, as noted previously.[23] Still, such studies provide tantalizing evidence that the collective employee attitudes of the organization or business unit may be correlated with overall performance of that organizational or business unit, even if for particular individuals, the attitudes are only weakly correlated with individual-level performance. For example, we noted that the Gallup Organization identified 12 worker beliefs (measures of employee satisfaction-engagement) that relate most closely to workplace profits and productivity.[24] This multiyear study was based on an analysis of data from more than 100,000 employees in 12 industries.
A subsequent meta-analysis (see Chapter 2) included data from almost 8,000 business units in 36 companies.[25] The results showed a consistent, reliable relationship between the level of the 12 beliefs among employees and unit-level outcomes such as profits, productivity, employee retention, and customer loyalty. At the level of the work group, groups that demonstrated positive attitudes were 50 percent more likely to achieve above-average customer loyalty and 44 percent more likely to have above-average profitability.
At the level of the business unit (division, plant, and so on), those in the top quartile on employee engagement had, on average, from $80,000 to $120,000 higher monthly revenues or sales than those in the bottom quartile. An $80,000 monthly difference translates into almost one million ($960,000) per year. Interestingly, researchers found significant variances among work groups or operating units within the same company, suggesting that even in companies that do well overall, there may be significant opportunities to improve individual business units.
Thus, understanding how the connections between attitudes and organizational outcomes vary depending on the unit of analysis is important. Again, one implication is that organizations should not presume that the whole story is in the relationships between individual employee attitudes and their behaviors. It appears that even when relationships at the individual level are weak, there may still be strong relationships when the aggregated attitudes of employees are related to aggregate performance at the work group or business-unit level. Choosing the appropriate level of analysis is a matter both of the power of the statistical test and of the strategic question at hand. In most organizations, the fundamental strategic issues involve business-unit or work-group performance, so results suggesting that relationships may be more powerful or stable at this level of analysis are encouraging.
Based on results from the same meta-analysis, the authors concluded that the causal order runs from employee attitudes to organizational performance, although they recognized that multidirectional (reciprocal) relationships might also be expected. In the earlier section on “time lags,” we cited a study that included longitudinal data from 35 companies on employee attitudes and longitudinal data from the same companies on organizational financial and market performance (8 years of data).[26] Armed with both of these sets of data, the researchers were able to explore questions involving causal ordering and time lags among both of these sets of variables.
Their analyses revealed statistically significant and stable relationships across various time lags for three of seven attitude scales. Overall job satisfaction and satisfaction with security were predicted by ROA and EPS more strongly than the reverse, although some of the reverse relationships were also significant. Satisfaction with pay exhibited a more reciprocal relationship with ROA and EPS. Based on these results, it is clear that relationships among employee attitudinal variables and organizational performance are complex, and they may be multidirectional or reciprocal in nature. Researchers can therefore be misled if they simply assume, on the basis of cross-sectional data, that employee attitudes predict organizational financial or market performance, but not vice versa, and if they do not allow for the possibility of reciprocal relationships. To avoid this trap, researchers must collect employee attitude data and organizational performance data longitudinally, at multiple points in time. Doing so allows researchers to test forward and backward lags and to draw meaningful inferences about causal priorities.
The remainder of this chapter shows how financial and attitudinal measures can be synthesized to produce an estimate in dollars of the costs and benefits of human resource management programs designed to improve employee attitudes. We begin with the behavior-costing approach to attitude valuation, and then illustrate its application in two firms: Sears and SYSCO.
The behavior-costing approach to employee attitude valuation is based on the assumption that measures of attitudes are indicators of subsequent employee behaviors.[27] These behaviors can be assessed using cost-accounting procedures, and they have economic implications for organizations. The conceptual framework underlying behavior costing stems from psychological theories that emphasize that employees’ behavior at work is the result of choices about whether to appear at the workplace (“participation-membership”),[28] and of choices about how to behave at work (“work strategies”).[29] This framework assumes that employees will be more likely to come to work than be absent or quit if they are satisfied with their jobs. In addition, they are likely to exert more effort and to choose more effective job performance strategies if they expect to be rewarded, either intrinsically or extrinsically, for their efforts.[30]
These ideas suggest that attitudinal indexes of employee satisfaction and engagement should be the best predictors of participation-membership, because they reflect perceptions of the rewards associated with being at work, and that attitudinal indexes of employee motivation should predict job performance, because they reflect some of the performance outcomes contingent on doing a good job: competence, achievement, and self-realization.
