Figure 18.1 The High Performance Cycle
Laura Borgogni Department of Psychology, Sapienza University of Rome Silvia Dello Russo Business Research Unit, ISCTE-IUL
The High Performance Cycle (HPC), developed by Locke and Latham (1990), is a meta-theory, based on principles and empirical findings supporting goal setting theory, that predicts, explains, and influences an employee’s job performance and satisfaction (see Figure 18.1). At the model’s core is the relationship between specific, difficult goals (demands) and these two dependent variables. This relationship is mediated by four mechanisms. Boundary conditions are also specified regarding the extent to which demands/ goals lead to high performance. The HPC shows that job performance leads to both intrinsic and extrinsic rewards and, through these, to job satisfaction. Job satisfaction, in turn, is related to organizational commitment that leads to the setting of and commitment to high goals and hence the recursive nature of the HPC. Latham, Locke, and Fassina (2002) provided an enumerative review of the literature and found strong support for the HPC.
In this chapter, we provide a brief overview of the literature on the HPC published after Latham et al.’s (2002) review. This is followed by the presentation of a questionnaire developed to quantitatively assess the HPC, and empirical data that tested the validity of aspects of the HPC’s predictions for employees in Italy.
The HPC states that a goal’s specificity and level of difficulty, which are referred to as demands, affect job performance positively through three motivational mechanisms, namely, direction, effort, and persistence (Latham et al., 2002). As noted by Latham and Locke (2007), goals focus attention on and direct subsequent behavior to relevant aspects of tasks; this typically leads to ignoring performance dimensions for which goals are not set (Latham & Locke, 2006). Second, goals serve an energizing function. This is because people expend effort in pursuing their goals. Therefore, higher demands propel employees forward through greater effort to attain higher performance. Moreover, goals prolong effort over time, thus affecting persistence.
In addition to these three motivational mechanisms, a fourth mediator is primarily cognitive in nature. When pursuing a goal, employees automatically use their existing skills and knowledge to execute well-established action plans. If the extant knowledge is inadequate for performing a new task, goal setting stimulates the discovery and development of new strategies (Latham, Seijts, & Crim, 2008).
Five factors moderate the goal–performance relationship.
Figure 18.1 The High Performance Cycle
Ability and self-efficacy. Specific, difficult goals lead to higher performance only if people possess the ability to perform the task, and believe they can use their knowledge and skill when required. Self-efficacious employees interpret difficult goals as challenges rather than obstacles (Bandura, 2011). This is because the underlying properties of self-efficacy are anticipation, self-regulation, and self-reflection, which enable people to direct and adjust their behaviors. Self-efficacy is positively correlated with the development of task-related strategies and, through these, to performance (Seijts & Latham, 2001).
Goal commitment. Goal commitment is an individual’s determination to pursue a goal over time. It implies a psychological bind to a goal (See Chapter 6). “It is virtually axiomatic that if there is no commitment to goals, then goal setting will not work” (Locke, Latham, & Erez, 1988, p. 23). Factors that bring about commitment to difficult goals are both situational (such as receiving support from the organization) and personal (particularly individual motives and the anticipation of rewards). Empirical research has shown that goal commitment not only moderates the relationship between difficult goals and performance, but it can also have a main effect on performance (Seijts & Latham, 2011).
Feedback. Feedback can lead to revising, either downward or upward, the goals set by an individual (Tolli & Schmidt, 2008). Feedback can increase or decrease a person’s self-efficacy, in particular when internal rather than external attributions are made. Consistent with social cognitive theory (Bandura, 2011), this typically leads to adjusting one’s level of aspirations. In fact, feedback activates goal regulation, such that employees generate a positive discrepancy by setting a higher goal as a result of positive feedback, whereas they often reduce a negative discrepancy following negative feedback by lowering their goal (Ilies & Judge, 2005).
