adoption of concept of balance scorecard in nsw health

5/16/2018 Adoption of Concept of Balance Scorecard in Nsw Health - slidepdf.com

http://slidepdf.com/reader/full/adoption-of-concept-of-balance-scorecard-in-nsw-health 1/27

Financial Accountability & Management, 27(3), August 2011, 0267-4424

ADOPTION OF THE CONCEPT OF A BALANCEDSCORECARD WITHIN NSW HEALTH: AN EXPLORATION

OF STAFF ATTITUDES

M ARIA C ADIZ D YBALL, LORNE CUMMINGS AND HUA YU∗

INTRODUCTION

The implementation of the Balanced Scorecard (BSC) as a strategic manage-

ment tool is an area which has had limited research coverage (Tuomela, 2005;

and Malmi, 2001). Whilst the concept of the BSC has had broad application by

the health sector internationally (e.g., Villalbi et al., 2007; ten Asbroek et al.,2004; Wyatt, 2004; Zelman et al., 2003; Le Grand et al., 1998; and Macdonald,

1998), moving from concept to practice has often proved difficult (Ukko et al.,

2007; and Chow et al., 1998). Some argue that its founders Kaplan and Norton

are not forthcoming with their guidance on how to implement the BSC (Ahn,

2001; and Butler et al., 1997). The practical reality of a successful design and

implementation of the BSC appears to be much more challenging and dependent

upon access to time, resources and staff commitment (Woods and Grubnig,

2008; and Wisniewski and Olafsson, 2004). Kaplan and Norton (2001) concede

that major management and staff commitment and effort are required for itsimplementation. Indeed the potential of employees to impact the development

and implementation phases of all performance measurement systems is an area

where more research could be done (Ukko et al., 2007). This paper reports

on staff attitudes towards the adoption of a BSC concept in New South Wales

Department of Health (NSW Health) in Australia, at its head office in Sydney

and one of its area health services in the Hunter New England Area (HNEH).

The interest on staff attitudes is predicated on a view that attitudes influence

staff’s intentions to use a system (Jarrar et al., 2007; Pierce and O’Dea, 2003;

and Agarwal and Prasad, 1998).

∗The authors are from the Department of Accounting and Finance, Macquarie University,Sydney. They appreciate the support of the executive directors, management and staff atNSW Health, in particular Dr. Robert McDonald and Ms Jennifer Jennings. The authorsare also thankful for feedback of the 2008 seminar and conference attendees at theCentre for Accounting and Auditing Research, Nanyang Technological University, Singapore;Department of Accounting and Finance, Macquarie University, Sydney; Global Management

Accounting Research Symposium and Accounting and Finance Association of Australia and

New Zealand Conference. This project was funded by the Department of Accounting andFinance at Macquarie University.

Address for correspondence: Maria Cadiz Dyball, Department of Accounting and Finance,Macquarie University, Sydney, NSW 2109, Australia.e-mail: [email protected]

C 2011 Blackwell Publishing Ltd, 9600 Garsington Road,

Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA, MA 02148, USA. 335



336 DYBALL, CUMMINGS AND YU

Both the management accounting and information technology (IT) literatures

acknowledge that the expected benefits of a system application cannot be

realised if staff do not accept the system for task performance (Woods and

Grubnig, 2008; Wisniewski and Olafsson, 2004; Scapens and Roberts, 1993;

and also see Bhattacherjee and Sanford, 2006). The IT literature embracesthe view that the implementation of systems must be addressed at different

levels including the semantic and psychological impact of the system on users

(DeLone and MacLean, 1998 and 2003).1 The semantic level focuses on the

precision of conveying to the user the meaning and purpose of the information

generated by the system whereas the psychological level concentrates on the

influence of the system on user behaviour. In this paper we draw inspiration from

Davis et al.’s (1989) Technology Acceptance Model (TAM), which was originally

conceived to better understand attitudes and intention of employees to use

computer technologies. The model has applications in management accounting(Islam and Kellermanns, 2006; and Kellermanns and Islam, 2004) and in other

disciplines (Phillips et al., 1994). The model is able to accommodate normative

and empirical claims of the usefulness and ease of use of the BSC. TAM predicts

that employee perception of ease of use and usefulness of a system impacts on

employee attitudes and intention to use a system.

The study therefore answers two research questions on perceptions of staff in

NSW Health at its Sydney head office and HNEH of the BSC: (1) how perceived

usefulness and perceived ease of use of the BSC are related to attitudes toward,

and consequent intention to use the BSC; and (2) how job level, functionalspeciality and degree of participation predict the perceived usefulness and

perceived ease of use of the BSC.

The remainder of the paper comprises five sections. The first section reviews

academic literature on the implementation of the BSC with particular emphasis

on the health sector. The second section provides background information on

NSW Health and HNEH and describes the basis for a BSC adoption. The third

section presents the research methodology and the fourth section contains the

results, validity tests and analyses of findings. The final section concludes and

identifies limitations and areas for further research.

LITERATURE REVIEW

Since its inception by Kaplan and Norton in 1992, the BSC has evolved into

a holistic methodology that converts organisational vision and strategy into an

inclusive set of performance measures (Voelker et al., 2001). It is considered

to be one of the major theoretical innovations in performance measurement

(Otley, 1999 and 2003; and Ittner and Larcker, 1998) and more importantly,

has the ability to link performance measures with business strategy, thereby assisting managers in strategy implementation (Otley, 1999). However, there

is a view that rhetoric is not convincingly supported by empirical evidence,

in particular, that the BSC is able to improve organisational performance

C 2011 Blackwell Publishing Ltd



ADOPTION OF THE CONCEPT OF A BALANCED SCORECARD 337

and if so, how it is able to do so (Davis and Albright, 2004; Norreklit, 2003;

and Speckbacher et al., 2003).2 Nonetheless, the application of the BSC now

extends beyond the profit sector, and is even purported to be equally useful in

public management (Johnsen, 2001; also see Inamdar and Kaplan, 2002; and

Kaplan and Norton, 2001). In a review of research on the BSC in healthcareorganisations, Aidemark and Funck (2009) observe that recent areas of interest

are on demonstrating financial gains and improved feedback for clinicians on

patient care, and benchmarking performance.

