Lost in translation: exploring the link between ... - Wiley Online Library

Lost in translation: exploring the link between HRM and performance in healthcare Timothy Bartram, Pauline Stanton and Sandra Leggat, La Trobe University, Australia Gian Casimir, University of Newcastle, Australia Benjamin Fraser, La Trobe University, Australia Human Resource Management Journal, Vol 17, no 1, 2007, pages 21–41

Using data collected in 2004 from 132 Victorian (Australia) public healthcare providers, comprising metropolitan and regional hospital networks, rural hospitals and community health centres, we investigated the perceptions of HRM from the experiences of chief executive officers, HR directors and other senior managers. We found some evidence that managers in healthcare organisations reported different perceptions of strategic HRM and a limited focus on collection and linking of HR performance data with organisational performance management processes. Using multiple moderator regression and multivariate analysis of variance, significant differences were found in perceptions of strategic HRM and HR priorities between chief executive officers, HR directors and other senior managers in the large organisations. This suggested that the strategic human management paradigm is ‘lost in translation’, particularly in large organisations, and consequently opportunities to understand and develop the link between people management practices and improved organisational outcomes may be missed. There is some support for the relationship between strategic HRM and improved organisational outcomes. Implications of these findings are drawn for managerial practice. Contact: Timothy Bartram, School of Business, Faculty of Law and Management, La Trobe University, Victoria 3086, Australia. Email: [email protected]

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nternationally there is a growing body of research that explores the critical role of strategic HRM in improving organisational outcomes with some evidence of a measurable and positive impact on organisational performance (Delaney and Huselid, 1996; Huselid et al., 1997; Godard, 2004). While much of this research has been carried out in manufacturing where performance in terms of output or share price is perhaps easy to measure, more recently attention has been given to the healthcare sector, where studies from the US and the UK have demonstrated some links between people management practices and improved organisational outcomes (Aiken et al., 2000; West et al., 2002). In this article we report on evidence from public healthcare organisations in Victoria, Australia. Using survey data collected in 2004 from 132 Victorian public health facilities, we investigated the adoption of strategic HRM from the reported experiences of chief executive officers (CEOs), HR directors (HRDs) and other senior managers (SMs). In this article, first, we investigate links between HRM and HUMAN RESOURCE MANAGEMENT JOURNAL, VOL 17 NO 1, 2007 © 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd., 9600 Garsington Road, Oxford, OX4 2DQ, UK and 350 Main St, Malden, MA, 02148, USA.

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Lost in translation: exploring the link between HRM and performance in healthcare

performance in healthcare settings and the extent to which healthcare organisations are monitoring HRM. Second, we explore differences and similarities in perspectives of strategic HRM from the respondent groups. Finally, we provide insights into some of the factors associated with these similarities and differences. STRATEGIC HRM AND ORGANISATIONAL PERFORMANCE Strategic HR theory suggests that the HR functions should consistently influence employee and management behaviour so as to enable and achieve the strategic plans of the organisation (Boxall and Purcell, 2003). Recent research has identified a link between strategic HR practices and organisational outcomes suggesting the importance of a set of internally consistent policies and practices that ensure a firm’s human capital contributes to the achievement of its business objectives – through compensation systems, team-based job designs, flexible workforces, quality improvement practices and employee empowerment (Lado and Wilson, 1994; Huselid et al., 1997). In theory, a labour-intensive, highly motivated, highly skilled professional workforce, as in the healthcare sector, should be an ideal context for the successful implementation of strategic HR practices. Despite empirical studies identifying the difficulties of practising HRM in the largely government funded health sector (Bach, 2000; Stanton et al., 2004), recent writers have highlighted the need for better people management practices in healthcare that directly support other goals such as providing a quality and safe service and hence improving healthcare performance and patient outcome (Stanton, 2002; Leggat and Dwyer, 2005). MEASUREMENT AND MONITORING HRM A central tenet of high performance organisations is the measurement of the impact of HR practices and policies on organisational performance (Godard, 2004). A major problem in the healthcare sector is the contentious nature of the measurement of performance, with international studies attempting to link people management practices to patient mortality in acute hospitals. Studies of the ‘magnet’ hospitals in the US focused on those hospitals that attracted and retained good nurses through their people management practices. These studies explored the relationship between good nursing care and mortality rates arguing that ‘magnet’ hospitals had lower patient mortality rates (Aiken et al., 2000; Upenieks, 2003). Kramer and Schmalenberg (2004) suggested that nurses employed at magnet hospitals experienced higher levels of empowerment and job satisfaction due to greater accessibility of magnet nurse leaders, better support of clinical nurse autonomous decision-making by these leaders and greater access to work empowerment structures (e.g. information and resources). West et al. (2002) also focused on patient mortality in the UK but included a range of people management practices including appraisal, teamwork and training, suggesting a link between these specific practices and lower patient mortality. However, there are limitations with these studies: first, direct causal links between specific HR practices and patient outcome are difficult to prove due to the presence of so many other potential variables, and second, patient mortality alone is an unreliable measure of performance. With increased consumer expectations of better 22

HUMAN RESOURCE MANAGEMENT JOURNAL, VOL 17 NO 1, 2007 © 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd.