Task complexity. Task complexity encompasses component, coordinative, and dynamic complexity (Wood, 1986). Hence, complexity is due to (a) the number of elements included in the task (e.g., acts to be executed or information to be processed), (b) the relationship among those elements, and (c) the evolution of this relationship over time, that requires monitoring and adapting one’s behavior. On complex tasks, performance increases are due to the development of effective strategies appropriate to the difficulty of required actions. The resulting improvement in performance often involves a time lag (Seijts & Latham, 2005). This is because of the need to develop cognitive strategies, as well as the acquisition of new competencies and skills.
Situational constraints. In their discussion of factors that must be taken into account when setting goals, Latham and Locke (2006) stressed the necessity for (a) adequate resources (e.g., financial, technical) for employees to attain their goals, (b) the establishment of organizational systems and culture to support the goal setting process, (c) the provision of technical, operative, and emotional support by the immediate supervisor, (d) the minimization of conflicting goals (e.g., individual versus group goals; the activation of competitive dynamics among colleagues), and (e) the avoidance of increasing pressure on employees to achieve higher standards after the goal had been accomplished.
The HPC states that job performance leads to contingent rewards and, through these, to job satisfaction. Contingent rewards include pay, promotion, career opportunities, recognition, and feelings of accomplishment. In two meta-analyses, a moderate correlation (p = .29) was found between an individual’s actual pay and pay level satisfaction, whereas a significant but weak correlation (p = .15) was highlighted between level of pay and his or her overall job satisfaction (Judge, Piccolo, Podsakoff, Shaw, & Rich, 2010; Williams, McDaniel, & Nguyen, 2006 ). This suggests that there are a variety of factors that enhance job satisfaction. For example, intrinsic rewards (e.g., pride in accomplishment) following goal attainment increases job satisfaction (Latham, 2007). A central point in the HPC is that job satisfaction is related to job performance only if the latter is perceived as instrumental for attaining valued outcomes.
Job satisfaction is not just dependent on performance rewards but also “noncontin-gent” benefits, namely, positive returns to employees that are not linked to goal attainment, yet are connected to the nature of employment relationship (Jacobsen & Skillman, 2008). These include base-pay, benefits, job security, flexible hours, amount of leave, and congenial co-workers. They affect job satisfaction when they satisfy different needs, such as autonomy, pride, and work flexibility. There is evidence linking each of these noncontingent rewards to job satisfaction: fringe benefits (Artz, 2010); job security (OECD, 1997); and flexible work arrangements through the mediation of work–family enrichment (McNall, Masuda & Nicklin, 2010).
Finally, the HPC model states that job satisfaction affects organizational commitment. Job dissatisfaction leads an employee to reduce his or her commitment to the organization, namely, the identification with the company’s values, a desire to strive for the organizational goals, and the willingness to maintain membership in the organization.
Although the single paths of the model have been extensively studied and supported (Latham & Locke, 2007), only one study has empirically tested the overall HPC model. Selden and Brewer (2000) conducted a study involving over 2000 senior executives in US federal agencies. This sample was chosen because of its homogeneity in terms of pay plans based on performance compared to employees in other agencies. The authors selected items from data collected in the Survey of US Federal Government Employees to analyze the HPC. That is, they selected from that survey items that could be used as proxy variables for the HPC’s theoretical concepts to test the model. Because they could not match responses to the questionnaire items with external sources of information about an employee’s performance, they had to rely on self-report measures of job performance. Overall support for the hypothesized paths of influence were found. Specifically, the positive relationship between demands and performance was supported, as well as the positive association between performance and contingent rewards. The moderators, goal commitment and feedback, were significantly related to demands. However, the mediators were either not significant or negatively related to demands (i.e., persistence).
Selden and Brewer concluded that the HPC has practical significance for policy makers and managers in the public sector. This is especially true regarding the connections among job performance, contingent rewards, and job satisfaction. They also emphasized that financial incentives alone are not motivating variables; rather, they are effective only when high performance is in response to high goals, together with internal rewards (e.g., feelings of accomplishment and personal recognition).
In conclusion, the above-cited study suggests that the HPC has internal and external validity. Nevertheless, empirical data concerning an accurate operationalization of HPC constructs are needed, as is a test of its validity in the private sector.