Sears, Roebuck & Company provides a good example showing how all the elements we’ve discussed so far come together. In 1998, Sears executives realized that a fertile ground for improving operations was in better measuring and understanding the connection between employee attitudes and behaviors in stores, and associated customer attitudes and behaviors that led to financial performance. At the time, Sears store managers had little idea about this connection, and so they often operated stores to minimize labor costs or with little attention to employee attitudes. Store managers often believed that the key to store success was mostly in the more tangible elements of merchandise.[31] Although the top leadership at Sears believed there was a connection between retail store employees’ attitudes and financial results, they had no hard data to demonstrate it, and in a pragmatic industry like retail, the decisions of store managers and executives often require compelling evidence.
The Sears model didn’t just examine whether employee attitudes correlated with financial outcomes. Instead, Sears created a model with a logical set of connections between employee attitudes and store financial performance, including customer behavior as a critical intervening variable. Sears created a logic based on the proposed connections between employee attitudes, behaviors, customer responses, and financial outcomes. Sears also used analytics and measures to test and refine the logic, producing an integrated and data-based depiction of the strength of the relationships. Finally, Sears then created an effective process to embed the results in the organization’s decision systems, by carefully communicating the results to business leaders and employees, holding store managers accountable for the attitude measures that had proven to be related to store financial outcomes, and continuing to measure and refine the model so that even deeper insights emerged regarding particular merchandise categories and customer segments. Many retailers might try to copy the Sears measures, even the modeling techniques, but they might fail to realize the benefit from the model, because the logic, analytics, and measures of the system are not in themselves enough to make it work. It is their integration, and their connection to the fundamental operating processes of the Sears organization, that creates the organization change. We highlight a few of the key elements here.
Careful analysis of the output of company task forces and focus groups of customers led Sears managers to a business model that connected employees, customers, and investors. The managers became convinced that for Sears to succeed financially, it had to be a compelling place both to work and to shop (that is Work × Shop, not Work + Shop). The right merchandise at the right prices would not enable the company to succeed financially if its employees were poorly motivated. These ideas comprised the three “compellings”: Sears should be a compelling place to work, to shop, and to invest. The formula, Work × Shop = Invest, comprised leading, not lagging, indicators. As the authors noted: “Financial results are a rearview mirror; they tell you how you did in the last quarter, and not how you will do in the next...few if any companies have ever come up with dependable predictive metrics, and that’s what we were after.”[32]
The next step was to convert the three compellings into a set of measures and a measurement model. The objective was to devise a kind of balanced scorecard for the company—the Sears Total Performance Indicators, or TPI—that would show pathways of actual causation all the way from employee attitudes to profits. The nonfinancial measures (for example, measures of employee attitudes) had to be every bit as rigorous and auditable as the financial ones.
Over two three-month periods (two quarters), Sears managers collected survey data from employees and customers and financial data from 800 Sears stores. A team of consulting statisticians then factor-analyzed the data into meaningful clusters, and used causal pathway modeling to assess cause-effect relationships. Based on initial results, Sears adjusted the model and continued to collect data for a new iteration at the end of the next quarter.
How did Sears benefit from the model? It could see how employee attitudes drove not just customer service, but also employee turnover and the likelihood that employees would recommend Sears and its merchandise to friends, family, and customers. It discovered that an employee’s ability to see the connection between his or her work and the company’s strategic objectives was a driver of positive behavior. It also found that asking customers whether Sears is a “fun place to shop” revealed more than a long list of more specific questions would. It began to see exactly how a change in training or business literacy affected revenues.
Recall from the earlier section that there are many attitude measures, running the gamut from general measures of overall attitudes, to measures that include very specific items and scales for individual work attributes such as pay, supervision, and so on. Sears used a 70-item questionnaire to assess employees’ attitudes; it found that a mere 10 of those questions captured the predictive relationship between employee attitudes, behavior toward the customer, and customer satisfaction. Six of these predicted an employee’s attitude about his or her job:
I like the kind of work I do.
My work gives me a sense of accomplishment.
I am proud to say I work at Sears.
How does the amount of work you are expected to do influence your overall attitude about your job?
How do your physical working conditions influence your overall attitude about your job?
How does the way you are treated by those who supervise you influence your overall attitude about your job?