Timothy Bartram, Pauline Stanton, Sandra Leggat, Gian Casimir and Benjamin Fraser

treatment, coupled with the pressure from government funders to ensure high quality safe healthcare with financial sustainability, healthcare organisations are increasingly required to identify more extensive performance measures. The strong positive link between monitoring performance and improving organisational performance has been well documented (Scanlon et al., 2001; Julian, 2002). Performance information is important to improve organisational effectiveness, ensure accountability, monitor management and foster collaboration within the sector (Leggat et al., 1998). The experience of health service organisations in monitoring performance has suggested the need to consider a range of performance indicators including financial indicators; service indicators that focus on satisfaction with service delivery; and clinical indicators that evaluate the processes of care and/or the resulting patient outcomes (Ballard, 2003). However, in this debate the measurement of HR outcomes and the link with organisational performance is hardly recognised (Leggat et al., 2005). ORGANISATIONAL DECISION-MAKERS AND STRATEGIC HRM Organisations are multi-level systems (Kozlowski and Klein, 2000) and understanding the linking mechanisms through which the organisation, groups and individuals interact is essential if the components of organisational performance and the links with HRM are to be identified. The HR–performance relationship and the systems and strategic perspectives ‘help stage’ how HR practices and their influences on employee attributes can lead to desired outcomes (Bowen and Ostroff, 2004: 204) and assume implicit, multi-level relationships among HR practices, individual employee attributes and organisational performance (Huselid, 1995). The HR system and processes can send signals to employees that allow them to understand the desired and appropriate individual and collective responses (Bowen and Ostroff, 2004). Bowen and Ostroff (2004) argue that if the HR system is perceived as high in distinctiveness, consistency and consensus it will create a ‘strong situation’ and consistent employee behaviour and thereby improve organisational performance. They suggest that distinctiveness of the HR system is dependent on four characteristics of HRM: visibility, understandability, legitimacy of authority and relevance. Consistency of HRM is dependent on instrumentality, validity and consistent HR messages. Consensus is dependent on agreement among principal HR decision-makers and fairness of HR practices (see Bowen and Ostroff, 2004, for a more in-depth discussion of these concepts). The authors propose that a ‘strong HRM system can enhance organisational performance owing to shared meanings in promotion of collective responses that are consistent with the organisational strategic goals’ (Bowen and Ostroff, 2004: 213). If HR systems are weak, HR practices will send messages that are ambiguous and subject to individual interpretation leading to inconsistency of practice that can impact on employee performance. The role of key organisational decision-makers such as the senior management team in developing consensus and agreement among principal HR messages is critical to developing a strong HR system and subsequent organisational performance (Fiske and Taylor, 1991; Klein et al., 2001). Moreover, agreement among message senders helps to promote consensus among line managers and employees (Fiske and Taylor, 1991). The strategic HR paradigm indicates that the senior HUMAN RESOURCE MANAGEMENT JOURNAL, VOL 17 NO 1, 2007 © 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd.

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management team sets the strategic goals and designs the system of HR practices for achieving those goals (Schuler and Jackson, 1987; Godard, 2004). When individual message senders are in strong agreement among themselves concerning the message, they are more likely to form consensus of the HR message (Bowen and Ostroff, 2004). Confirmation of the factors related to within-group agreement of senior management teams is an evolving area of research. According to Lado and Wilson (1994) integration and close interactions among HR managers, SMs and the CEO foster the exchange of tacit knowledge that enables the formulation and implementation of HR practices that ensure the achievement of the strategic goals of the organisation. Klein et al. (2001) hypothesised that the greater dispersion of within-group demographic variables such as age, education, pay, tenure and gender, the greater the variability in group members’ perception of the work environment. Moreover, studies have increasingly documented that within-group dispersion of demographic variables, skills, attitudes, perceptions or values is positively related to group creativity, but negatively related to social integration within the group, cohesion, speed of decision-making and ease of decision implementation (Hambrick, 1994; Bliese and Halverson, 1998). A large body of organisational theory and research suggests that interaction among members of a group fosters similarity among group members’ beliefs and perceptions (Klein et al., 2001). Hambrick (1994) argued that high behavioural integration among the members of a senior management team enhances perceptual agreement. Rentsch (1990) using network analysis found that people involved in the same interaction groups attached similar meanings to organisational practices, whereas people attached to different groups attached different meanings to these events. In line with this, it can be argued that greater social interaction between the management team in smaller organisations relative to larger organisations results in greater local ownership and participation (Wilkin et al., 2003). This can be explained by size causing structural complexity (horizontal structural differentiation multiplied by vertical structural differentiation) and thereby creating more bureaucratic organisational layers (Donaldson, 2001). Further compacting this relationship are the findings that small organisations generally have fewer formalised HR practices than their larger counterparts (Guest and Conway, 1999; Bartram, 2005). Hence, in this study, we pose four key questions. First, is there a link between HRM and performance in the healthcare settings? Second, to what extent are Australian healthcare organisations monitoring HRM and linking it to organisational performance? Third, what are the differences and similarities in perspectives of strategic HRM from the respondent groups? Finally, what are some of the factors associated with these similarities and differences? METHODOLOGY Between December 2003 and April 2004, we conducted a survey of 132 public healthcare facilities in Victoria, Australia, including metropolitan health services which are large city-based hospital networks, large regional base hospitals, smaller district and rural hospitals and community health services. Five hundred and thirty-six questionnaires were distributed to the CEO, HRD and two other SMs in 24