To redress the little guidance from its founders on how to implement

the BSC (Ahn, 2001; and Butler et al., 1997), researchers have identified

enablers and barriers to its successful implementation (e.g., Assiri et al., 2006;

Radnor and Lovell, 2003; and Kasurinen, 2002). Assiri et al. and Kasurinen

focus on private sector implementations whilst Radnor and Lovell investigate

the application of the BSC within the health sector. Interestingly, normativeliterature on the implementation of the BSC in the private sector tends to

emphasise design issues, management commitment and external or internal

exigencies necessitating change, whilst relevant literature in the health sector

focuses on individual staff acceptance. Perhaps this focus reflects the ‘new public

management’ mantra in the public sector and provides credence to Kim (2002,

p. 231) who states that ‘one of the leading challenges in public management has

been implementing effective human capital strategies to enhance government

performance and accountability’. It also highlights staff acceptance as a key

element in a successful BSC implementation which in turn provides legitimacy to the new system (Woods and Grubnig, 2008; Wisniewski and Olafsson, 2004;

and Scapens and Roberts, 1993).

Radnor and Lovell (2003) in their evaluation of the BSC within the Bradford

health sector in the United Kingdom (UK) identify factors that could facilitate

its successful adoption. Top on their list is demonstrating to individuals ‘how’ the

BSC system could add value over existing performance management systems.

Second, is developing the BSC at a strategic level to allow accurate evaluation

of the scale of compromises that practical deployment of the BSC at lower

strategic/tactical levels will require. Third, is implementation of the BSC at various levels within the organisation resulting in a chain of linked BSCs. This

factor extends to the identification of key performance indicators (KPIs) to

be included in the BSC, as it could be difficult for employees to make an

unbiased decision regarding which KPIs to use (Chan, 2006). This difficulty

is not rare in the few studies on BSC implementation across sectors (see

Banker et al., 2004; and Youngblood and Collins, 2003) although Pineno (2002),

using an incremental approach to test the cause and effect relationships of

BSC measures in hospitals in Ontario Canada, found that measures can reflect

hospital objectives. Voelker et al. (2001) studying issues in developing a BSC in the US health

sector, anticipated a significant level of resistance from employees at the initial

stage. Inamdar and Kaplan (2002) confirm that fear was a challenge in BSC





development and implementation, which required time to mitigate. A related

problem is identified in a review of the US health sector by Zelman et al.

(2003), who found that senior and front-line managers opposed the BSC when

it was imposed by external parties suggesting that employee involvement in

the conceptualisation and development of the BSC is a contributing factor toits successful adoption. The impact of external imposition is reflected in the

introduction of the BSC in New Zealand (NZ) hospitals, which was mandated.

Northcott and France (2005, p. 44) thus caution that ‘it cannot be assumed that

NZ hospitals BSC will permeate practice and decision-making to the point of

having any substantive impact on hospital management.’

The brief literature review on the BSC in the health sector reveals

that individual staff acceptance is an essential element in its successful

implementation (Aidemark and Funck, 2009; Northcott and France, 2005;

Radnor and Lovell, 2003; Zelman et al., 2003; Inamdar and Kaplan, 2002; Voelker et al., 2001; and also see Kaplan and Norton, 2001). Individuals must

be shown ‘how’ the BSC system could add value over existing performance

management systems. This process is known as addressing the semantic level of

system application, where the meaning and purpose of the system is conveyed

to the user (DeLone and MacLean, 1998 and 2003). The resistance and fear

from healthcare staff highlights the psychological aspect of a system application

where staff behaviour toward a system is addressed. This study focuses on the

semantic and psychological levels of BSC implementation and investigates the

impact of factors including perceived usefulness and ease of use of the BSC onindividual staff attitudes toward the BSC in a healthcare setting in Australia.

Thus the paper is able to provide suggestions on how employee resistance and

fear of the BSC could be overcome.

IMPLEMENTATION OF THE BSC IN NSW HEALTH

The New South Wales Hunter New England Health Service (HNEH) is one

of eight Area Health Services of the New South Wales Department of Health

(NSW Health). The Area Health Services are responsible for providing healthservices across a wide range of settings, from primary care posts in the remote

outback to metropolitan tertiary health centres. HNEH provides rural services

and was created on January 1, 2005. HNEH provides care for approximately

840,000 people, covers a geographical area of over 130,000 square kilometres,

and has approximately 14,500 staff, 1,500 medical officers and 1,600 volunteers.

It provides health services to 12% of NSW’s population, and 20% of the State’s

Aboriginal population. It spans 32 local government areas and spends A$1.3

billion (approximately US$1.2billion)3 per annum (approximately 12% of the

2006-07 NSW Department of Health budget) (NSW HNEH, 2008a).HNEH has priorities and goals that are underpinned by both the State Plan

and the seven strategic directions for NSW Health. NSW Health’s goals are: to

keep people healthy, to provide the health care that people need, to deliver high





quality services and to manage health services well. HNEH sets out to achieve

the seven strategic directions in the State Health Plan. It has its Area Strategic

Plan, which identifies strategies and initiatives for attaining these directions and

measures to assess their degree of attainment. The seven strategic directions

are to:

1. Make prevention everybody’s business,

2. Create better experiences for people using health services,

3. Strengthen primary health and continuing care in the community,

4. Build regional and other partnerships for health,

5. Make smart choices about the costs and benefits of health services,

6. Build a sustainable health workforce,7. Be ready for new risks and opportunities (NSW HNEH, 2008b).

In 2003, prior to a merger in 2005 with adjacent local government areas, the

Hunter Area Health Service (AHS) pioneered a BSC performance framework

(see Appendix A for 2007 version) that identified ‘areas of responsibility’

(IPART, 2003 as cited by Northcott and France, 2005). The aim was to use

multi-dimensional information to translate HNEH’s vision and purpose into

specific goals and actionable items, to ensure that the health system fulfils

public expectations. In 2003 it was reported that:the balanced scorecard model has been accepted very well within Hunter AHS. Staff feel that it provides them with clear directions, encourages organisational alignment,focuses on what is important, and is comprehensive and credible (Northcott andFrance, pp.40-1, citing IPART, 2003).

