Despite incredible advancements in science and technology, employees across the world still seem to experience high degrees of psychological stress in the workplace (Kinnunen-Amoroso & Liira, 2016). Academics are no exception, with research confirming that they experience high to very high levels of occupational stress (Darabi, Macaskill, & Reidy, 2017; Malik, Björkqvist, & Österman, 2017). The rising levels of occupational stress could be ascribed to the substantial growth in student numbers and higher education institutions, increased emphasis on research, adapting to ever-changing curricula and quality assurance measures, and keeping abreast with rapid technological advances (Catano et al., 2010; Martins & Ungerer, 2014; Slišković & Maslic Seršič, 2011). The consequences of occupational stress among academics include teaching below standard, conflict with students and seeking employment elsewhere (Barkhuizen & Rothmann, 2008; Stevenson & Harper, 2006). Darabi et al. (2017) warn that the stressors that academics experience will continue to increase unless they adopt strategies and mechanisms to cope with these demands.

Coping is a continuous, goal-directed process in which individuals consciously and unconsciously engage to adjust their cognitive and behavioural efforts to maintain personal control during specific stressful situations. The coping effort is thus central to the psychological discipline in that it allows individuals to: (1) regulate distressing emotions (Pascual, Conejero & Etxebarria, 2016), (2) do something about situations that are causing psychological distress (Folkman & Lazarus, 1985) and (3) maintain their physiological and psychological health and well-being (Aldao, Nolen-Hoeksema & Schweizer, 2010). Researchers have therefore proposed several coping strategies for individuals to adopt in response to environmental demands. However, in an analysis of research on coping, Skinner, Edge, Altman and Sherwood (2003) identified more than 400 types of coping strategies, which indicate that there is no consensus among researchers on the best way to conceptualise the coping effort and categorise coping strategies. In addition, there is no clear consensus on how the coping effort should be measured, as existing coping questionnaires do not measure all the domains that are relevant to the coping process (Dewe, O’Driscoll, & Cooper, 2010). The existing literature further outlines various conceptual and methodological concerns regarding the measurement of coping (Folkman, 2010; Stemmet, 2013). Lastly, because academia is a highly stressful occupation, there is a need for academics to identify occupational stressors in their institutions and develop solutions to eliminate them. There is thus a need to develop a measuring instrument that could holistically measure coping with occupational stress in South African higher education institutions. This study aimed at developing a measuring instrument that could: (1) acknowledge the entire coping process, from when an occupational stressor is perceived until a coping response is adopted (2) consolidate the major types of coping strategies into one measuring instrument and (3) holistically measure coping with occupational stress in South African higher education institutions.

The purpose of this research was to construct a valid and reliable measurement instrument for measuring coping with occupational stress in academia.

It was anticipated that the results of this study would not only lead to the development of a more holistic or consolidated coping measurement instrument, but also provide insights into the coping strategies that academics adopt in response to occupational stressors. The results obtained would further allow industrial and organisational psychologists in higher education institutions to design and implement wellness interventions that should not only promote the health and well-being of academics but also that of the institution.

The concept of stress has been a source of immense interest over the past few decades and has gradually evolved to the seminal work of Lazarus and Folkman (1984) in the 20th century. These researchers noted that stress is process oriented and transactional, encompassing appraisals, coping and emotions. From this perspective, stress is defined as the relationship between the person and the environment that is appraised as taxing or exceeding their coping resources (Lazarus & Folkman, 1984, p. 19). Occupational stress results from individuals’ inability to cope with pressures of the job because of a poor fit between their abilities and work requirements (Beheshtifar & Nazarian, 2013). The perception of stress increases until the individual has made a conscious decision to cope with the stressor. Consequently, coping is defined as conscious efforts that individuals adopt to manage specific internal and/or external demands that are appraised as taxing or exceeding their coping resources (Lazarus & Folkman, 1984). Similarly, Skinner and Wellborn (1994, p. 112) and Compas et al. (2014, p. 72) conceptualise coping as ‘regulation under stress’ and defined it as how individuals regulate their behaviour, cognition, emotion and physiology under conditions of psychological stress. Coping efforts therefore fall under the broad definition of self-regulation, as individuals are continuously adjusting their thoughts and behaviours, and regulating their emotions to change their perceptions of the stressor.

Coping is an important regulatory process, but the literature reveals that there is no clear consensus about the conceptualisation of the coping construct and the categorisation of the numerous coping strategies (Allen & Leary, 2010; Folkman, 2010). The coping concept is also not adequately represented by a single measure, and numerous inventories have been proposed to assess coping (Knight & Sayegh, 2010). Consequently, for the purposes of this study, a number of coping questionnaires were reviewed (see Table 1). The researcher’s intention was not only to outline each questionnaire’s basic composition, but also to analyse its psychometric properties and the critique it received from other researchers.