each organisation. A total of 184 questionnaires (34 per cent response rate overall) were returned, including 64 from CEOs, 35 from HRDs and 85 from SMs (almost 50 per cent response from CEOs and an estimated 90 per cent response rate from HRDs as all organisations do not have a designated HRD). The HRDs’ questionnaire contained a series of questions relating to HR outcomes. After considerable consultation with CEOs and HRDs from the industry the researchers were informed that only HRDs would keep HR outcome information. We only received 35 completed questionnaires related to HR outcomes. Therefore, the exploratory correlation analysis is undertaken with a sample size of 35 organisations. The performance monitoring analysis was undertaken with a sample of 64 CEOs. Participant hospitals, CEO and HRD names and contact details were provided by the Victorian Hospitals’ Industrial Association (VHIA, employer association) from their membership database. Three questionnaires were mailed to the CEO, one for the CEO to complete and the other two questionnaires to be passed on to SMs. This method of recruitment was used as the VHIA does not keep names and contact details of SMs (e.g. directors of nursing, team leaders, assistant directors of nursing or chief financial officers). One questionnaire was mailed to the HRDs in the sample. All of the questionnaires were returned by mail and were coded using SPSS. Measures The questionnaires were developed through collaboration between academics and HRDs. All Cronbach alpha estimates of internal reliability exceeded the 0.7 level recommended by Nunnally (1978). Strategic HRM The strategic HRM index, a 13-item modified measure (Huselid, 1995), was used to assess the extent to which management strategically integrated HR strategic planning (see Appendix 1). Example items included ‘human resource strategies are effectively integrated with this organisation’s strategy’ and ‘human resource practices are integrated to be consistent with each other’, rated on a five-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). As a result of a principal components analysis 10 items were summed to form an index of strategic HRM for the investigation of HRM and its impact on HR outcomes (mean = 34.82, SD = 5.69, alpha = 0.82). See below for a discussion of the principal components analysis. HR priorities The 13-item measure was adapted from West et al. (2002) (see Appendix 2). Respondents were asked ‘The human resource management strategy of this organisation places a very high priority on’ 13 different facets of hospital operation, such as ‘reducing labour costs’ and ‘effective healthcare teams’. As a result of a principal components analysis 11 items were summed to form an index of HR priorities (mean = 42.76, SD = 5.89, alpha = 0.87). See below for discussion of the principal components analysis. HR functions These measures were adapted from the EQUIP Guide: A Framework to Improve Quality and Safety of Health Care, developed by the Australian Council on Health Care Standards (ACHS). ACHS is an independent, not-for-profit organisation dedicated to improving the quality and safety of healthcare in Australia through HUMAN RESOURCE MANAGEMENT JOURNAL, VOL 17 NO 1, 2007 © 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd.

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continual review of performance, assessment and accreditation (ACHS, 2003). The Council is responsible for assigning accreditation status to healthcare providers and achievement of this accreditation is a requirement of the Victorian government. All HR functional areas were rated on a five-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). HR planning comprised a five-item measure. An example item includes ‘the workforce plan is clearly linked to the organisation’s strategic direction’. The items were summed to form a composite measure (mean = 16.02, SD = 3.46, alpha = 0.80). Recruitment and selection comprised an eight-item measure. An example item includes ‘Recruitment, selection, appointment, credentialing and continuing employment processes are transparent and equitable’. The items were summed to form a composite measure (mean = 34.48, SD = 3.29, alpha = 0.79). Training and development was a 12-item measure. An example item includes ‘Staff contribute to the planning of the learning and development system’. The items were summed to form a composite measure (mean = 43.60, SD = 7.17, alpha = 0.90). Performance management used a 17-item measure. An example item is ‘The performance management criteria reflect the strategic goals of the organisation’. The items were summed to form a composite measure (mean = 67.09, SD = 7.42, alpha = 0.86). Equal employment opportunity (EEO) was also a 17-item measure, with ’Information sessions are carried out to explain EEO policy’ an example. The items were summed to form a composite measure (mean = 58.81, SD = 12.30, alpha = 0.91). The employee participation measure comprised a measure of nine items. An example item is ‘This organisation has a formal joint consultation committee of management and employee representatives’. The items were summed to form a composite measure (mean = 34.45, SD = 4.81, alpha = 0.78). HR outcomes These measures were also adapted from the EQUIP Guide (ACHS, 2003). While there is no requirement that an organisation must monitor specific indicators or quantitative measures, the accreditation guidelines place a strong emphasis on monitoring and benchmarking performance. In the development of measurement of HR outcomes, organisations need to consider indicators and activities that relate to the health services they provide and for the size and type of the organisation. In developing these questions the researchers undertook extensive discussions with HRDs and CEOs within the industry, and the final list of HR outcome questions (see Appendix 3) was agreed upon by the researchers and SMs to be the most valuable and widely collected outcome data. The HRDs were asked to provide organisational data (last financial year 2002–2003) for HR outcomes for their organisations. The outcomes were recorded in absolute numbers for each organisation. Organisation size was controlled for by taking a ratio of absolute outcome and the number of equivalent full-time staff for each organisation. HR outcomes included number of staff leaving voluntarily; number of reported complaints pertaining to lack of training and development opportunities; number of grievances lodged; number of complaints due to organisational change; number of disciplinary actions; number of stress-related leave episodes; number of incident reports lodged; number of hours lost through injury; and the number of WorkCover (sickness and accidents insurance) complaints exceeding 20 days. 26