Four items predicted an employee’s attitude about the company:
I feel good about the future of the company.
Sears is making the changes necessary to compete effectively.
I understand our business strategy.
Do you see a connection between the work you do and the company’s strategic objectives?
In summary, as of 1998, Sears did the following things. It produced a model, revised it three times, and created a TPI for the company as a whole. It conducted interviews and collected data continually, assembled its information quarterly, and recalculated the impacts on its model annually to stay abreast of the changing economy, changing demographics, and changing competitive circumstances. The revised model (see Figure 6-4) became a tool to help Sears managers run the company in 1998.
For example, the model showed that a five-point improvement in employee attitudes will drive a 1.3-point improvement in customer satisfaction in the next quarter, which in turn will drive a 0.5 percent improvement in revenue growth. Thus, Sears could predict that if one store had a five-point higher level of employee attitudes than other comparable stores, and if revenue growth in the district were 5 percent, revenue growth at this particular store would be 5.5 percent.
The employee-customer-profit chain applied at Sears illuminated, for the first time, the “black box” between employee attitudes and financial performance to show the linkages between attitudes, employee behavior, customer impression, and financial performance. In fact, because Sears had information about customer behaviors and attitudes, it could actually trace the connections between employee attitudes and specific customer behaviors, such as repeat purchases, or customer attitudes, such as their stated likelihood that they would tell a friend about Sears.
With the data in place, Sears leaders then began to hold store managers accountable for the elements of the model. They were rated and tracked with regard to the employee attitude measures, and the system tracked the relationship between those attitudes and customer behaviors and store performance. As the system evolved, Sears created web portals that were organized according to the three compellings, and that allowed store managers to highlight and click on particular connections for further study. Eventually, Sears invited store managers and others to post their best practices to the website, and these were also integrated with the logical model of the connections between the three “compellings.” Thus, not only could Sears store managers now track the measures, they could undertake the analysis using the proven logic of the model to evaluate and predict their own store’s performance. Moreover, if they saw an area where improvement seemed to have potential to enhance store performance significantly, they could click and see the best practices of other stores that had enhanced those attitudes or behaviors.[33]
The Sears model applied specifically to retailing, where customers and retail associates interact very closely. Next, we describe an organization that used a similar logical model in an industry that was far different from multilocation retail—the marketing and delivery of food-service products.
At about the same time as Sears was developing its employee-customer-profit chain, SYSCO, the largest food marketer and distributor in North America, modified a service-profit-chain model developed earlier.[34] That new model included a more descriptive explanation of the process of creating customer value, with a broader range than the service sector per se. Figure 6-5 shows SYSCO’s model.
As Figure 6.5 shows, a satisfied work force enables a company to pursue excellence in innovation and execution. The logical proposition was, like Sears, that higher employee satisfaction drove innovation and execution, which, in turn, enhanced customer satisfaction, customer purchasing behavior, and eventually, long-term profitability and growth. Certainly management needs to put in place systems, people, technology, and processes that will initiate and sustain innovation and execution—the principal components of an effective value-profit chain. Technology and processes are easily copied by competitors, but a highly skilled, committed, and fully engaged work force is difficult to imitate.
A basic management model—the set of practices that describe how a company seeks to engage the hearts and minds of employees with its employer brand—has been termed the 5-STAR management model.[35] That model is all about taking care of people—extending the same respect to employees as it does to customers. The framework is general enough to apply to any type of company structure or business model, and it allows businesses wide discretion in actual implementation. As Figure 6-5 shows, the five principles of the STAR model (“Management Practices” in Figure 6-5) are as follows:
Ensuring that leaders offer direction and support
Strengthening front-line supervisors
Rewarding performance
Addressing employees’ quality of life
Including employees by engaging them and leveraging diversity
Although specific leadership and management practices that address each of the 5-STAR principles are beyond the scope of this chapter, we do want to emphasize that employee attitudes are integral components of the STAR model because as a set, those attitudes reflect employee satisfaction, a key component of the value-profit chain. At a broader level, Figure 6-5 shows how SYSCO creates value from its human capital. It shows clearly the intermediate linkages between employee attitudes and financial performance. Indeed, the logic of the model is so compelling that it is taught to every manager and employee from his or her first day on the job.