In the remainder of this chapter, we report two empirical studies of the HPC conducted in an organization in Italy. The purpose of the first study was to develop a reliable and valid questionnaire to measure the variables in the HPC. The purpose of the second study was to test the model’s validity in a private multinational telecommunication company.
During the past 20 years many changes have occurred in Italy with regard to the business environment. There is an ongoing growth and competition between multinational companies in the country and local companies. These changes have affected Italian HR management practices, in that there is a shift:
As a result of these changes, goal setting is increasingly implemented in Italian organizations. Therefore, Italy provides an interesting context to investigate the validity of the HPC model, and to make recommendations for practice derived from the research results.
A questionnaire was developed by combining previously validated scales in the literature, and developing new items worded specifically to assess HPC constructs for which validated scales were not available. The questionnaire was tested in two samples in two private organizations, as described below.
The first sample consisted of 322 middle managers in a telecommunications organization. This sample consisted of 23% females and 77% males. Sixty-one percent of them ranged in age between 36 and 45 years old; 92% had more than 6 years of tenure in this organization.
The participants were employed in a company that was formed in 1997, during the “new economy.” It was the first provider in Italy of integrated wireless, fixed-line, and Internet services. From its beginning, this company has stood out in the marketplace for its innovative technological, organizational, and marketing skills. This led to the decision to acquire two additional telecommunication companies. In 2005, the majority shareholder, an Italian privatized company, sold the organization to an Egyptian telecommunication business. Major changes subsequently occurred with regard to organizational structure as well as in the senior management team. The senior management team was in place for two years when the present study was conducted. This management team focused on reducing costs and maximizing revenues. They downsized personnel and set demanding goals in terms of the results to be attained. This led the company to making a profit for the first time in its history.
The second sample consisted of 173 employees and managers from a multinational insurance company. The sample comprised 35% females and 65% males. Forty-four percent were 45 years old or more; 47% had between 1 and 5 years of tenure in the organization.
The company was founded in 2007 as a joint venture between a French multinational group and an Italian bank. As such, it is still in the start-up stage with regard to HR practices. The HR department has introduced several innovations in terms of procedures and tools for organizational development, including climate and culture surveys, leadership style assessment, talent management, and pay for performance.
A web-based questionnaire was delivered to both samples. Each participant received a personal code from the company’s HR department to voluntarily access the web site. Confidentiality of individual responses was guaranteed, because the HR department assigned random codes to employees, and the researchers who accessed and processed the data were unaware of the association between each code and an individual’s name.
A 53-item questionnaire was developed to measure the constructs in the HPC model. Each item was assessed using a 7-point Likert-type response format (1 = strongly disagree; 7 = strongly agree).
Demands. Consistent with Lee and Bobko’s (1992) argument that “perceptions of self-referenced goal difficulty may be more appropriate in ipsative models” (p. 1425), goal difficulty was measured using three of their five items (e.g., “The goals I am given require as much attention and effort as I can give”).
Mediators. Twelve items measured an employee’s (a) direction, (b) effort, (c) persistence, and (d) strategy in relation to their goals.
Self-efficacy. The 8-item scale by Chen, Gully, and Eden (2001) was used to measure self-efficacy (e.g., “I will be able to attain my goals”).
Goal commitment. Goal commitment was measured using three items from the scale developed by Hollenbeck, Klein, O’Leary, and Wright (1989) (e.g., “Quite frankly, I don’t care if I achieve this goal or not”).
Feedback. In order to assess the extent to which the supervisor gives feedback to employees in relation to their goals, four items were adapted from Locke and Latham’s (1990) questionnaire (e.g., “I get regular feedback indicating how I am performing in relation to my goals”, Reversed).
Task complexity. Consistent with Wood’s (1986) definition, three items were worded to assess task complexity (e.g., “In my job I perform complex tasks”).
Situational constraints. We operationalized this construct as the lack of constraints and the presence of opportunities in the organizational context that facilitate the goal setting process. Specifically, we used two scales: support by supervisor and organizational support. Three items measured the extent to which the supervisor is perceived as supportive of an employee’s goal attainment (e.g., “My boss is supportive when I face obstacles in my job”). Organizational support regarding the resources needed for attaining a goal was measured using three items (e.g., “Company policies here help rather than hurt goal attainment”). In both cases, items were adapted from Locke and Latham’s (1990) questionnaire.