Organisational size, tenure of the manager and time in industry of the manager All of these variables were treated as continuous variables. Gender Gender was coded as a dichotomous variable. Organisational performance monitoring A combination of open-ended and structured questions were used to explore how the CEOs monitored performance, including HR outcomes, within their organisation, as well as how they judged the performance of peer organisations. In the structured questions the researchers asked about the use of indicators that would most commonly monitor the financial (such as financial results and service volumes), service (such as waiting lists and staff and patient satisfaction ratings) and clinical (such as adverse events, clinical outcomes, functional status) perspectives. PROCEDURE AND RESULTS Strategic HRM and its impact on HR outcomes First, given the limitations of the data, exploratory analyses using zero-order correlations were initially performed between the summed components of the strategic HRM index, the summed components of measures of HR functions and the HR outcomes listed previously. Theoretically, we would assume that organisational strategy (strategic HRM index) would be positively associated with the HR functions (i.e. organisational fit) (Huselid et al., 1997). Moreover, we would also assume that the HR functions would also be positively associated with one another (i.e. HR fit) (Schuler and Jackson, 1987). It can also be surmised that the HR functions would exhibit stronger associations with HR outcomes than the strategic HRM index, as the HR functions are the techniques that shape and influence employee behaviours (e.g. HR outcomes) (Schuler and Jackson, 1987). Strategic HRM index From Table 1 we see that the strategic HRM index was positively correlated with only one HR outcome – the number of incident reports lodged (r = 0.43, p < 0.05). This may be interpreted as improvements in systematic record keeping and a culture of reporting incidents. In relation to HR functions the strategic HRM index was positively correlated with performance management (r = 0.55, p < 0.01), HR planning (r = 0.78, p < 0.01), recruitment and selection (r = 0.33, p < 0.05) and HR development (r = 0.41, p < 0.05). HR functions The HR planning measure was negatively correlated with the number of complaints about training and development (r = -0.41, p < 0.05) and organisational change (r = -0.50, p < 0.01). The performance management measure was negatively correlated with the number of hours lost through injury (r = -0.46, p < 0.05). The HR development measure was negatively correlated with the number of staff leaving voluntarily (r = -0.51, p < 0.05), the number of hours lost through injury (r = -0.47, p < 0.05), the total number of WorkCover (accident and sickness insurance) claims HUMAN RESOURCE MANAGEMENT JOURNAL, VOL 17 NO 1, 2007 © 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd.

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28

Strategic HRM HR planning Recruitment Participation Training EEO Performance management No. of staff leaving voluntarily No. of complaints pertaining to lack of training No. of grievances lodged No. of complaints due to organisational change No. of disciplinary actions No. of stress-related leave episodes No. of incident reports lodged Hours lost due to injury Total number of WorkCover claims exceeding 20 days

* p < 0.1; ** p < 0.05; *** p < 0.01. n = 35.

15. 16.

14.

13.

12.

10. 11.

9.

1. 2. 3. 4. 5. 6. 7. 8.

Variable

0.78*** 0.33** 0.218 0.41** 0.21 0.55*** 0.01 0.46** 0.29 0.38** 0.11 0.55*** 0.29

-0.17 -0.12

0.43** 0.05 0.14

0.32

0.11

0.15

-0.17

0.15

0.02 -0.50***

-0.41*

3

0.04 0.04

-0.12

1

TABLE 1 Correlations for all variables 5

6

7

-0.39 -0.33

-0.03

0.44**

-0.28

0.13 0.12

-0.54** -0.34

-0.06

0.19

-0.45**

0.14 0.13

0.04

0.03

0.20

-0.29

0.09 0.31

-0.25

-0.04

8

-0.47** -0.68*** -0.46** -0.68*** -0.45 -0.44

-0.12

0.04

-0.04 0.16

-0.39*

0.02 -0.22

-0.06

-0.44**

0.05 -0.01

-0.30

0.25 0.40** 0.34** 0.53*** 0.22 0.53*** 0.48*** 0.47*** 0.49*** 0.38** 0.02 -0.35 -0.51** -0.33 -0.21

4

0.04

11

0.06

13

0.01 -0.13

0.34

12

-0.24

14

15

16

0.99*** -0.18 -0.04 0.59* 0.57* 0.06 0.95***

-0.12 -0.19 -0.09 -0.01

0.01

-0.05 -0.09

0.27 0.17

10

0.84*** -0.07 -0.16 0.10 0.65** -0.17 0.26 -0.14

-0.15

0.59**

0.54**

-0.16 -0.05

-0.08

9


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© 2007 The Authors.


exceeding 20 days (r = -0.68, p < 0.01) and the number of disciplinary actions (r = -0.39, p < 0.1). The EEO measure was negatively correlated with the number of disciplinary actions (r = -0.45, p < 0.05) and the number of hours lost through injury (r = -0.68, p < 0.01). The recruitment and selection measure was positively correlated with the number of stress-related leave episodes (r = 0.44, p < 0.05). The employee participation measure was negatively associated with the number of disciplinary actions (r = -0.44, p < 0.05) and hours lost due to injury (r = -0.54, p < 0.05). Overall, the findings show that the HR functions were associated with various positive HR outcomes. Organisational performance: use of performance indicators The respondents completed a table that outlined possible performance indicators and identified how these indicators were used in monitoring performance in the organisation. The use options included: • • • • • •

discussion regularly at senior/executive management meetings report to the Board report to funding agencies publicly available community report benchmark with other organisations visible link to staff performance management.