To measure the attitudes of its employees, SYSCO developed a work climate–employee engagement survey built around each of the 5-STAR principles. All members of each SYSCO operating company participate in a comprehensive annual self-assessment and impromptu and informal assessments on an as-needed basis.[36] The total survey comprises 61 items, but, like Sears, SYSCO found that just 14 of them differentiated the top-performing 25 percent of its 147 operating companies from the bottom 25 percent. Table 6-1 shows these.
Table 6-1. The 14 Most “Impactful” Items from SYSCO’s Work Climate Survey
5-STAR Principle | Work Climate Survey Item |
|---|---|
Leadership Support | I know what is expected of me at work. |
Upper management spends time talking with employees about our business direction. | |
Front-Line Supervisor | My supervisor treats me with dignity and respect. |
My supervisor and I review my top goals and discuss how they contribute to the company’s success. | |
I have received constructive feedback on my performance within the last six months. | |
My supervisor removes obstacles so I can do my job better. | |
Quality of Life | I trust what the company tells me. |
Different departments of our company work together to get the job done. | |
Rewards | My pay is the same or better than other companies in our market. |
Doing my job well leads to monetary rewards. | |
Decisions made about promotions or job changes within this organization are fair. | |
Engagement/Diversity | I am willing to work harder to make this company succeed. |
I am proud to work for SYSCO. |
SYSCO has a decentralized organizational structure comprised of 147 autonomous operating companies. It employs an organizationwide rewards system to encourage managers of the autonomous operating companies to share information with each other and to transfer best practices within the organization. Like Sears, SYSCO built a “best business practices” web portal on its intranet to provide a platform for organizationwide improvement. The web architecture offered a framework for managers to do two things: share information on their own operating company’s successful practices, and learn from the best practices of other SYSCO operating companies.
SYSCO also assesses the performance of each operating company in terms of balanced-scorecard metrics in four areas: financial, operational, human capital, and customer performance. Scores on the work climate–employee engagement survey comprise one element of the human capital metrics, along with measures of productivity (employees per 100,000 cases shipped) and employee retention (among marketing associates, drivers, and night warehouse employees). Managers of operating companies can use the “ best business practices” portal to identify and learn from operating companies in the top quartile of performance on one or more metrics in the balanced scorecard.
As an example, consider the area of safety (specifically, the costs of workers’ compensation for work-related injuries). By leveraging best practices and shared, reciprocal visits among managers of its operating companies, SYSCO reduced the performance gap in workers’ compensation costs between the top and bottom 25 percent of operating companies, and it increased companywide safety results by nearly 50 percent over a five-year period. As a result, SYSCO cut by half its overall cost of workers’ compensation as a percentage of sales. That represented a significant improvement in performance and an annual cost savings to the company of $36 million.[37] Note that operating managers worked with the set of key metrics—operations, financial, customers, and human capital—to leverage best practices to reduce the costs of workers’ compensation. Work-climate scores comprise only one element of human capital metrics, which, in turn, comprise only one component of the balanced scorecard. One cannot conclude that improvements in work-climate scores alone contributed to reductions in the costs of workers’ compensation.
SYSCO’s in-house research also supports other links in the value-profit chain. Table 6-2 shows that SYSCO operating companies with the most satisfied employees consistently receive the highest scores from their customers and have higher retention of marketing associates and drivers.
Table 6-2. Satisfied Employees Deliver Better Results, FY 2005, SYSCO Companies
High | Low | ||||
|---|---|---|---|---|---|
Associate Satisfaction | 4.00 – 5.00 | 3.90 – 3.99 | 3.75 – 3.89 | 3.55 – 3.74 | < 3.55 |
Customer Loyalty Score | 4.55 | 4.40 | 4.25 | 4.15 | 4.05 |
Retention, Marketing Associates | 88% | 85% | 81% | 75% | 76% |
Retention, Drivers | 87% | 81% | 81% | 75% | 76% |
The data in Table 6-2 are tantalizing, but there are some important questions left unanswered. Clearly, retention is higher in operating companies with better associate satisfaction. Although results for customer loyalty and employee retention are in the right direction (high/low customer loyalty systematically tracks with high/low employee retention), it is not clear that those results are statistically significant, and thus whether they generalize beyond the particular situation. Furthermore, causes and effects are not clear. Is it the case that making employees more satisfied causes customers to be more loyal? Or is it more rewarding to work in operating companies with loyal customers, and, as a result, that employees who work there tend to be more satisfied? The data in Table 6-2 simply do not provide answers to those important questions. This is not meant to deny the tangible and important contributions of the SYSCO analysis. It does, however, suggest that continued improvements in logic, analytics, measures, and process are vital, even in advanced systems like SYSCO’s.
