Week 5 Assignment

HR analytics and performance appraisal system

A conceptual framework for employee performance improvement

Anshu Sharma Department of Human Resource Management, School of Management,

BML Munjal University, Gurgaon, India, and

Tanuja Sharma Department of Human Resource Management,

Management Development Institute, Gurgaon, India

Abstract Purpose – This paper aims to explore the role of human resource (HR) analytics on employees’ willingness to improve performance. In doing so, the paper examines issues related to the performance appraisal (PA) system which affect employees’ willingness to improve performance and how HR analytics can be a potential solution to deal with such issues.

Design/methodology/approach – The paper develops a conceptual framework along with propositions by integrating both academic and practitioner literatures, in the field of HR analytics and performance management.

Findings – The paper proposes that the use of HR analytics will be negatively related to subjectivity bias in the PA system, thereby positively affecting employees’ perceived accuracy and fairness. This further positively affects employees’ satisfaction with the PA system, which subsequently increases employees’ willingness to improve performance.

Research limitations/implications – The paper provides implications for both researchers and practitioners in the performance management area for improving employees’ performance by applying HR analytics as a strategic tool in the PA system. It also provides implications for future researchers to empirically test the conceptual framework in different organizational settings.

Originality/value – The paper offers insights into how the use of HR analytics can deal with issues of subjectivity bias in the PA system and positively affects employees’ willingness to improve performance.

Keywords Performance appraisal, Employee performance, Performance improvement, HR analytics, Perceived accuracy

Paper type Conceptual paper

1. Introduction Employees are a significant investment for organizations (Schraeder and Jordan, 2011), as they have the power to affect organizational effectiveness (Sundaray, 2011). To meet increasing competition, they are expected to perform higher and better (Biswas and Varma, 2011). With rising importance of employee performance for organizational effectiveness and competitive advantage, organizations are increasingly investing in various development activities such as coaching, developmental centers and career planning for performance improvement (Hameed and Waheed, 2011). It is seen that employees’ performance improvement and effectiveness are strongly affected by their performance evaluations during performance appraisals

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Received 14 April 2016 Revised 20 November 2016 11 February 2017 Accepted 17 March 2017

Management Research Review Vol. 40 No. 6, 2017 pp. 684-697 © EmeraldPublishingLimited 2040-8269 DOI 10.1108/MRR-04-2016-0084

The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/2040-8269.htm

(Latham et al., 1993; Gibbons and Kleiner, 1994; Mir and Ahmed, 2014). Out of all human resource (HR) practices, the performance appraisal (PA) system is seen as most critical, but it also accounts for a large portion of employees’ dissatisfaction in terms of perceived fairness and effectiveness (Shrivastava and Purang, 2011), as biased performance evaluations create challenges for ethical decision-making in organizations (Maas and Torres-González, 2011), and usually result in employee dissatisfaction with the appraisal process (Ahmad et al., 2012). Dissatisfaction with the performance process can further be linked to negative employee outcomes such as higher turnover intention and lower commitment levels (Dusterhoff et al., 2014), which subsequently negatively affects employee performance (Fu and Deshpande, 2014; Wong et al., 2015).

However, there is limited research on how the PA system can help improve employee performance (DeNisi and Pritchard, 2006). This may be a probable reason why most companies only report overall effectiveness and efficiency of their PA system and shy away from reporting its effect on employee performance (Fink, 2010). Establishing an effective PA system is one of the key challenges faced by HR professionals for performance improvement (Harrington and Lee, 2015). Hence, there is a strong need for research to look into how PA systems can be made more acceptable to employees and to further examine their impact on employee performance. Reviewing literature on PA systems, Murphy and DeNisi (2008) suggested that research needs to examine the effects of new technologies on PA systems, as it is seen that adoption and implementation of new information technologies improve performance in organizations (Edmondson et al., 2003; Wang, 2010; Schraeder and Jordan, 2011). Recently, Farr et al. (2013) highlighted that incorporating technology into the PA system has several benefits over traditional PA systems and can benefit both organizations and employees. New age technology, such as analytics, also referred to as HR analytics, when used for HR purposes (Bassi, 2011; Davenport et al., 2010; Fink, 2010; Levenson, 2005), can have a significant impact on individual and organizational performance (CIPD, 2015). It is also seen that top-performing organizations tend to apply analytics rather than intuition to their decision-making activities, which differentiates them from their low-performing counterparts (LaValle et al., 2011). However, it is observed that HR analytics still play a little role in HR strategy formulation and decision-making (Falletta, 2014). Hence, in this paper, we aim to explore the role of HR analytics in the PA system and subsequently on employees’ willingness to improve performance.