It should also be noted that the CEOs were given the opportunity to mention other indicators that they used to track their organisation’s (or service’s/department’s) operational performance (e.g. any HR indicators they monitored). HR indicators were scarcely mentioned. For example, 3 per cent reported the use of performance appraisal. This may indicate that many of the respondents may not use HR indicators. Table 2 presents the indicators and how they are used as reported by the CEOs. Overall, the CEOs reported a strong focus on financial, volume and patient/client satisfaction indicators and indicators related to accreditation. Receiving much less attention were indicators related to the outcomes of healthcare processes, such as adverse events, clinical outcomes, functional status of patients/clients and community reintegration of patients/clients. As shown in Table 2, for the most part the performance indicators were discussed at senior/executive management meetings and were reported to the Board of Directors. There was substantial variation in the reporting to funding agencies, probably as a result of the varying reporting requirements of the government funder of most of these agencies. Despite the focus in the accreditation process on benchmarking, less than half of the CEO respondents reported that they benchmarked their performance on any of these indicators with other organisations. With regard to strategic HRM few organisations linked staff performance management processes with these performance indicators. Notably, despite the importance of achieving financial and volume targets in the Victorian system, less than half of the CEO respondents indicated that they linked these indicators with the internal performance management processes.


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30

Financial results Approved volumes Actual volumes Waiting lists Staff satisfaction Med staff satisfaction Patient satisfaction Accreditation Adverse events Clinical outcomes Functional status Community integration Care integration Total/Average %

Type of indicator

60 59 59 44 51 25 59 62 54 45 25 23 41 607

# 93.8 92.2 92.2 68.8 79.7 39.1 92.2 96.9 84.4 70.3 39.1 35.9 64.1 73.0

%

Senior management

100 90.6 90.6 56.3 67.2 34.4 85.9 100.0 78.1 56.3 20.3 25.0 39.1 64.9

%

Board report

64 58 58 36 43 22 55 64 50 36 13 16 25 540

#

TABLE 2 Reported use of performance indicators

62 59 56 34 6 1 28 53 38 20 9 10 16 392

# 96.9 92.2 87.5 53.1 9.4 1.6 43.8 82.8 59.4 31.3 14.1 15.6 25.0 47.1

%

Funder report

52 34 36 16 12 2 30 45 20 20 5 7 14 293

# 81.3 53.1 56.3 25.0 18.8 3.1 46.9 70.3 31.3 31.3 7.8 10.9 21.9 35.2

%

Community report

29 20 20 16 17 7 28 27 14 11 5 3 5 202

# 45.3 31.3 31.3 25.0 26.6 10.9 43.8 42.2 21.9 17.2 7.8 4.7 7.8 24.3

%

Benchmark

27 24 27 11 18 10 15 27 10 10 5 5 9 198

#

42.2 37.5 42.2 17.2 28.1 15.6 23.4 42.2 15.6 15.6 7.8 7.8 14.1 23.8

%

PM link

294 254 256 157 147 67 215 278 186 142 62 64 110 2232

#

76.6 66.2 66.7 40.9 38.8 17.5 56.0 72.4 48.5 37.0 16.2 16.7 28.7 44.7

Ave %

Total


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TABLE 3 Reported linking of indicators with staff performance management Type of indicator

Financial results Actual volume Waiting list Staff satisfaction Patient satisfaction Adverse events Clinical outcomes Functional status Community integration Care integration Total/Average %

MHS

Regional

District

CHS

#

%

#

%

#

%

#

%

7 5 5 5 3 3 2 0 1 2 33

87.5 62.5 62.5 62.5 37.5 37.5 25.0 0.0 12.5 25.0 41.3

3 2 1 2 5 1 1 1 1 1 18

42.9 28.6 14.3 28.6 71.4 14.3 14.3 14.3 14.3 14.3 25.7

10 3 4 7 21 5 7 4 3 3 67

35.7 10.7 14.3 25.0 100.0 17.9 25.0 14.3 10.7 10.7 26.4

7 7 1 4 20 1 0 0 0 3 43

33.3 52.8 4.8 19.1 71.4 4.8 0.0 0.0 0.0 14.3 20.0

Linking performance indicators to staff performance management As shown in Table 3, the CEOs were less likely to report that the performance measures had a visible link with the organisational staff performance management. On average just over 40 per cent of the metropolitan hospital CEOs reported linking of the identified performance indicators to the staff performance management processes, and fewer than 30 per cent of the CEOs of the other organisational categories reported linking. The metropolitan hospital CEOs reported linking the financial results to the greatest extent (88 per cent), with some linking of the service indicators but less emphasis on linking clinical indicators. In contrast, the regional (71 per cent) and district (100 per cent) hospitals and community health centres (71 per cent) reported the greatest focus on linking patient satisfaction measures, compared with only 38 per cent of the metropolitan hospital CEOs (Leggat et al., 2005). These data suggest that despite the large HR components of the budgets of these service providers, the public health sector CEOs do not have strategic HR mechanisms in place that enable them to link outcomes with performance expectations. Perceptions of strategic HRM Principal components analysis Strategic HRM To check the unidimensionality of the strategic HRM index (Huselid, 1995), a principal components analysis was conducted on the 13 items of the strategic HRM scale. This analysis revealed that three of the items (i.e. items 3, 4 and 5) did not load satisfactorily on the principal component. These three items were therefore removed and the results of the ensuing principal components analysis are presented in Table 4, which shows that the remaining 10 items loaded satisfactorily according to the 0.5 criterion of Hair et al. (1998). Furthermore, the internal HUMAN RESOURCE MANAGEMENT JOURNAL, VOL 17 NO 1, 2007 © 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd.