Most coping instruments ask participants to recall how they have reacted or responded to a stressful situation. These instruments therefore focus on past events or situations. The Proactive Coping Inventory (PCI) is the only future-oriented coping instrument (Greenglass et al., 1999). Coping instruments further measure coping styles, resources, responses and behaviours.

The most widely used dimensions of coping are problem- versus emotion-focused coping (Lazarus & Folkman, 1984). Alternative categorisations include engagement versus disengagement coping (Carver & Connor-Smith, 2010), adaptive versus maladaptive coping (Aldao et al., 2010) and avoidance coping (Carver et al., 1989). The identified strategies range from 2 (e.g. emotional processing and emotional expression in the Emotional Approach Coping Scale [EACS]) to 20 in the Adult Coping Scale (Frydenberg & Lewis, 2011). Coping strategies generally include four basic types: problem-solving or active planning, emotion-oriented coping, social support coping and avoidance coping.

Most coping instruments were deductively developed and administered to undergraduate students. A number of conceptual and methodological concerns were identified (Folkman, 2010; Stemmet, 2013). These included:

From the discussion above, it is evident that the continued refinement of existing coping instruments is required.

A thorough literature review was conducted, in which: (1) the constructs under investigation were conceptualised, (2) the dimensions and subdimensions of coping were reviewed, (3) existing measuring instruments were analysed (see Table 1) and (4) literature trends in occupational stress and coping among academics were considered for developing a conceptual framework for coping with occupational stress among academics. This framework allowed the researcher to generate questions and items for the intended measuring instrument. The conceptual framework is briefly discussed in the remainder of this section.

In this study, an instrument for measuring how academics cope with occupational stress was developed by following a combination of steps (see Figure 1) proposed by various scale development authors (Barry, Chaney, Stellefson & Chaney, 2011; DeVellis, 2017; Netemeyer Bearden, & Sharma, 2003; Schmiedel, Vom Brocke, & Recker, 2014; Worthington & Whittaker, 2006). To achieve this objective, a non-experimental, cross-sectional, quantitative survey design was used.

Data were obtained from a non-probability convenience sample of permanently employed academics in a higher education institution in the Gauteng province of South Africa (N = 4016). The sample of participants (n = 305) consisted of female (64.3%) and male (35.7%) employees who were predominantly between the ages of 40 and 55 years (44.3%). The majority of the sample (81.6%) had a postgraduate qualification, with 34.4% having a doctoral degree, 35.7% a master’s degree, 11.5% an honours degree and 6.6% a bachelor’s degree. Only a small proportion (11.8%) of participants had a diploma, higher certificate or matric certificate. The distribution of the sample further showed that 34.1% of the sample were employed as academic support staff and 65.9% as academics. The academic component comprised 16 (5.2%) junior lecturers, 74 (24.3%) lecturers, 65 (21.3%) senior lecturers, 21 (6.9%) associate professors and 25 (8.2%) professors. Lastly, the sample consisted of participants who had predominantly been employed for 10 years or more (62.0%).

Data were collected by means of a self-administered online questionnaire, which was uploaded onto an online survey application. The URL link to the questionnaire and instructions were provided to the participants through an electronic mail. The link redirected the respondents to the online platform, where their responses were captured. Informed consent was obtained and participation in the survey was voluntary.

IBM SPSS Statistics version 24 software was utilised to perform the statistical analysis.

The reliability of the instrument was assessed by calculating the Cronbach’s alpha coefficient. DeVellis (2017) suggests that a Cronbach’s alpha coefficient of 0.70 should serve as an absolute minimum for a newly designed instrument. Dimensionality and reliability are important aspects of an instrument’s psychometric properties and quality, but without validation any inferences made from the instrument are meaningless, inappropriate and of limited usefulness (Furr, 2011). In this study, the content validity and construct validity of the coping items were assessed.

Content validity: The content validity was assessed in the first phase (step 2) of the instrument development process by means of an expert review and cognitive interviews (Figure 1).

Construct validity: Evidence of construct validity was provided by conducting EFA and CFA, and assessing the convergent and discriminant validity of the instrument. An instrument demonstrates convergent and discriminant validity when it correlates highly with other variables with which it should theoretically correlate, and correlates minimally with variables from which it should differ (Foxcroft & Roodt, 2013). For the purpose of this study, the correlation coefficient was used to estimate the degree to which any two measures were related to each other. Hair et al. (2010) further advocate the use of composite reliability (CR) (>0.70), average variance extracted (AVE) (>0.50), maximum shared squared variance (MSV) (<AVE) and average shared squared variance (ASV) (<AVE) to measure convergent and discriminant validity.