2. Research question The purpose of the present paper is to explore two research questions:

RQ1. How does the PA system affect employee’s willingness to improve performance?

RQ2. How does the use of HR analytics in the PA system affect employee’s willingness to improve performance?

To explore the above-stated research questions, the paper begins by examining the issues related to the PA system that affect employees’ willingness to improve performance. The paper then examines how the use of HR analytics in the PA system can be used to deal with such issues.

3. Theoretical development 3.1 Performance appraisal system and issues of subjectivity bias Performance measurement is a key element of performance management (Brudan, 2010). One of the issues in measuring performance is that it is not a static entity but a fluid process,

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hence there are a number of levels at which performance can be measured, such as input, output and processes (Stannack, 1996). To improve performance, it is important to quantify the multi-dimensional aspects of performance which play a dominant role in performance measurement systems for better measurement and management of performance (Dervitsiotis, 2004). PA systems designed by organizations may vary in their levels of subjectivity and objectivity in their evaluation criterion, where subjectivity is defined as the extent to which rater has a direct personal influence on the ratee’s performance rating (Maas and Torres-González, 2011). Although, subjectivity in performance measurement was introduced to decrease distortion by taking into account those aspects of the employees’ job that cannot be captured through quantitative measures or in those cases where the employer is not able to measure what he requires from employees (Kauhanen and Napari, 2012). Subjective performance measures can be defined as the superior’s subjective judgments about the qualitative aspects of the job performance and increased discretion of managers in performance ratings (Moers, 2005), which also resulted in performance evaluation bias.

PA systems suffer from subjectivity bias for various reasons (Laird and Clampitt, 1985); one such reason is the human element related to raters’ attributions and expectations (Moser, 1992; Gibbons and Kleiner, 1993), assessment being a cognitive process. Managers’ cognitive ability to recall employees’ performance behavior over a period adds to PA biases in which performance information is selected, observed and organized by them, which leads to observational inaccuracy affecting accuracy and effectiveness of the PA system (Lee, 1985). Tsui and Bruce (1986) suggested that affect is a source of bias in appraisal, as it reduced rater accuracy in performance ratings. Personal factors such as employees’ gender, mood and interpersonal affect (Robbins and DeNisi, 1993, 1998) were also found to bias PA ratings. Interpersonal affect was found to affect performance appraisal ratings and showed how managers inflate performance ratings of low-performing subordinates due to interpersonal affect (Varma et al., 1996; Varma et al., 2005). Ittner et al. (2003) revealed that inherent subjectivity in the balanced score card plan led to the problems of favoritism and uncertainty in the reward system. Earlier multi-source assessments, also known as 360- degree feedbacks, were used to increase objectivity; however they also faced certain issues, such as the non-equivalence in ratings (Van der Heijden and Nijhof, 2004). Subjectivity in performance measurement was found to be a strong reason for inconsistent application of objective performance measures and a potential gaming strategy (Watts et al., 2009). Highlighting the presence of subjective biases in the PA process, Bento, White and Zacur (2012) revealed how obesity stigma influenced employees’ PA, once again questioning the “the ethos of objectivity” in PA. Also, centrality bias, a type of manager’s performance evaluation bias where the manager tends to compress performance ratings, emerges when managers subjectively evaluate performance, and this bias negatively affects performance improvement (Bol, 2011). Few researchers have shown that cultural variation of the rater in the form of interdependent self-construal also leads to subjective biases such as evaluation leniency and creates preferences during performance evaluations (Mishra and Roch, 2013; Saffie-Robertson and Brutus, 2014). The implementation of pay-for-performance raised issues related to perceived inequity due to subjective biases in performance measurement (Park, 2014). Most of the performance evaluations are deliberately distorted or biased (Campbell et al., 1998). Most of the employee dissatisfaction issues associated with the PA system are related to this subjectivity in performance measurement (Cooke, 2008). There is a need to reduce rater bias, as it is seen as a barrier to effective PA, such as gender and group identification (Roberson et al., 2007; Wilson, 2010; Javidmehr and Ebrahimpour, 2015). Issues of subjectivity related to human cognition make it difficult for the performance management system to be fair and accurate (Kim and Rubianty, 2011), and subjective

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evaluations are perceived to be unfair and biased (Maas and Torres-González, 2011). Hence, it is proposed that subjectivity bias in the PA system would decrease employees’ perceived accuracy and fairness of the PA system.

P1. Subjectivity bias in the PA system will be negatively related to the employees’ perceived accuracy and fairness of the PA system.