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TABLE 4 Principal component findings for the strategic HRM scale Component shrm1 shrm2 shrm6 shrm7 shrm8 shrm9 shrm10 shrm11 shrm12 shrm13

0.71 0.64 0.56 0.70 0.60 0.51 0.57 0.69 0.65 0.61

TABLE 5 Principal component findings for the HR priorities scale Component hrmp2 hrmp4 hrmp5 hrmp6 hrmp7 hrmp8 hrmp9 hrmp10 hrmp11 hrmp12 hrmp13

0.73 0.54 0.64 0.71 0.71 0.77 0.78 0.81 0.74 0.65 0.65

reliability coefficient of the 10-item strategic HRM scale was 0.82, which indicates that the 10-item scale has satisfactory internal reliability according to Nunnally’s (1978) 0.7 criterion. The scores for these 10 items were averaged to obtain an overall strategic HRM score. HR priorities To check the unidimensionality of the HR priorities scale (West et al., 2002), a principal components analysis was conducted on the 13 items of the scale. This analysis revealed that two of the items (i.e. items 1 and 4) did not load satisfactorily on the principal component. These two items were therefore removed and the results of the ensuing principal components analysis are presented in Table 5, which shows that the remaining 11 items loaded satisfactorily according to the 0.5 criterion of Hair et al. (1998). Furthermore, the internal reliability coefficient of the 11-item strategic HRM scale was 0.90, which indicates that the 11-item scale has satisfactory internal 32



TABLE 6 Means and standard deviations of strategic HRM and HR priorities by role

CEO HRD SM

Strategic HRM

HR priorities

3.63 (0.55) 3.46 (0.51) 3.38 (0.59)

3.95 (0.53) 4.00 (0.45) 3.79 (0.56)

reliability according to Nunnally’s (1978) 0.7 criterion. The scores for these 11 items were averaged to obtain an overall HR priorities score. One-way MANOVA It should be noted that it was not possible to match the respondents within the organisational units because of inadequate responses matching the three types of managers (e.g. CEO, HRD and SM) within organisations (only 26 complete matches). The analysis of the different managers is based on their perceptions of strategic HRM. This may not reflect organisational reality, but perception may guide dayto-day behaviour. A one-way multivariate analysis of variance (MANOVA) was conducted with managerial role (e.g. CEO, HRD and SM) as the independent variable and strategic HRM and HR priorities as the dependent variables. A one-way MANOVA is a dependence technique that measures the differences for two or more metric dependent variables based on a set of non-metric variables acting as independent variables (Hair et al., 1998). It should also be noted that SMs in this article were treated as a homogeneous group as there were no statistically significant differences between mean responses of SMs in relation to the strategic HRM and HR priorities scales. The assumption of homogeneity of variance was supported for both strategic HRM (F = 1.05, p > 0.05) and HR priorities (F = 0.80, p > 0.05) according to Levene’s test. Table 6 contains the means and standard deviations for strategic HRM and HR priorities for the three groups of respondents. The MANOVA revealed a significant multivariate effect that was consistent across several criteria: Pillai’s trace (0.07, p < 0.05), Wilks’s lambda (0.94, p < 0.05), Hotelling’s trace (0.07, p < 0.05) and Roy’s largest root (0.04, p < 0.05). Fisher’s least significant difference post hoc tests revealed that CEOs reported higher levels of strategic HRM than did managers whilst no other significant differences in strategic HRM were found. No significant differences in HR priorities were found between the three groups. Size ¥ managerial role interaction A hierarchical regression analysis was conducted using the product term to examine the moderating effects of managerial role on the relationship between size and strategic HRM. The size variable was standardised and the product term was created by multiplying the standardised size variable with managerial role. This procedure reduces problems of collinearity that are associated with the product term. This analysis revealed a significant interaction effect (b = -0.10, p < 0.05, DR2 = 0.02). HUMAN RESOURCE MANAGEMENT JOURNAL, VOL 17 NO 1, 2007 © 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd.

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To more closely examine the interaction effect, size was split into three groups (i.e. small, which comprised organisations with less than 146 employees, n = 61; medium, which comprised organisations with more than 146 employees but less than 430 employees, n = 65; and large, which comprised organisations with more than 430 employees, n = 58) using the 33rd and 67th percentiles as cut-off points (Cohen et al., 2003). A MANOVA was conducted for each of the three groups of size using role as the independent variable and strategic HRM and HR priorities as dependent variables (Hair et al., 1998). For the small and medium groups, the assumption of homogeneity of variance was supported for both strategic HRM and HR priorities according to Levene’s test. However, for the large group, Levene’s test indicated that although the assumption of homogeneity of variance was supported for strategic HRM it was not supported for HR priorities (F = 4.79, p < 0.05). The MANOVA revealed no significant differences in perceptions of strategic HRM and HR priorities between CEOs (n = 23), HRDs (n = 11) and SMs (n = 27) in the small group. Similarly, the MANOVA revealed no significant differences in perceptions of strategic HRM and HR priorities between CEOs (n = 22), HRDs (n = 9) and SMs (n = 34) in the medium group. In contrast, the MANOVA found a significant multivariate effect for the large group (i.e. Pillai’s trace 0.29, p < 0.01). Specifically, significant differences were found in perceptions of strategic HRM (F = 5.75, p < 0.05) and HR priorities (F = 4.37, p < 0.05) between CEOs (n = 18), HRDs (n = 16) and SMs (n = 24) in the large group. According to Fisher’s least significant difference post hoc test, which assumes homogeneity of variance, managers in large organisations reported lower levels of strategic HRM than did their CEOs and HRDs whilst there was a non-significant difference in strategic HRM between CEOs and HRDs. Furthermore, according to Tamhane’s post hoc test, which does not assume homogeneity of variance, managers in large organisations reported lower levels of HR priorities than did their HRDs whilst there was a non-significant difference in strategic HRM between CEOs and HRDs. Table 7 provides the means and standard deviations of strategic HRM and HR priorities for the CEOs, HRDs and SMs according to the size of the firm. Furthermore, Appendix 4 shows the means and frequencies for CEOs, HRDs and SMs in terms of time in industry of manager, tenure of manager and gender of manager. No statistically significant differences are found between CEOs, HRDs and SMs in terms of tenure or gender. Chief executive officers have longer time in the industry relative to the other managers. TABLE 7 Means and standard deviations for the managers according to size of the firm Small