The data in Table 6-2 do not include cost savings associated with improvements in the retention of marketing associates and drivers, but those cost savings were significant. We can use those retention numbers, along with the costing principles discussed in Chapter 4, “The High Cost of Employee Separations,” to provide an example of the economic effect of attitudes.
In 2000, retention rates for these groups were 75 percent and 65 percent, respectively. By 2005, those retention rates improved to 88 percent and 87 percent, respectively. SYSCO then estimated the replacement and training costs of these three groups of employees as $50,000 per marketing associate and $35,000 per driver. Assuming 100 employees per business unit, from 2000 to 2005, each business unit saved (in terms of costs that were not incurred) $650,000 among marketing associates and $770,000 among drivers, for a total savings of $1.42 million. Corporatewide savings in retention over all categories of employees from 2000 to 2005, assuming 10,000 employees, totaled $156.5 million.[38] Such savings contributed to the firm’s long-term profitability and growth.
Today, top executives at SYSCO meet on a quarterly basis to review the metrics. Their purpose is to see whether those numbers are, in fact, consistent with the operating expenses and the pretax earnings of each operating company, as well as with those of the corporation as a whole. What led SYSCO executives to pay attention to the human capital indices? HR researchers found a high multiple correlation (R2 = .46) between work-climate scores, productivity, retention, and pretax earnings. This means that 46% of the variation in pretax earnings was associated with variation in the combination of these three employee-related variables.
In short, SYSCO leaders began to pay attention when they realized that the human capital indices served as indicators of financial results that the executives could see in their own operating companies. The relationship is lagged about six months, and although exact cause-effect relations have not been determined, the business model that the company uses assumes that employee satisfaction drives customer satisfaction, which drives long-term profitability and growth. In short, SYSCO not only has been able to determine what practices and processes are helping to drive the human capital indices, but also how those, in fact, influence the financial metrics over time. This led SYSCO to develop the business model shown earlier in Figure 6-5.
A number of challenges remain in relating attitudes to costs (see Table 6-3). Note that although the logic of each of the attitude-cost models shown in Table 6-3 is similar, the major differences lie in how much of the process chain each approach actually measures.
Table 6-3. Assumptions, Advantages, and Challenges of Attitude-Cost Models
Model | Assumptions | Advantages | Challenges | ||
|---|---|---|---|---|---|
Behavior Costing | Attitudinal measures are indicators of subsequent employee behaviors—participation-membership. | a. | Relates attitudes to future costs. | a. | Difficult to validate cost savings because analyses are based on correlational data. |
b. | Yields the financial measure closely related to employee attitudes. | b. | Best time lag for determining attitude-behavior relationships is unknown. | ||
c. | Analysis is explicitly at individual, not at work group or organizational levels. | c. | Instability in attitude-behavior relationships yields inaccurate financial changes. | ||
Employee-Customer-Profit Chain | Employee attitudes influence employee behavior, which affects customer satisfaction, which drives financial results. | a. | Identifies cause-effect relations. | a. | Data collection and annual updates may be time - consuming and expensive. |
b. | Is predictive in nature. | b. | Managers must align their actions with the model. | ||
c. | Enables answers to “what if” questions. | c. | Managers must deploy the model to employees at every level. | ||
d. | Guides management actions. | ||||
Value-Profit Chain | Effective management actions drive employee satisfaction, which enables excellence in innovation and execution, which leads to customer satisfaction, which drives profitability and growth. | a. | More complete specification of intermediate linkages between attitudes and outcomes. | a. | Requires regular data collection, analysis, and reporting to leverage best practices. |
b. | Analysis is explicitly at the work group or organizational levels. | b. | “Best” time lag is unknown. | ||
c. | More generally applicable than other models. | c. | Longitudinal data required to test causal ordering of links in the model. | ||
Certainly refinements are needed in the methods described here, but the potential of cost-benefit comparisons of attitude-behavior relationships is enormous. If organizations can develop compelling, logical frameworks that relate employee attitudes and employee engagement to financial outcomes, and if they can use sound analytics and measures to draw meaningful conclusions from their data, they can engage in a more rational decision-making process regarding where they should and should not make investments. Most important, they will be able to identify critical decision pivot-points where this kind of information will make the biggest difference.