3.2 HR analytics and performance appraisal system Biases result in discrimination at work, and means should be employed to check such biases (Hennessey and Bernardin, 2003; Kastl and Kleiner, 2003). Scholars have highlighted the need to resolve issues related to subjective biases in performance evaluation (Laird and Clampitt, 1985; Maas and Torres-González, 2011; Moers, 2005; Van der Heijden and Nijhof, 2004; Watts et al., 2009). Providing objective measures is one way to mitigate biases in supervisory ratings (Campbell et al., 1998). Researchers suggested structured diary-keeping as one way to reduce inaccuracy by minimizing performance information recall bias (DeNisi and Peters, 1996; Varma et al., 1996). The principal–agent model states that favoritism and bias can be reduced by placing more emphasis on objective rather than subjective measures in the PA (Ittner et al., 2003) and with the use of observable, objective evaluation criteria. Performance measurement is never seen as a complete scientific activity, and there is always a need to develop frameworks that generate accurate and trustworthy information for HR use (Baron, 2011). Organizations have started appreciating the need for unbiased, accurate and timely performance information, as the time and quality of information provided determine the speed and quality of HR decision-making (Hill, 2013). According to Simon (1955), human decision-making is bounded by their limited cognitive ability and the availability of information for making that decision, which he conceptualized as the term “bounded rationality”. He posited that the quality of managerial decisions improves substantially, that is it becomes “objectively rational” if done with computer-assisted reasoning, as these decisions are not accompanied with any social and/or cognitive biases (Simon, 1996). Tools such as fuzzy multi-attribute decision-making have been found to make fair performance evaluations by identifying and sorting employees based on their improvement needs (Manoharan et al., 2011). Hence, conscious efforts should be made by organizations to use information systems so as to facilitate unbiased decision-making (Maas and Torres-González, 2011).

This is where HR analytics can play a significant role. HR function had undergone transformation with the advent of the human resource information system, and there are possibilities that analytics will further transform HR into a strategic business partner by providing performance data (Lawler et al., 2004). The huge data collected through various information systems are of little use, if the data cannot be properly analyzed to provide meaningful implications (Pemmaraju, 2007). Although most of the organizations till now used analytics to make financial and operational decisions, organizations have begun to use analytics for HR decisions, such as to evaluate employee performance and/or to allocate employees’ time and effort (Kiron et al., 2012). HR metrics are found to affect HR decisions (Dulebohn and Johnson, 2013), but HR analytics is more than just metrics and/or scorecards (Mondore et al., 2011), it consists of various modeling tools such as behavioral modeling, predictive modeling, impact analysis, cost–benefit analysis and ROI analysis (Levenson, 2005) required for strategic HR decision-making. Also, the use of analytics makes it easier to collect, document and retrieve a variety of performance data from various sources (both external and internal), which provides manager with better information to observe employee performance in terms of both outcome and behavior. Analytics has greater ability to capture

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and aggregate data; hence, the insights derived through data analytics help to take fact- based decisions (Kiron et al., 2012) and help managers to focus on hard facts rather than intuition, which also changes the power dynamics in the company (Falletta, 2014). As the use of HR analytics provides integrated, consistent and trustworthy data (LaValle et al., 2011), it can significantly reduce biases related to human cognition. Reducing such subjectivity biases makes the PA process more accurate and reliable (Murphy and DeNisi, 2008). Hence, it is proposed from the above discussion that HR analytics can help increase perceived accuracy of the PA system by giving more objective, accurate and unbiased data related to employees’ performance behavior.

P2a. Use of HR analytics in the PA system will be negatively related to the subjectivity bias in the PA system.

P2b. Use of HR analytics in the PA system will be positively related to employees’ perceived accuracy and fairness of the PA system.