HNEH has continued with the BSC framework since the 2005 merger with

Hunter AHS staff gaining considerable hands-on experience.

The BSC concept adopted by the Head Office in Sydney is different from that

of HNEH, the former being developed by a NSW Health System Performance

Indicator Committee in 2003. The committee drew a minimum set of indicatorsthat would be relevant at all levels within health. The final indicator set

has created a health dashboard that allows monitoring of strategic directions

while providing an early warning system for any necessary remedial action.

The dashboard contains 20 high-level indicators designed to provide better

accountability for government; better information for strategic management

at the Departmental and Board/Health Service level, and better alignment of

effort at the clinical/operational level (NSW Health, 2008b).

This project is therefore timely in seeking to elicit and analyse staff attitudes

toward the BSC concept to enable an appreciation of staff’s behaviour, that is, asense as to whether staff will use the BSC in performing their tasks. Until 2007

when this study was conducted there had been only a partial roll-out of the BSC

at HNEH.





RESEARCH METHODOLOGY

Theoretical Framework

Attitudes toward the BSC of NSW Health staff in the Sydney Head Office and

HNEH are investigated using a theoretical framework which incorporates key concepts from Davis et al.’s (1989) Technology Acceptance Model (TAM). The

model is adapted to incorporate a number of variables identified in the academic

literature as critical for staff acceptance and use of a system (see the second

section previously; and also see Davis et al., 1989). The TAM was conceived to

explain user acceptance of computer technology, and various studies in the IT

literature found the model to have good predictive power (e.g., Karahanna et

al., 2006; Bhattacherjee and Sanford, 2006; Calantone et al., 2006; Venkatesh

et al., 2003; and Venkatesh and Davis, 2000). TAM proposes that individual

perceptions of usefulness and ease of use of a system influences their generalattitudes and intention to use it. Perception of an object is an important

factor affecting individual’s attitudes and behaviour toward it (Jarrar et al.,

2007; Warren, 2006; Pierce and O’Dea, 2003; and Agarwal and Prasad, 1998).

Perception is an outcome of a process where individuals represent objects in

the world around them. In the management accounting literature the TAM has

already been applied to assess user perceptions of the ease of use and usefulness

of the BSC and activity-based costing system (Islam and Kellermanns, 2006; and

Kellermanns and Islam, 2004). We continue this area of research using the case

of NSW Health to anticipate whether staff will reject or accept the BSC. Thetheoretical framework is presented in Figure 1.

The key concepts in the TAM are Perceived Usefulness (PU), Perceived Ease

of Use (PEU), General Attitudes and Intention to Use. PU is defined as ‘the

degree to which a person believes that using a particular system would enhance

Figure 1

Theoretical Framework

PerceivedUsefulness

Perceived Easeof Use

GeneralAttitudes

Intention to UseDegree of

Participation

Job Level(Director/managervs Non-director/

manager)

Functionalspeciality

(Clinician vs Non-clinician)

Original TAM Model





his or her job performance’ (Davis, 1989, p. 320). PEU, on the other hand, is

‘the degree to which a person believes that using a particular system would be

free of effort’ (Davis, 1989, p. 320). For this study, the concepts of PU, PEU,

general attitudes and intention to use were operationalised to accommodate

previous research and normative statements on usefulness of the BSC at bothorganisational and individual level. Additional related variables identified in

the psychology and accounting literatures were also incorporated to propose a

theoretical framework.

A concept incorporated in the theoretical framework is participation, defined

here as user behaviours and activities with respect to a given system. Barki

and Hartwick (1994) find that employees who participate in the development

process of a technology or system will tend to develop a positive attitude toward

it because of their better understanding of the merits of the innovation and

its benefits to individual employees (also see Kloot and Martin, 2000; Argyrisand Kaplan, 1994; Hartwick and Barki, 1994; and Milani, 1975). Research by

Northcott and France (2005) on the adoption of the BSC concept in NZ hospitals

suggests that the long-term acceptance of the BSC could be built up by employee

participation including, for example, staff engagement to ensure that the choice

and design of KPIs truly reflect the strategies and goals of the organisation.

Participation therefore also provides employees an opportunity to develop their

skills in BSC design. This leads us to the following hypotheses:

H1a: Participation is positively related to perceived usefulness (PU) of theBSC.

H1b: Participation is positively related to perceived ease of use (PEU) of the

BSC.

Job level and functional specialty are the second and third variables explored

in this paper as influencing PEU and PU. Job level is operationalised at two

levels: (1) A Director at the Head Office or manager at HNEH, and (2) A

Non-director of NSW Health or non-manager at HNEH. Functional specialty

is identified through the nature of the departments and for our purposes wesegregated clinicians from non-clinicians. The view that these variables influence

PEU and PU is based on research findings that employees at different levels of

the hierarchy and in different functional groups experience different demands,

requirements, job expectations and attitudes (Adams et al., 1977). Adams et al.

also found that the interaction of job level and functional speciality had a

significant effect on job attitudes. That is, even at the same job level, people from

different functional specialities have different job attitudes. Furthermore, they

predicted that members in similar organisational functions would have a greater

tendency to share common attitudes than members from different functions.Strebel (1996) claims that the differing views of managers and employees on

change is a common cause of difficulties in the introduction of new systems.