The original model revealed a mediocre to poor fit, indicating that there was a significant discrepancy between the correlations proposed and the correlations observed. The goodness-of-fit (GFI) obtained was 0.78 (prescribed threshold > 0.90) and the adjusted goodness-of-fit (AGFI) was 0.75 (prescribed threshold > 0.90). The normed fit index (NFI), relative fit index (RFI), comparative fit index (CFI) and Tucker Lewis index (TLI) were 0.79, 0.77, 0.87 and 0.85, respectively, compared to the recommended level of above 0.90 (Baumgartner & Homburg, 1996). The root mean square error of approximation (RMSEA) was 0.07, which was above the recommended limit of 0.06 (Hu & Bentler, 1999), and the root mean square residual (RMR) was above the recommended threshold of 0.02 at 0.16 (Hu & Bentler, 1999). The theorised model therefore did not fit well with the observed data.

The modification indices and standardised residual covariance were assessed to remedy the discrepancies between the proposed and estimated models. Residuals greater than 2.5 suggested an unacceptable degree of error and resulted in the deletion of items (Field, 2013). Nine additional items with residuals equal to or greater than 2.5 were removed to account for the correlations between variables in the data set.

The Santorra–Bentler chi-square statistic of 820.75, based on 459 degrees of freedom (p = 0.00), was significant and revealed an acceptable model fit. The CMIN/DF ratio was 1.79 (CMIN/DF ≤ 5.0), which indicates a good model fit (Garson, 2002). According to these guidelines, the revised model appeared to fit the data well. Bentler (2007), however, advises that the CMIN value should be used with caution and other fit indices, such as the CFI, RMSEA and standardised root mean square residual (SRMR), should be used to assess the model’s fit.

The GFI obtained was 0.87, and the AGFI was 0.84. The NFI, RFI, CFI and TLI were 0.87, 0.85, 0.94 and 0.93, respectively, compared to the recommended level of above 0.90. Both the RMSEA and SRMR values were 0.05, which were in accordance with the guidelines established by Hair et al. (2010), in that RMSEA values between 0.05 and 0.08 and SRMR values of less than 0.50 are indicative of an acceptable model fit. The CMIN/DF value, CFI, RMSEA and SRMR values, therefore, met the minimum requirements for model fit. The revised model for the nine dimensions is shown in Figure 4.

The dimensions were retained because the reliability (CR) for all the dimensions was above the recommended threshold of 0.70 (RUM α = 0.79; SOC α = 0.91; REL α = 0.92; COG α = 0.87; ACT LEI α = 0.88; VAC TIME α = 0.86; AVOID α = 0.78; SOC DIS α = 0.85; EMO α = 0.72) (Hair et al., 2010). The convergent reliability (AVE) for the dimensions was above the recommended threshold of 0.50 prescribed by Hair et al. (2010) and was thus retained. The discriminant validity (MSV and ASV) for all the dimensions fell within the recommended threshold, where MSV < AVE and ASV < AVE (Hair et al., 2010). Considering the goodness-of-fit results and reliability and validity results, the revised model (Figure 4) was accepted. Table 4 outlines the standard regression weights between the nine coping strategies and individual items, as well as the correlations between the coping strategies.

This study could prove useful for industrial and organisational psychologists because a valid and reliable measuring instrument was developed for determining which coping strategies academics adopt to regulate heightened emotions in response to an occupational stressor. The instrument could thus be used as a diagnostic tool for determining how academics respond to occupational stressors. The results obtained from administering the instrument should further allow industrial and organisational psychologists to gain an in-depth understanding of: (1) the occupational stressors that academics perceive as taxing and/or exceeding their coping resources, (2) the emotions elicited when a workplace stressor is perceived as stressful and (3) the coping strategies academics adopt to modulate the felt emotion. If industrial and organisational psychologists are able to appreciate the consequences of occupational stress and comprehend the complexities of the coping process, then they will be able to design and implement wellness practices that should not only promote the health and well-being of the academics, but also that of the institution.

No study of this nature is without limitations, especially because the coping process is a complex phenomenon. The following limitations were encountered in this study: firstly, the sources consulted were mostly of international origin, and there is little agreement among researchers about the best way to conceptualise these concepts. Secondly, the literature on the categorisation and measurement of coping strategies is limited, obsolete and incongruent. Thirdly, although the sample size was adequate to conduct the statistical analysis, further research needs to be conducted among a broader spectrum of participants. Lastly, a non-probability convenience sample was selected.

Despite having acceptable psychometric properties, continued refinement of the instrument is suggested. Modifications to the questionnaire could include the following: firstly, although a deductive approach was followed to generate items, the researcher recommends that both deductive and inductive approaches should be considered to increase the instrument’s content validity further (Hinkin, 1995). Secondly, while granting that the final instrument obtained provides adequate support for reliability and validity, the dimensions with three or fewer items should be revised and new items should be considered for inclusion. Thirdly, the emotional coping items should be reviewed because only one subdimension with two items (emotional expression) survived the stages of scale development. Lastly, future researchers should consider the inclusion of distraction as a subdimension of coping with occupational stress among academics. Distraction, which could be categorised as an adaptive coping strategy, is often used as a technique to divert oneself from an emotion-eliciting stimulus (Lehto, Park, Fu, & Lee, 2014).