Contingent rewards. Three items were used to measure both tangible (i.e., pay raise) as well as intangible (i.e., supervisory appreciation) rewards employees anticipate if their goals are attained. Two of these items were selected from Locke and Latham’s (1990) questionnaire (e.g., “If I reach my goals, it increases my chances for a pay raise”).
Noncontingent rewards. Three items measured rewards not dependent upon goal attainment (e.g., “I have good working conditions”).
Job satisfaction. Three items were adapted from Judge and colleagues (Judge, Locke, Durham, & Kluger, 1998) to measure overall job satisfaction (e.g., “Overall, I am satisfied with my job”).
Organizational commitment. Five items were adapted from the Italian version (Pierro, Lombardo, Fabbri, & Di Spirito, 1995) of Allen and Meyer’s (1990) affective commitment scale (e.g., “The organization where I work has great meaning for me”).
In order to test the multidimensionality of the questionnaire, a PAF exploratory factor analysis with direct oblimin rotation was conducted that allowed for correlations among factors (Fabrigar, Wegener, MacCallum, & Strahan, 1999). This was followed by a confirmatory factor analysis (CFA), conducted using Mplus (Muthén & Muthén, 1998) and the maximum likelihood estimation. Both analyses were conducted on the two samples combined, for a total sample size of 495 individuals.
After the exploratory analysis, items that did not load strongly on the intended factor (< .30), or cross-loaded on multiple factors were deleted. Thus, two items from the self-efficacy scale and two items from the noncontingent rewards scale were not included in subsequent analyses. The results showed that each theoretical construct in the HPC was represented by an empirical factor. The exception was items measuring feedback and support by the supervisor; these loaded on the same factor. In the confirmatory factor analysis, we estimated this new pattern of loadings.
Overall, fit indices from the CFA satisfied the recommended standards (e.g., Bagozzi & Yi, 1988; Browne & Cudeck, 1993). This suggests that the 14-factor model fit the data (χ2 = 2586.66, df = 1037, p < .01, N = 491; CFI = .90; RMSEA = .05 (CI .05− .06); SRMR = .05).
The standardized factor loadings, shown in Table 18.1, are all above .45. Moreover, the Cronbach alpha coefficients ranged from .65 to .93 (Table 18.2). Each empirical factor had adequate internal consistency. The corrected item-total correlations ranged between .44 and .84.
The scales assessing the HPC variables were shown to have adequate internal consistency. The factor loadings suggest that the HPC measures are valid. With only one exception,
Table 18.1 Items and Item Loadings from the First Confi rmatory Factor Analysis
the items loaded on the appropriate latent factor, thus providing empirical support for the HPC constructs. It is worth noting that the finding that feedback and supervisory support loaded on the same factor is consistent with previous goal setting field research (Ronan, Latham, & Kinne, 1973). Consequently, a second study was conducted to empirically test the HPC model.
After developing the questionnaire for assessing the HPC, we assessed the relationships among the variables included in the first part of the HPC model; that is, the antecedents of job performance were assessed.
A subsample of managers from the telecommunication sample (n = 101) who participated in the previous study was used in this second study. These managers had been assigned quantitative goals. Individual responses to the HPC questionnaire were collected nearly two months after each manager had been given goals for the year, and then matched with supervisory appraisals of job performance conducted at the end of the year. The evaluations of their job performance were provided by the HR department to the researchers.
Table 18.2 Bivariate Correlations
Job Performance. The performance appraisal was expressed as the percentage of goal attainment at the end of the year, as a composite of the quantitative goals (maximum three) assigned to each manager.
Table 18.2 presents the correlations among HPC scales and job performance.
Structural equation modeling (SEM) of the HPC model was conducted using the software Mplus (Muthén & Muthén, 1998). Following an iterative approach, we tested a model that could fit the observed data and could be considered a reliable representation of them. Thus, we chose a revised model, shown in Figure 18.2, in which some modifications to the original theoretical paths were implemented in order to improve the fit to an acceptable degree. These relationships held together theoretically.