Top-level managers often perceive change as an opportunity whereas for many





employees change is neither sought after nor welcomed (Kasurinen, 2002). A

common feature of health sector reform in many countries has been a managerial

approach to health sector management which has resulted in the emergence of

a clinico-managerial grouping within the medical professions in Germany, Italy

and the UK, that assumes financial and managerial responsibilities (Jacobs,2005). Bradley and Parker (2006) find that Australian public sector managers

have an external focus and are more interested in the strategic direction

of the organisation compared to lower level employees. They also observe

that many managers have participated in numerous seminars and training

activities promoting the ‘new’ management framework, and would be expected

to have embraced the prescriptions of managerialism. Given that the BSC is

an innovation in NSW Health and HNEH, has a strategic focus, and reflects

managerialism in the public sector (see Hood, 1995), we propose the following

hypothesis:

H2: Directors and managers will have a higher PU and PEU compared to

non-directors and non-managers.

Early research on teams in a general hospital in the US (Hrebiniak, 1974)

found that different job functions have a significant impact on interpersonal

trust and commitment by task unit members. Findings of a comparative study

of 850 staff located in four English and two Australian hospitals highlighted

marked differences between the values and beliefs of respondents from medicaland nursing backgrounds (Degeling et al., 1998).

Nursing staff had collective orientations compared with the individualism of medicineand of the centrality to the professional/personal identity of medical respondents of their claimed right to self define, self describe and self validate their work (Degeling,2000, p. 12).

Generally doctors have also been seen as antagonistic to accounting practices

which are considered a threat to the fundamental values of the medical

profession (Jacobs, 2005; and Jones and Dewing, 1997). Particularly on the

BSC, Aidemark (2001) found that not all medical professionals welcomed its

adoption. Hospital managers were interested in the BSC, but some doctors

thought ‘why measure at all?’ believing that BSC measurements were useless.

In contrast, research by Bilkhu-Thompson (2003) on the implementation of the

BSC in an emergency medicine service system in the US, indicated that medical

professionals had more understanding of the BSC than employees within support

departments. This leads us to the following hypothesis:

H3: Non-health staff will have a higher PU and PEU compared to health

staff.

Following Davis et al.’s (1989) TAM, the following relationships between the

remaining variables are expected:





H4: PU is positively related to general attitudes.

H5: PEU is positively related to general attitudes.

H6: PEU is positively related to PU.

H7: PU is positively related to intention to use.

H8: General attitudes are positively related to intention to use.

Across the many empirical tests of TAM in the IT literature, PU has consistently

been correlated with usage intentions, with standard regression coefficients

typically around 0.6 (Vankatesh and Davis, 2000, p. 187). PEU, on the other

hand, has exhibited a less consistent relationship on intention across many

studies.

Method

Employee perceptions of the BSC in the NSW Health Service were obtained

using targeted mail-out questionnaires. The questionnaire consisted of a total

of sixty-two (62) multiple-choice questions covering six sections: background,

perceived usefulness, perceived ease of use, participation, general attitudes and

intention to use4. The questions were adapted from previous research that

operationalised the constructs, usefulness (Kaplan and Norton, 1996; and Davis,

1989); perceived ease of use (Davis, 1989); participation (Kaplan and Norton,

1996; Barki and Hartwick, 1994; Milani, 1975; and White and Ruh, 1973);general attitudes (Venkatesh and Davis, 2000; Ferguson, 1997; Taylor and Todd,

1995, and Milani, 1975), and employees’ intention to use (Bhattacherjee and

Sanford, 2006; and Calantone et al., 2006). A five point Likert scale (‘Strongly

Disagree,’ ‘Disagree,’ ‘Indifferent,’ ‘Agree,’ and ‘Strongly Agree’) was used for

the last five sections.

The Operational Director at HNEH assisted the authors in identifying one

hundred and seventy six (176) potential respondents, who were registered

attendees of BSC workshops held at HNEH in 2007. The workshops cover

general information on BSCs, how the BSC is used in HNEH, what the attendees’roles are, the benefits of using a BSC, as well as the development of BSCs for

represented subunits using the HNEH framework. Attendees are also provided

assistance with procedures for data collection, reporting and use. The Director

of Demand and Performance Evaluation of the Health System Performance

Division at the Sydney Head Office identified a further seven respondents; a

total of 183 target respondents. The survey was targeted to individuals familiar

with the BSC approach, who would have felt that they possessed sufficient

knowledge to respond to the questionnaire. Familiarity with the concept was

needed, in order to avoid the return of questionnaires with invalid data. Thefindings will resonate with the management of NSW Health in predicting if

staff would apply the BSC in their task performance and indeed caution must

be applied in extrapolating results from this study to other settings.5





The Partial least squares (PLS) approach was used to analyse survey results

because of its minimal demands on measurement scales, residual distributions

and sample size (Chin, 1999 and 2007). PLS allows sample sizes to be much

smaller (n < 100) (Smith and Langfield-Smith, 2004). The PLS approach is also

suitable in studies where there is low theoretical information and the goal isprediction (Joreskog and Wold, 1982). Based on a system of interdependent re-

gressions, the PLS path analysis makes several types of predictions, one for each

regression in the system. In this project, the authors adapted Davis et al.’s (1989)

TAM to incorporate normative claims and insights from the accounting and

psychology literatures. Thus, this study is exploratory rather than a confirmatory

investigation of a robust theoretical model in the accounting literature. The PLS

path analysis approach, however, has its followers in the accounting discipline

(e.g., Abernethy et al., 2007; Moulang 2006; Chenhall, 2005; Anderson et al.,

2002; Vandenbosch, 1999; and Ittner et al., 1997). Indeed Smith and Langfield-Smith (2004, pp. 78-9) encourage researchers in management accounting to use

the PLS path analysis to promote theory development.