1. | Your boss has asked you for evidence that shows the link between employee attitudes such as job satisfaction, commitment, and engagement, and both individual and organizational outcomes. In other words, convince him that attitudes matter. What sort of evidence might you present? |
2. | As a special project, top management wants to use behavior-costing methodology to estimate the change in revenues to be expected at its retail stores. Using the model shown in Figure 6-4, and assuming the same predictive relationships hold, if Store A showed a five-point improvement in employee attitudes, relative to other comparable stores, and if quarterly revenue growth among stores in the same district went from an average of $16 million to $16.8 million, what is the expected level of revenue for Store A? |
3. | You have read about the employee-customer-profit chain at Sears and how it serves as a business model for the company. As a senior manager, respond to the following questions: What implications might such a model have for recruitment, selection, orientation, training, performance management, and incentive compensation? What practical issues have to be considered in deploying the model throughout the company? |
4. | What is the value-profit chain? Explain each link and why it is important in understanding how management practices affect employee satisfaction, customer satisfaction, and ultimately, long-term profitability and growth. |
5. | You are CEO of a public relations company. You have just read about the 5-STAR management model in the value-profit chain and want to implement it in your company. Develop a detailed strategy for embedding the model into your organization’s culture. |
| 1. | W. F. Cascio and C. Young, “Work-family balance: Does the market reward firms that respect it?” in D. F. Halpern and S. E. Murphy (eds.), From Work-Family Balance to Work-Family Interaction: Changing the Metaphor (Mahwah, NJ: Lawrence Erlbaum Associates, 2005) 49–63. |
| 2. | The S&P 500 is one of the most widely used benchmarks of the performance of U.S. equities. It represents leading companies in leading industries, and consists of 500 stocks chosen for their market size, liquidity, and industry-group representation. Each stock’s weight in the index is proportionate to its market capitalization (stock price times number of shares outstanding). The Russell 3000 is an index of the 3,000 largest U.S. companies weighted by market capitalization. It includes only common stocks incorporated in the United States and its territories, and represents approximately 98 percent of the investable U.S. equity market. |
| 3. | I. S. Fulmer, B. Gerhart, and K. S. Scott, “Are the 100 best better? An empirical investigation of the relation ship between being a ‘great place to work’ and firm performance,” Personnel Psychology, 56, 2003, 965–993. |
| 4. | Ibid. 987. |
| 5. | G. Filbeck and D. Preece, “Fortune’s Best 100 companies to work for in America: Do they work for shareholders?” Journal of Business Finance & Accounting, 30:5, 2003, 771–797. |
| 6. | E. Esen, SHRM Job Satisfaction Series: 2006 job satisfaction (Alexandria, VA: Society for Human Resource Management, June 2006). |
| 7. | J. E. Mathieu and D. M. Zajac, “A review and meta-analysis of the antecedents, correlates, and consequences or organizational commitment,” Psychological Bulletin, 108:2, 1990, 171–194. |
| 8. | K. Carrig and P. M. Wright, Building Profit through Building People (Alexandria, VA: Society for Human Resource Management, 2006). |
| 9. | W. Schaufeli, M. Salanova, V. Gonzá lez-Romá, and A. B. Bakker, “The measurement of engagement and burnout: A two-sample confirmatory factor-analytic approach,” Journal of Happiness Studies, 3, 2002, 71–92. |
| 10. | M. Salanova, S. Agut, and J. M. Peiroá, “Linking organizational resources and work engagement to employee performance and customer loyalty: The mediation of service climate,” Journal of Applied Psychology, 90, 2005, 1216–1227. |