Medium

Large

CEO

HRD

SM

CEO

HRD

SM

CEO

HRD

SM

Strategic HRM

3.8 (0.6)

3.5 (0.5)

3.6 (0.5)

3.5 (0.6)

3.5 (0.6)

3.5 (0.5)

3.6 (0.5)

3.4 (0.5)

3.0 (0.7)

HR priorities

4.2 (0.5)

4.1 (0.3)

4.0 (0.4)

3.9 (0.6)

3.9 (0.6)

3.9 (0.5)

3.8 (0.5)

4.0 (0.4)

3.5 (0.7)

34



TABLE 8 Stepwise regression Independent variables How long have you been in the industry (years)? Managerial role Gender R2 = 0.11

b 0.181** -0.195*** -0.158**

** p < 0.05; *** p < 0.01 N = 184 Dependent variable: strategic HRM

Other interaction effects A hierarchical regression analysis was also conducted using the product term to examine the moderating effects of managerial role on the relationship between managerial tenure and strategic HRM, time in industry of manager and strategic HRM, and gender of manager and strategic HRM. Each interaction effect was not significant (role ¥ tenure: b = 0.16, DR2 = 0.39; role ¥ time in industry: b = 0.08, DR2 = 0.72; role ¥ gender: b = 0.13, DR2 = 0.63). Stepwise regression An exploratory stepwise multiple linear regression analysis was conducted using role, size, tenure, gender and time in industry as independent variables to examine which of these were the best predictors of strategic HRM. A stepwise regression analysis retains only significant predictors of the dependent variable in the model (Cohen et al., 2003). As shown in Table 8, the final model contained only time in industry, role and gender. More specifically, time in industry was correlated positively with perceptions of strategic HRM, role was correlated negatively with strategic HRM indicating that CEOs reported higher levels of strategic HRM than did SMs, and gender was correlated negatively with strategic HRM indicating that females reported higher levels of strategic HRM than did males. A stepwise multiple linear regression analysis was conducted using role, size, tenure, gender and time in industry as independent variables to examine which of these were the best predictors of strategic HRM. As shown in Table 9, the final model contained only size, which was correlated negatively with perceptions of HR priorities indicating that perceived levels of HR priorities decreased as the size of the firm increased. DISCUSSION AND CONCLUSIONS This study investigated the perceptions, monitoring and impact of strategic HRM in the Victorian public healthcare sector. The findings are interesting and worthy of HUMAN RESOURCE MANAGEMENT JOURNAL, VOL 17 NO 1, 2007 © 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd.

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TABLE 9 Stepwise regression Independent variables

b

Organisational size R2 = 0.02

-0.154**

** p < 0.05; *** p < 0.01 N = 184 Dependent variable: HR priorities

further investigation. First, the study showed positive associations between the HR functions and HR outcomes suggesting that emphasis on sophisticated people management practices may be associated with cost-effective outcomes. Second, results of the organisational size ¥ managerial role interaction found that organisational size moderated the relationship between role and strategic HRM. Further analysis using MANOVA revealed that SMs in large organisations had lower strategic HRM and HR priorities scores relative to both CEOs and HRDs. It appears that, in the large hospitals (e.g. metropolitan hospitals), SMs may not share the same positive perceptions of the practice of strategic HRM and rate HR priorities lower in their organisations relative to the CEOs and HRDs. The findings indicate that organisational size is an important factor in explaining differences in perceptions of strategic HRM and HR priorities among the different groups of managers. These findings may be due to larger firms being more bureaucratic and having greater structural complexity, which may, in turn, create less social interaction and involvement of SMs in HR matters thereby resulting in different perceptions of strategic HRM and HR priorities (Donaldson, 2001; Wilkin et al., 2003). The stepwise regression analysis revealed other factors that predict strategic HRM and HR priorities. Time in industry was correlated positively with perceptions of strategic HRM, role was correlated negatively with strategic HRM indicating that CEOs reported higher levels of strategic HRM than did SMs, and gender was correlated negatively with strategic HRM indicating that females reported higher levels of strategic HRM than did males. Organisational size, which was correlated negatively with perceptions of HR priorities, indicated that perceived levels of HR priorities decreased as the size of the firm increased. These results again confirm the importance of organisational size and managerial role in explaining perceptions of strategic HRM and HR priorities. They also provided some additional information in that female CEOs with considerable experience in the industry have higher perceptions of strategic HRM independent of their tenure or the size of their organisations. Moreover, other organisational factors such as history, mandate and clarity of strategic goals may have an impact. Using moderator regression modelling we were able to discount some of the characteristics of the managers (such as gender and tenure) as having an impact on the differences among groups of managers. It is also possible that other characteristics that we were not able to test, such as clinical/ administrative background and training, may make a contribution to the observed 36