FINDINGS AND ANALYSIS

Response Rate and Demographics

A total of 48 questionnaires were returned from the 183 targeted participants; an

overall response rate of 26.23%.6 Six of these questionnaires were received from

the Sydney Head Office; a response rate of 85.71%. The other 42 questionnaires were received from the 176 targeted respondents from the HNEH; a response

rate of 23.86%.

Forty-seven individuals out of the 48 returned questionnaires had complete

demographic information. Of the 47, 30 were female (63.83%) and 17 were male

(36.17%). Many (n = 23) of the respondents were in the age range of 45-54 years

old (1 = 25-34 years old; 13 = 35-44 years old; 10 = 55+ years old). Of the 42

respondents from HNEH, the two most represented divisions were Population

Health, Planning and Performance (38.10%) and Clinical Operations (33.33%).

A majority of the respondents were also working in non-clinical related positions(61.71%). A further category of key respondents included directors/managers in

clinical health. Respondents (n = 3) who chose ‘other’ as their functional area,

and did not specify their position, were treated as missing data. As it is impossible

to identify whether the people who identified Allied Health as their functional

area were managers or not (there is no option given for them to choose), they

were also treated as missing data when analysing the manager/non-manager

distribution. Table 1 illustrates the function speciality and position/job level of

the 44 remaining respondents.

A majority of respondents (63.83%) held a postgraduate degree, and all ‘healthcategory of employment’ respondents possessed either an undergraduate or a

postgraduate qualification. By comparison, people in the non-health category of

employment had education ranging from secondary school to postgraduate.





Table 1

Function Speciality and Position Level

Clinician Non-Clinician Total

Director/Manager 13 (29.54%) 19 (43.19%) 32 (72.73%)Non-Director/Manager 2 ( 4.55%) 10 (22.72%) 12 (27.27%)Total 15 (34.09%) 29 (65.91%) 44 (100%)

Descriptive Statistics – Measurement of Variables

The mean values of all measures in each construct have a theoretical range from

one to five. All 48 responses were used to calculate the descriptive statistics of

five constructs. The mean value in Table 2 is the mean value of all respondents’

answers in each construct respectively. All the means are between three and

four, indicating a ‘slight’ agreement overall.

Table 2

Descriptive Statistics – Measurement

Actual Range Number Number of Std.

Construct of Items Respondents Mean Deviation Minimum Maximum

Perceived Usefulness 20 48 3.87 0.6354 1.70 5.00Perceived Ease of Use 11 48 3.46 0.5637 2.36 4.82Participation 9 48 3.82 0.7576 2.00 5.00General Attitudes 10 48 3.41 0.5589 1.90 4.40Intention to Use 4 48 3.81 0.8466 1.25 5.00

Factor Analysis, Item Reliability, Convergent Validity and Discriminant Validity Tests

Factor analysis was applied to determine whether there were distinct factor

groupings within each construct. It was determined that of the constructs, it was

only PU which had two factors, which we describe to be Perceived Usefulness

for the Individual (PUI) and Perceived Usefulness for the Organisation (PUO).

PUO referred to organisational benefits relating to risk management, strategic

formulation and design of organisational key performance indicators.

Item (question) reliability was tested using factor analysis with a loading

threshold of 0.7 (Hulland, 1999). High loadings indicate that a construct strongly

influences the items, whilst low loadings indicate that the construct has a weakinfluence on the items. Items with loadings of less than 0.70 were treated as

unreliable and therefore removed from subsequent analysis. This resulted in 19

of the 62 items (questions) in the reliability test being eliminated (see Appendix

B for items/questions and loadings).





Table 3

Composite Reliability Statistics

Perceived Usefulness for Individual:

Composite Reliability 0.945 AVE 0.712 Perceived Usefulness for Organisation:

Composite Reliability 0.925 AVE 0.638 Perceived Ease of Use:

Composite Reliability 0.915 AVE 0.684 Participation:

Composite Reliability 0.941 AVE 0.665General Attitudes:

Composite Reliability 0.918 AVE 0.691 Intention to Use:

Composite Reliability 0.927 AVE 0.810

Convergent validity tests to determine the extent to which multiple measures

of a construct agree with one another (Campbell and Fiske, 1959, as cited by

Barki and Hartwick, 1994) were also undertaken using composite reliability

tests and Average Variance Extracted (AVE) (Hulland, 1999). Thresholds of 0.7

and 0.5 for composite reliability tests and the AVE, respectively, were applied

to measure the convergent validity of the five constructs in the model (see

Table 3).Discriminant validity ensures that the measures of each construct are

different from the measures of other constructs and that each construct

measures different concepts. The square root of AVE should be larger than the

correlations of each of the constructs, so that discriminant validity is maintained.

In Table 4, figures at the first cell of each column are the square roots of AVE;

whilst below diagonal figures are the correlations between the constructs. It

illustrates that all AVE values were larger than the correlations, suggesting that

there is discriminant validity between items of each construct.

Analysis

The summarised results (Figure 2) were produced by the PLS-Graph 3.0 using

the data from the questionnaires. We performed bootstrapping7 as a necessary

step to obtain the significance level of the path coefficient (Chin, 2007). The

resample size for bootstrapping was 6,000, determined by a principle that the

greater the resample size the more stable the t-statistics will be. Because the

relationships between the constructs are directional, the t-statistics were treated

as a one-tailed test and were compared with the critical values 1.645 ( p < 0.05),

2.326 ( p < 0.01) and 3.091 ( p < 0.001) following similar research (Moulang,

2006).