3.3 Employees’ satisfaction with the performance appraisal system Researchers need to study factors that predict positive employee reactions to appraisals, such as their perceived accuracy, fairness and satisfaction with the PA system (Pichler, 2012). Employees’ perception of PA system effectiveness is measured through their perceived accuracy and fairness of the PA system (Sharma et al., 2016). Perceived fairness of the PA system is found to be affected by fulfillment of employees’ psychological contract (Harrington and Lee, 2015). In a multi-level study, Farndale and Kelliher (2013) found that organizational commitment was affected by employees’ perceived fairness of the PA system. Employees lose trust in the PA system and subsequently in the performance ratings when they do not see this system to be fair (Murphy and DeNisi, 2008). In a meta-analytical review of justice literature, Colquitt et al. (2001) revealed that fairness perceptions at work were largely affected by justice perceptions. Organizational justice theory (Skarlicki and Latham, 1996) has often been used to understand acts of perceived discrimination in an organization (Harris et al., 2004; Bibby, 2008; Wood et al., 2013). Using organizational justice as a theoretical support, Greenberg (1990, 2004) posits that the construct of perceived fairness of the PA system is multidimensional in nature with three sub-constructs, namely, distributive, procedural, interactional – interpersonal and relational justice. These justice dimensions can be linked to perceived fairness of an actual appraisal rating, of procedures used to determine the appraisal rating and of the rater’s interpersonal treatment of the ratee during the appraisal process, respectively (Narcisse and Harcourt, 2008). Also, justice dimensions are found to affect reciprocatory behaviors by employees (Frenkel and Bednall, 2016). Fairness perceptions influenced by these justice perceptions lead to satisfaction with the PA system and performance feedback (Jawahar, 2007). Justice is also seen as a predictor to acceptability of the PA system (Briscoe and Claus, 2008). Justice perceptions have found to mediate relationships between administrative PA activities (namely, salary adjustments, promotion decisions and performance standards) and organizational commitment (Zhang and Agarwal, 2009), and satisfaction with the PA system (Thurston and McNall, 2010). Effectiveness of the PA system depends on justice perceptions of employees (Clarke et al., 2013). Also, perceptions of organization justice have been found to affect ethical and unethical behavior at work (Jacobs et al., 2014). However, perceived accuracy, the extent to which the performance evaluation accurately captures employees’ actual job performance (Kim and Rubianty, 2011), is seen as an important antecedent to employees’ justice perceptions, particularly their perceptions of distributive justice (Narcisse and Harcourt,

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2008). Increased accuracy of the decisions positively affects justice perceptions in the PA system (Briscoe and Claus, 2008). On the contrary, acceptability of the PA system increases trust in the management (Mayer and Davis, 1999). Perceived accuracy has been identified as an important predictor of employees’ satisfaction with the PA system (Keeping and Levy, 2000). Also, from the perspective of moral judgment, employees’ satisfaction with the PA system is partly determined by the perceived moral justifiability of the PA process (Dusterhoff et al., 2014). Hence, perceived accuracy and fairness become important antecedents which can affect employees’ satisfaction with the PA process.

P3. Employees’ perceived accuracy and fairness of the PA system will be positively related to employees’ satisfaction with the PA system.

3.4 Employees’ willingness to improve performance Research claims that employees’ performance improvement after receiving performance feedback largely depends on their attitude toward the PA system (Maurer and Tarulli, 1996). Literature on performance feedback suggests that most of the time feedback interventions had a negative impact on performance (Kluger and DeNisi, 1996; Cannon and Witherspoon, 2005), as performance improvement is most likely to occur when the receiver has a positive feedback orientation and reacts positively to change (Smither et al., 2005). Also if employees accept the PA system, the supervisor/rater is likely to give true feedback and would not resort to other means of performance improvement because he/she understands that employees are more likely to accept their feedback (Briscoe and Claus, 2008). Also, the relationship between performance ratings and feedback acceptance is mediated by the employee reactions to feedback (Bell and Arthur, 2008).

One of the reasons for non-acceptance of the performance feedback is the lack of agreement with the PA system (Campbell et al., 1998). Employees often disagree with their performance evaluations, as they perceive them to be inaccurate (Campbell and Lee, 1988). Such performance evaluations, in the form of performance ratings, do not provide sufficient information for employees to improve performance, as these rating scales do not completely eliminate the subjectivity bias (Van der Heijden and Nijhof, 2004). The perceived fairness and accuracy of performance feedback is one of the determinants for employees’ willingness to improve performance (Lee and Akhtar, 1996). The acceptance of performance feedback increases self-efficacy among employees with regard to that feedback (Nease et al., 1999). Even negative feedback can result in performance improvement (Fedor et al., 2001), if that feedback is accepted by employee. Only if employees accept and trust the system to be legitimate, they have positive reactions to their performance feedback (both positive and negative) and will try to improve their performance (Briscoe and Claus, 2008). After the initial reaction to feedback, the employee sets the goal and start taking action which can lead to performance improvement (Smither et al., 2005). Satisfaction with PA feedback has also been linked to satisfaction with the rater, job satisfaction and organizational commitment (Jawahar, 2006). Fairness and justice perceptions (Colquitt et al., 2001) and satisfaction with the PA system are seen as important predictors of employee performance (DeNisi and Pritchard, 2006). Hence, satisfaction with the PA system can affect work performance (Kuvaas, 2006). Employees’ perceptions of fairness and accuracy is affected by the quality of PA feedback, which affects employees’ performance (David, 2013; Selvarajan and Cloninger, 2012). Employee performance can be improved by increasing their willingness to improve performance after receiving the performance feedback. This can happen only when they accept the feedback received

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and work on it, which largely depends on how satisfied they are with the PA system. Hence, it is proposed that employees’ satisfaction with the PA system would result in an increase in employees’ willingness to improve performance.