differences (Leggat and Dwyer, 2005). These findings are a concern as they suggest that the senior management team in large organisations may not be ‘singing the same song’ in relation to HR systems, practices and processes. This in turn can undermine the distinctiveness and the consistency that Bowen and Ostroff (2004) identify as key components in the HRM–performance link. Somehow even within the senior management team the key HR messages may be ‘lost in translation’ and there is some evidence to suggest that the larger the organisation the more likely the message is to be lost. Finally, the study also demonstrated limited performance monitoring throughout the Victorian public health sector. The sector reported a strong focus on financial, activity and patient satisfaction measures, with limited reporting on HR measures (Leggat et al., 2005). It is acknowledged that effective management requires appropriate performance measurement and the mechanisms to translate data into knowledgeable actions – neither of which were strongly demonstrated in this study. Despite the importance of HRM, this study found limited collecting and reporting of performance measures related to people management, suggesting that opportunities to improve people management practice in healthcare organisations may be missed. In conclusion, this investigation raises a number of interesting questions for further exploration at a case study level. The results of this study lend support to the potential benefits of strategic HRM. However, we see the varying perceptions of strategic HR-related roles among the three groups of managers in large organisations and limitations in monitoring HRM as barriers to the development of strategic HRM. We argue that, until the facilitating and enabling role of HRM is understood and express links are made between HRM and the performance management expectations of staff in relation to achieving patient outcomes, HRM will continue to be seen as an administrative function vulnerable to financial restraint in difficult times. Despite the evidence that positive practices with positive outcomes are taking place, much of this experience is being lost in translation within the organisational environment. These outcomes are not being captured, recognised and then translated into greater support for and understanding of the potential of strategic HRM.

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Scanlon, D.P., Darby, C., Rolph, E. and Doty, H.E. (2001). ‘Use of performance information for quality improvement’, Health Services Research, 36: 3, 619–641. Schuler, R.S. and Jackson, S.E. (1987). ‘Linking competitive strategies with human resource management practices’, Academy of Management Executive, 1: 3, 207–219. Stanton, P. (2002). ‘Managing the healthcare workforce: cost reduction or innovation’, Australian Health Review, 25: 4, 92–98. Stanton, P., Bartram, T. and Harbridge, R. (2004). ‘HRM practices in the public health sector: lessons from Victoria, Australia’, Journal of European Industrial Training, 28: 2/3/4, 310–328. Upenieks, V.V. (2003). ‘What’s the attraction to magnet hospitals?’, Nursing Management, 34: 2, 43–45. West, M., Borrill, C., Dawson, J., Scully, J., Carter, M., Anelay, S., Patterson, M. and Waring, J. (2002). ‘The link between the management of employees and patient mortality in acute hospitals’, International Journal of Human Resource Management, 13: 8, 1299–1310. Wilkin, D., Bojke, C., Coleman, A. and Gravelle, H. (2003). ‘The relationship between size and performance of primary care organisations in England’, Journal of Service Resource Policy, 8: 1, 11–17.

APPENDIX 1: STRATEGIC HRM INDEX Variable

Definition

SHRM SHRM SHRM SHRM SHRM

HR strategies are effectively integrated with this organisation’s strategy HR practices are integrated to be consistent with each other HR personnel are a key influence in setting HR strategy HR strategy is distinct from the business strategy HR strategy will become a more important influence in this organisation’s strategy in the future HR strategy has an insufficient input-influence on this organisation’s general strategy This organisation matches the characteristics of managers to the strategic plan of the organisation This organisation identifies managerial characteristics necessary to run the firm in the long term This organisation modifies the compensation systems to encourage managers to achieve long-term strategic objectives This organisation changes staffing patterns to help implement business or corporate strategies This organisation evaluates key personnel based on their potential for carrying out strategic goals Job analyses are based on what the job may entail in the future Development programmes are designed to support strategic changes

1 2 3 4 5

SHRM 6 SHRM 7 SHRM 8 SHRM 9 SHRM 10 SHRM 11 SHRM 12 SHRM 13


39


APPENDIX 2: HR PRIORITIES Variable

The HR strategy of this organisation places a very high priority on

PRIORITY 1 PRIORITY 2

. . . reducing risk to patients . . . recruiting, developing and retaining sufficient staff and enables the organisation to meet its obligations . . . reducing labour costs . . . improving employee efficiency and productivity . . . improving service quality by investing in HRs . . . ensuring the full utilisation of a high quality staff as a basis for guaranteeing high quality performance of the organisation . . . improving health outcomes by improving the skill-mix of the workforce . . . promoting the health, well-being and development of staff . . . ensuring the satisfaction and commitment of all staff . . . developing the skills and knowledge of all staff . . . effective healthcare teams . . . encouraging innovation in clinical, management and support services . . . training health professionals

PRIORITY PRIORITY PRIORITY PRIORITY

3 4 5 6

PRIORITY 7 PRIORITY PRIORITY PRIORITY PRIORITY PRIORITY PRIORITY

8 9 10 11 12 13

APPENDIX 3 For each of the following please indicate for the last financial year (2002–2003) Number Number Number Number Number Number Number Number Number

40

of of of of of of of of of

staff leaving voluntarily complaints pertaining to lack of training grievances lodged complaints due to organisational change disciplinary actions stress-related leave episodes incident reports lodged hours lost due to injury WorkCover claims exceeding 20 days



APPENDIX 4 Variable

How long have you worked in the organisation? (years) How long have you worked in the industry? (years)

CEO mean (n = 63)

HRD mean (n = 34)

SM mean (n = 85)

5.47

3.72

4.46

21.67

15.43

16.90

F

1.90 5.45***

Variable

Male

Female

CEO HRD SM c2 = 0.214**

38 13 31

26 21 54

** p < 0.05; *** p < 0.01


41