T a b

l e 4

S q u a r e R o o t o f A v e r a g e V a r i a

n c e E x t r a c t e d v s . C o r r e l a t i o n s

P e r c e i v e d

P e r c e i v e d

U s e f u l n e s s

U s e f u l n e s s

P e r c e i v e d

f o r

f o r

E a s e o f

G e n e r a l

I n t e n t i o n

I n d i v i d u a l

O r g a n i s a t i o n

U s e

P a r t i c i p a t i o n

A t t i t u d e s

t o U s e

P e r c e i v e d U

s e f u l n e s s f o r I n d i v i d u a l

0 . 8

4 4

P e r c e i v e d U

s e f u l n e s s f o r O r g a n i s a t i o n

0 . 8 1 9

0 . 7 9

9

P e r c e i v e d E a s e o f U s e

0 . 6 0 9

0 . 3 7 3

0 . 8

2 7


0 . 4 8 3

0 . 3 9 8

0 . 3 8 2

0 . 8

1 5

G e n e r a l A t t

i t u d e s

0 . 6 9 9

0 . 5 1 9

0 . 7 6 9

0 . 4 2 1

0 . 8

3 1

I n t e n t i o n t o

U s e

0 . 7 0 2

0 . 6 8 9

0 . 6 2 1

0 . 4 1 4

0 . 7 6 4

0 . 9

0 0





F i g u

r e 2

R e s

u l t s

P e r c e i v e d

U s e f u l n e s s f o r

I n d i v i d u a l

R 2 = 5 4 . 7 % P

e r c e i v e d E a s e o f

U s e

R 2 = 1 7 . 8 %

G e n e r a l A t t i t u d e s

R 2 = 6 7 . 7 %

I n t e n t i o n t o U s e

R 2 = 7 0 . 2 %

D e g r e e o f


J o b L e v e l

( D i r e c t o r / m a n a g e r v s

N o n - d i r e c t o r /

m a n a g e r )

F u n c t i o n a l s p e c i a l i t y

( C l i n i c i a n v s N o n -

c l i n i c i a n )

P e

r c e i v e d

U s e f

u l n e s s f o r

O r g a n i s a t i o n

R 2

= 3 6 . 0 %

0 . 2 6 8 *

N S

N S

- 0 . 2 8 4 *

- 0 . 3 7 9 * *

N S

N S

0 . 4 4 3 * *

0 . 2 6 8 *

0 . 5 2 * * *

0 . 5 5 8 * * *

N S

N S

0 . 5 8 1 * * *

0 . 4

4 4 * *

0 . 3 1 8 *

0 . 4 4 4 * *

N S

N S

N o t e s :

N S : N o t S i g n i f

i c a n t .

∗

: p <

0 . 0 5 l e v e l o f s i g n i f i c a n c e .

∗ ∗

: p <

0 . 0 1 l e v e l o f s i g n i f i c a n c e .

∗ ∗ ∗

: p <

0 . 0 0 1 l e v e l o f s i g n i f i c a n c e .





Table 5 presents the path coefficient, significance level, and support for each

of the hypotheses of our adapted model (Figure 2). Three of the seventeen paths

(PEU to PUI, PEU to general attitudes and general attitudes to intention to use)

were significant at the 0.1% level, three at the 1% level (functional speciality

Table 5

Model Path Coefficients and Significance

Path T-

Relationships Coefficients statistics Support for Hypothesis

Functional Speciality → PUI −0.284 2.295∗ Hypothesis 3: Non-health staff willhave a higher PU and PEUcompared to health staff. PARTIALSUPPORT

Functional Speciality → PUO −0.379 2.8279∗∗

Functional Speciality → PEU −0.046 0.3324

Job Level → PUI 0.268 1.7216∗ Hypothesis 2: Directors andmanagers will have a higher PU andPEU compared to non-directors andnon-managers. PARTIALSUPPORT

Job Level → PUO 0.241 1.3197 Job Level → PEU −0.171 0.9723

Participation → PUI 0.154 1.3688 Hypothesis1a: Participation ispositively related to perceived

usefulness (PU). NOTSUPPORTEDParticipation → PUO 0.167 1.2214

Participation → PEU 0.443 2.8767∗∗ Hypothesis1b: Participation ispositively related to perceived easeof use (PEU). SUPPORTED

PEU → PUI 0.52 3.5337∗∗∗ Hypothesis 6: PEU is positively related to PU. SUPPORTED

PEU → PUO 0.268 1.6663∗

PEU → General Attitudes 0.558 4.1091∗∗∗ Hypothesis 5: PEU is positively related to general attitudes.

SUPPORTED

PUI → General Attitudes 0.318 1.9511∗ Hypothesis 4: PU is positively relatedto general attitudes. PARTIALSUPPORT

PUO → General Attitudes 0.048 0.3721

PUI → Intention to Use −0.068 0.3988 Hypothesis 7: PU is positively relatedto intention to use. PARTIALSUPPORT

PUO → Intention to Use 0.444 2.8366∗∗

General Attitudes →Intention to Use

0.581 3.5875∗∗∗ Hypothesis 8: General attitudes arepositively related to intention to use.SUPPORTED

Notes:∗: p < 0.05 level of significance.∗∗: p < 0.01 level of significance.∗∗∗: p < 0.001 level of significance.





to PUO, participation to PEU and PUO to intention to use), with another four

(functional speciality to PUI, job level to PUI, PEU to PUO and PUI to general

attitudes) significant at the 5% level.

The analysis demonstrates that the strongest positive relationships are along

the path starting from participation to PEU ( p < 0.01), PEU to general attitudes( p < 0.001), and finally general attitudes to intention to use ( p < 0.001).

These results suggest that in NSW Health’s Sydney Head Office and HNEH,

participation in the process of designing and implementing the BSC is useful in

predicting employee perceptions on the BSC’s ease of use, which in turn allows

prediction of attitudes toward and intention to use the BSC concept. In addition,

PEU could significantly predict PUI ( p < 0.001), which then anticipates staff’s

general attitudes ( p < 0.05). PUO ( p < 0.05) is also significantly related to staff’s

intention to use ( p < 0.01).