We estimated a latent factor encompassing the four mediators, which was affected by demands. Demanding goals led managers to specify the direction of their actions, exert considerable effort, persevere in the face of obstacles, and search for suitable strategies. Consistent with the HPC model, these four mechanisms mediated the relationship between demands and job performance, as suggested also by a significant indirect link. Self-efficacy and goal commitment also exerted a main rather than a moderating effect on the mediators. Again, the indirect paths were significant. Therefore, the more confident managers were about their abilities and the more committed they were to their goals, the more they pursued their goals and the better their actual performance. Task complexity did not play a significant role in the model.
Figure 18.2 Empirical model tested in the telecommunication sample.
We estimated a latent factor that includes the dimension in which feedback merged with support from the supervisor, and organizational support. This second-order factor was derived from our conceptualization of organizational opportunities and was related to goal commitment. This finding is consistent with Hollenbeck and Klein’s (1987) argument that supervisory support and situational constraints are antecedents of goal commitment. Receiving timely feedback and support from one’s supervisor, in conjunction with organizational support, encouraged the managers to commit to their goals.
A revised model of the HPC was derived inductively. The variables in this model are related to one another. The relationships are consistent with previous findings (DeShon, Kozlowski, Schmidt, Milner, & Wiechmann, 2004; Donovan & Radosevich, 1998; Klein & Kim, 1998; Renn, 2003; Seijts & Latham, 2001). In particular, feedback, support by the supervisor, and organizational support are related to goal commitment. Goal commitment together with self-efficacy affected the mediating variables that are related to job performance. That is, the level of commitment to the goals and one’s confidence to succeed are related to (a) the pursuit of one’s goals, (b) the expenditure of effort and persistence, (c) the discovery of suitable strategies, and (d) high performance.
Overall, the results of the structural equation modeling show that the main tenets of the HPC model are valid. To the extent to which a manager received difficult goals, his or her self-efficacy and goal commitment affected his or her motivation, by activating the four motivational mechanisms specified in the HPC. These results, obtained in the private sector in Italy, support those obtained in the public sector in the United States (Selden & Brewer, 2000). However, it was not possible to verify the second part of the model concerning the consequences of job performance, namely contingent rewards, job satisfaction, and organizational commitment, because the data collected for these variables were only cross-sectional.
The limitations of this study are at least threefold. Although the questionnaire and the performance appraisals were collected during different time periods, we were not able to test the overall model and its recursive nature. Future research should investigate in the private sector the paths connecting job performance to contingent rewards, job satisfaction, and organizational commitment. Second, the sample size was relatively small. Finally, the revised HPC model should be replicated in different contexts in order to assess its generalizability.
The present results led the company to implement three interventions. First, top management now purposively acts as a role model for setting goals. They provide people the resources for goal attainment. For example, they supply equipment, information, money, as well as the time for people to attain their goals. They give managers freedom to develop strategies for pursuing their goals rather than prescribing specific action plans. Finally, they have instituted a meritocratic culture whereby rewards are now contingent on performance.
Second, training in self-efficacy is being conducted. Employees are given the opportunity to put themselves to the test and strengthen their self-efficacy through a series of successful experiences in daily work activities. Progressively more complex goals are set to increase employees’ self-efficacy. Managers also learn from colleagues, by observing their behaviors in the face of difficult situations, and discerning the underlying strategy that led them to succeed. A third source of self-efficacy is verbal persuasion, through which managers encourage themselves (and are encouraged by others) to engage in challenging activities. In this regard, verbal self-guidance has proved to be a useful method for increasing self-efficacy (Millman & Latham, 2001).
A third intervention concerns top management giving the necessary information to perform well and delivering timely feedback to subordinates. The company has also implemented a well-structured training program focused on leadership skills development. Rather than traditional training on leadership styles, they have started to promote self-awareness with regard to leaders’ perception of their role in general, particularly in relation to the goal setting process.
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