P4. Employees’ satisfaction with the PA system will be positively related to employees’ willingness to improve performance.

4. Conceptual framework The conceptual model emerging from the above discussion is shown in Figure 1. The propositions are denoted as P1, P2a, P2b, P3 and P4. The propositions explaining negative relationships are denoted by dotted lines (P1 and P2a). Likewise, solid lines denote positive relationships (P2b, P3 and P4). Here, P1 explains a negative relationship as to how subjectivity bias in the PA system reduces employees’ perceived accuracy and fairness of the PA system. Similarly, P2a explains how the use of HR analytics in the PA system negatively affects the subjectivity bias in the PA system. The other three propositions (P2b, P3 and P4) explain positive relationships such as how perception of accuracy and fairness of the PA system increases employees’ satisfaction with the PA system, subsequently increasing their willingness to improve performance.

5. Discussion and conclusion This paper contributes in several ways. First, it integrates and extends the literature on two independent fields of study: analytics and PA, former being predominantly an information technology domain (Pemmaraju, 2007) and latter being an HR management domain. Thus, the present study is inter-disciplinary in nature. Second, it is one of the few studies to examine the role of HR analytics on the PA system and employees’ performance improvement. Third, it attempts to address the call of researchers to deal with issues of subjectivity in the PA system by identifying HR analytics as a potential solution (Laird and Clampitt, 1985; Maas and Torres-González, 2011; Moers, 2005; Van der Heijden and Nijhof, 2004; Watts, Augustine and Lawrence, 2009) with organizational justice theory (Skarlicki and Latham, 1996) and bounded rationality (Simon, 1955) as the theoretical underpinning.

Future researchers may empirically test this conceptual framework and propositions in different organizational settings to study how HR analytics affect PA systems and employee

Figure 1. Conceptual model

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performance improvement. The study can be extended further by linking HR analytics to other important employee and organizational outcomes, such as PA, which is found to affect employees’ participation in informal learning activities at work (Bednall et al., 2014), and organizational performance (Ayers, 2015). As employee performance is seen as a function of both individual and organizational factors (Douglas, 2014), future studies may relate how HR analytics can improve employee performance by linking to other organizational factors such as organizations’ service climate (Sharma, 2008) and organizational psychological climate (Biswas and Varma, 2011), which have been found to affect employee performance. Also, it is important to note that the role of HR analytics in reducing biases in the PA system is limited to the quality of data. HR needs to measure what is important rather than measuring what is easy to (Bassi, 2011; Ingham, 2011).

Recently, analytical tools such as Synergita and IBM Kenexa HR analytics powered by IBM Watson help HR professionals to get insights into performance data for performance improvement and talent management (IBM, 2017; Synergita, 2017). In one of Gartner’s research notes, Hostmann et al. (2009) developed a performance management framework linking analytics and business intelligence. Our paper resonates with the work of Kasemsap (2015) on how business analytics can be used for organizational transformation such as performance management.

However, the use of HR analytics for strategic decision-making largely depends on the organizational culture because to promote fact-based decision-making to reduce the cognitive biases in PA, organizations should have data-oriented leadership (LaValle et al., 2011). Such a data-driven culture may be defined as:

[. . .] a pattern of behaviors and practices by a group of people who share a belief that having, understanding and using certain kinds of data and information plays a critical role in the success of their organization (Kiron et al., 2013, p. 18).

Based on this culture, organizations may be categorized on their level of analytical capability from analytically impaired to analytical competitors (Davenport and Harris, 2007). DELTA (Data, Enterprise, Leadership, Target and Analysts) provides a basic framework for implementing analytics in organizations (Davenport et al., 2010). Willing firms which are analytical innovators build a data-oriented culture by recruiting and promoting analytical talent (Ransbotham et al., 2015).

To conclude, this paper made an attempt to explore the role of HR analytics on PA system and its subsequent impact on employees’ willingness to improve performance by proposing a conceptual model with testable propositions. The paper highlights subjectivity bias in the PA system as one of the issues that needs to be addressed to increase its perceived accuracy and fairness, which in turn affect employees’ satisfaction with the appraisal system. To do so, HR analytics was found to be a potential solution by increasing accuracy and objectivity in the appraisal process with the use of sophisticated data analysis tools. Along with implications for both practitioners and researchers in the field of performance management, the paper also suggested directions for future research to further enrich the field.

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Corresponding author Anshu Sharma can be contacted at: anshu.sharma@hotmail.co.in

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