The expectation that different variables impact upon PU and PEU was alsopartially supported at the 1% and 5% levels. Job level appears to affect PUI ( p <

0.05), which suggests that directors and managers embrace the attitude that the

BSC is more useful personally, compared to employees who are neither directors

nor managers. Functional speciality appears to be related to both PUI ( p < 0.05)

and PUO ( p < 0.01), with clinicians seemingly of the view that the BSC was not as

useful personally or for the organisation, compared with non-clinicians. However,

both job level and functional specialty did not appear to have any relationships

with PEU, which was predominantly influenced by participation. Interestingly,

the results suggest that participation was not related to employee opinion onBSC usefulness. Another interesting finding is that PUI can significantly affect

general attitudes ( p < 0.05), but not intention to use, whilst PUO has significant

influence on intention to use ( p < 0.01), but not general attitudes.8

Two-sample t-tests were also conducted to identify which group of respondents

had a greater opportunity to participate in the design and implementation of

the BSC. The results indicate that job level has a significant relationship ( p

< 0.01) with participation. This suggests that directors/managers participated

more often compared to non-director/manager employees.

Another two-sample t-test was used to compare the mean results of Sydney Head Office and HNEH respondents. The results demonstrated that there

were significant differences between these two groups with respect to perceived

usefulness ( p < 0.05) and general attitudes ( p < 0.05). Respondents from the

Sydney Head Office, perceived the BSC to be more useful and had a more

positive attitude to the BSC compared to respondents from the HNEH who are

physically located within a hospital campus.9

CONCLUSION, LIMITATIONS AND AREAS FOR FURTHER RESEARCH

This study of staff attitudes toward the adoption of the BSC concept at NSW

Health and HNEH sought to answer: (1) how perceived usefulness and perceived

ease of use of the BSC are related to attitudes toward, and consequent intention





to use the BSC; and (2) how job level, functional speciality and degree of

participation predict the perceived usefulness and perceived ease of use of the

BSC. The findings demonstrated that in the context of BSC adoption in a public

health sector setting, participation and perceived ease of use (PEU) are key

variables that predicted employee attitudes and ultimately intention to use theBSC. Participation positively influences staff PEU of the BSC. PEU, in turn,

is positively correlated with staff perceived usefulness (both at individual and

organisational level: PUI and PUO) of the BSC and general attitudes. This

finding implies that staff perceived the BSC to be useful if it was easy to use.

With increasing pressures from the public and other government agencies for

greater service and accountability from NSW Health, one could appreciate that

any performance management system that is difficult to use will not be seen as

useful. Results also indicate that job level and functional specialty could affect

respondents’ (management and employee) perceptions regarding the strategicusefulness of the BSC. Directors and managers embrace the BSC as more useful,

compared with employees who are neither directors nor managers. Clinicians

also seem to view the BSC as less useful when compared with non-clinicians,

which partly confirms prior findings by Aidemark (2001) and is consistent

with Jacobs’ (2005) assessment of a seemingly antagonistic view of the medical

profession toward accounting practices.

Further analyses revealed also that managers participated more often than

non-managerial staff across different functional specialities in the design and

implementation of the BSC concept. This finding follows normative advice from Assiri et al. (2006) who, in their road map to successful BSC implementation,

identify as a dominant factor executives’ and senior managers’ commitment.

They recommend that senior management be actively responsible for and

involved in BSC implementation at the ground level. This study’s findings,

however, suggest that there is a point where senior management needs to

allow non-managerial staff to participate if they want to engender positive

general staff attitudes toward the BSC. Respondents from NSW Health, who

operate within a strategic managerial/office based environment also appear to

perceive the BSC to be more useful and had a more positive attitude toward itcompared to respondents from HNEH, who are closer to the interface between

hospital systems, patient care and community needs, and who are responsible

for the primary collation of much of the data. Consequently, the benefits of

a BSC need to be clearly articulated to clinicians, who are physically tasked

with providing these health services. Instead of being perceived as a purely

burdensome managerial tool, the BSC process must be seen as beneficial

and relevant to those professionals involved in gathering and achieving BSC

indicators, for this process to be effective.

The application of a modified TAM in a different context, viz, the healthsector and the BSC system, reveal findings that deviate from previous empirical

studies examining the application of technology. In particular, PEU was found

to be a fundamental predictor of usage intentions in our context instead of PU,





which was more relevant in applications of technology. The implications are

that in complex public organisations like NSW Health and HNEH which are

subject to competing stakeholder interests, needs, and expectations; eliciting

individual staff acceptance and uptake of any system requires a PEU of systems

instead of PU.The findings of this study, however, are subject to at least three limitations.

First is the intrinsic limitation of the survey method, which others refer to as

defects in the process of designing and conducting the survey. Technically, self-

administered questionnaires can be subject to measurement errors which are

the result of social desirability bias (Singleton and Straits, 2005). The second

limitation is the small sample size of the study, which limits the generalisability

of the results beyond NSW Health. Future research examining results from

other area health services, which have, or may consider the adoption and

implementation of a BSC at a future date might reveal different results.The third limitation is with the PLS path analysis approach to analysing the

survey results. The nature of the PLS approach is rather exploratory and data-

driven than confirmatory. It is usually referred to as a ‘soft’ modelling approach

because no normality hypothesis is claimed to be required, there seems to be no

needed constraint on the measurement scale of the manifest indicators and the

number of observations may be limited with respect to the number of variables

(Vinzi, 2007). Thus the small and targeted sample respondents of BSC workshop

attendees at the NSW Health combined with the PLS path analysis approach

limit the generalisability of results outside of the NSW Health.However, we encourage other management accounting researchers to con-

tinue to use the PLS path analysis approach to develop theories (see also Smith

and Langfield-Smith, 2004). There are also other areas of research arising

out of this exploratory study. In this project, job level was simply categorised

into director/manager and non-director/manager. Functional speciality was cat-

egorised into clinician and non-clinician. In future research, other employment

categories could be explored. More importantly, the attitudes of stakeholders

other than employees (including members of the local community and patients)

could be solicited in order to provide a ‘balanced’ assessment of the relevanceof the BSC to the health sector from a recipient perspective. Finally, this study

only considered the relationship between three factors (participation, job level

and functional specialty) and PU, PEU and general attitudes that consequently

affected the intention to use the BSC. Future research could test other factors

affecting PU and PEU, from alternate models, including organisational culture

and structure, the relationship between attitudes and executive commitment

and project ownership. Alternatively the proposed theoretical framework could

be applied in a longitudinal study. With the health sector facing ever increasing

expectations by the public for better performance and service, new systemsof performance measurement should enable health agencies to become more

efficient and effective. This paper highlighted and explored a key element in

successful implementation of the BSC as a PMS, that of staff acceptance.