| 11. | S. M. Brooks, J. W. Wiley, and E. L. Hause, “Using employee and customer perspectives to improve organizational performance,” in L. Fogli (ed.), Customer Service Delivery (San Francisco: Jossey-Bass, 2006) 52–82. A. Cohen, “Organizational commitment and turnover: A meta-analysis,” Academy of Management Journal, 36, 1993, 1140–1157. C. Ostroff, “The relationship between satisfaction, attitudes, and performance: An organizational-level analysis,” Journal of Applied Psychology, 77, 1992, 963–974. A. M. Ryan, M. J. Schmit, and R. Johnson, “Attitudes and effectiveness: Examining relations at an organizational level,” Personnel Psychology, 49, 1996, 853–883. K. L. Rogg, D. B. Schmidt, C. Shull, and N. Schmitt, “Human resource practices, organizational climate, and customer satisfaction,” Journal of Management, 27, 2001, 431–449. W. Schaufeli, and A. B. Bakker, “Job demands, job resources, and their relationship with burnout and engagement: A multi-sample study,” Journal of Organizational Behavior, 25, 2004, 293–315. |
| 12. | B. Schneider, S. S. White, and M. C. Paul, “Linking service climate and customer perceptions of service quality: Test of a causal model,” Journal of Applied Psychology, 83, 1998 150–163. |
| 13. | Salanova et al., 2005, op. cit. |
| 14. | A. J. Rucci, S. P. Kirn, and R. T. Quinn, “The employee-customer-profit chain at Sears,” Harvard Business Review, Jan-Feb 1998, 82–97. |
| 15. | Corporate Voices for Working Families, “Business impacts of flexibility: An imperative for expansion,” November 2005, at www.cvwf.org. |
| 16. | J. M. Feldman and J. G. Lynch, Jr. “Self-generated validity and other effects of measurement on belief, attitude, intention, and behavior,” Journal of Applied Psychology, 73, 1988, 421–435. J. M. Olson and G. R. Maio, “Attitudes in social behavior,” in T. Millon and M. J. Lerner (volume eds.), Handbook of Psychology, Vol. 5, Personality and Social Psychology (Hoboken, NJ: Wiley, 2003) 299–324. |
| 17. | S. K. Parker, “Job satisfaction,” in S. G. Rogelberg (ed.), Encyclopedia of Industrial and Organizational Psychology (Vol. 1) (Thousand Oaks, CA: Sage, 2007) 406–410. See also W. K. Balzer and J. Z. Gillespie, “Job satisfaction measurement,” in S. G. Rogelberg (ed.), Encyclopedia of Industrial and Organizational Psychology (Vol. 1) (Thousand Oaks, CA: Sage, 2007) 410–413. |
| 18. | N. J. Allen, “Organizational commitment,” in S. G. Rogelberg (ed.), Encyclopedia of Industrial and Organizational Psychology (Vol. 2) (Thousand Oaks, CA: Sage, 2007) 548–551. |
| 19. | R. T. Mowday, R. M. Steers, and L. W. Porter, “The measurement of organizational commitment,” Journal of Vocational Behavior, 14, 1979, 224–247. |
| 20. | L. Micco, “Gallup study links worker beliefs, increased productivity,” HR News, September 1998, 17. |
| 21. | F. K. Pil and J. P. MacDuffie, “The adoption of high-improvement work practices,” Industrial Relations, 35:3, 1996, 423–455. |
| 22. | B. Schneider, P. J. Hanges, D. B. Smith, and A. N. Salvaggio, “Which comes first: Employee attitudes or organizational financial and market performance?” Journal of Applied Psychology, 88:5, 2003, 836–851. |
| 23. | Ryan et al., op. cit; Schneider, White, and Paul, op. cit. |
| 24. | Micco, op. cit. |
| 25. | J. K. Harter, F. L. Schmidt, and T. L. Hayes, Business-unit-level relationship between employee satisfaction, employee engagement, and business outcomes: A meta-analysis,” Journal of Applied Psychology, 87, 2002, 268–279. |
| 26. | Schneider, Hanges, Smith, and Salvaggio, op. cit. |
| 27. | P. H. Mirvis and E. E. Lawler, III, “Measuring the financial impact of employee attitudes,” Journal of Applied Psychology, 62, 1977, 1–8. |
| 28. | J. O. March and H. A. Simon, Organizations (New York: Wiley, 1958). |
| 29. | E. E. Lawler, Motivation in Work Organizations (Monterey, CA: Brooks/Cole, 1973). |
| 30. | V. H. Vroom, Work and Motivation (New York: Wiley, 1964). |
| 31. | Rucci et al., op. cit. |
| 32. | Ibid., 88. |
| 33. | Personal communication between Steve Kirn and John Boudreau, 2000. |
| 34. | J. L. Heskett, T. O. Jones, G. W. Loveman, W. E. Sasser, Jr., and L. A. Schlesinger, “Putting the service-profit chain to work,” Harvard Business Review, March-April 1994, 164–174. |
| 35. | Carrig and Wright, op. cit. |
| 36. | Ibid. |
| 37. | Ibid. |
| 38. | Ibid. |