A P P E N

D I X A

2 0 0 7 H u n

t e r N e w E n g l a n d - N S W

H e a l t h P e r f o r m a n c e F r a m e w o r k





A P P E N

D I X B

I t e m R e l i a b i l i t y L o a d i n g s

I t e m s

L o a d i n g s

D e s c

r i p t i o n

P e r c e i v e d U s e f u l n e s s f o r I n d i v i d u a l

A t m y o r g

a n i s a t i o n , u s i n g a B S C w o u

l d :

P U 1

0 . 8 6 7 2

i m p r o v e

m y j o b p e r f o r m a n c e

P U 2

0 . 9 1 3 9

i n c r e a s e

m y j o b p r o d u c t i v i t y

P U 3

0 . 7 9 6 3

m a k e i t

e a s i e r f o r m e t o p e r f o r m m y

t a s k s

P U 1 1

0 . 7 2 8 5

e n a b l e m y o r g a n i s a t i o n t o a l i g n o r g a n i s a t i o n a l a n d p e r s o n a l g o a l s

P U 1 7

0 . 8 4 4 4

e n a b l e m y o r g a n i s a t i o n ’ s ‘ s t a k e h o l d e r s ’ t o b e t t e r a s s e s s f i r m p e r f o r m a n c e

P U 1 9

0 . 9 3 8 8

O v e r a l l

t h e B S C w o u l d b e u s e f u l t o m

e

P U 2 0

0 . 7 9 8 5

O v e r a l l

t h e B S C w o u l d b e u s e f u l t o t h e o r g a n i s a t i o n a s a w h o l e

P e r c e i v e d U s e f u l n e s s f o r O r g a n i s a t i o n

A t m y o r g

a n i s a t i o n , u s i n g a B S C w o u

l d :

P U 4

0 . 7 8 4 1

e n h a n c e o r g a n i s a t i o n a l e f f i c i e n c y

P U 5

0 . 8 5 1 4

e n h a n c e o r g a n i s a t i o n a l e f f e c t i v e n e s s

P U 6

0 . 8 0 3 2

h e l p i d e

n t i f y t h e o r g a n i s a t i o n s m a i n p e r f o r m a n c e d r i v e r s

P U 7

0 . 6 7 2 8

h e l p i d e

n t i f y a n d m a n a g e o r g a n i s a t i o

n a l r i s k s

P U 8

0 . 6 1 2 9

e n a b l e m y o r g a n i s a t i o n t o c l a r i f y a n d

g a i n c o n s e n s u s a b o u t s t r a t e g

y

P U 9

0 . 7 7 5 5

e n a b l e m y o r g a n i s a t i o n t o i d e n t i f y a n

d a l i g n s t r a t e g i c i n i t i a t i v e s

P U 1 0

0 . 7 8 2 9

e n a b l e m y o r g a n i s a t i o n t o c o m m u n i c a t e i t s s t r a t e g y

P U 1 2

0 . 7 3 2 0

e n a b l e m y o r g a n i s a t i o n t o p e r f o r m p e r i o d i c a n d s y s t e m a t i c s t r a t e g i c r e v i e w s

P U 1 3

0 . 6 9 1 1

e n a b l e m y o r g a n i s a t i o n t o r e c e i v e f e e

d b a c k o n a r e a s f o r i m p r o v e m e n t

P U 1 4

0 . 7 5 7 7

e n a b l e m y o r g a n i s a t i o n t o b a l a n c e s h o r t - a n d l o n g - t e r m o b j e c t i v e s

P U 1 5

0 . 6 5 8 8

e n a b l e m y o r g a n i s a t i o n t o b a l a n c e f i n

a n c i a l a n d n o n - f i n a n c i a l t a r g e t s

P U 1 6

0 . 4 7 0 2

e n a b l e m y o r g a n i s a t i o n t o b a l a n c e e x

t e r n a l a n d i n t e r n a l t a r g e t s

P U 1 8

0 . 5 6 8 1

b e p r o b l e m a t i c f o r m e a s I a m s t i l l c o

n f u s e d a s t o w h a t t h e p u r p o s e o f t h e B S C

i s a c t u a l l y f o r





A P P E N D I X B

( C o n t i n u e d )

I t e m s

L o a d i n g s

D e s c

r i p t i o n

P e r c e i v e d E a s e o f U s e

A t m y o r g

a n i s a t i o n , I w o u l d f i n d t h a t

t h e B S C :

P E U 1

0 . 6 4 2 0

w o u l d b

e e a s y t o l e a r n

P E U 2

0 . 6 8 5 0

w o u l d n

o t t a k e u p t o o m u c h o f m y t i m

e

P E U 3

0 . 8 2 0 1

w o u l d b

e c l e a r a n d u n d e r s t a n d a b l e

P E U 4

0 . 8 0 2 7

w o u l d b

e f l e x i b l e i n t e r m s o f i n t e r a c t i o n

P E U 5

0 . 6 5 2 4

w o u l d a l l o w m e t o b e c o m e s k i l f u l i n u

s i n g i t

P E U 6

0 . 5 7 0 8

w o u l d m

a k e i t e a s i e r t o c o - o r d i n a t e w

i t h o t h e r d e p a r t m e n t s

P E U 7

0 . 8 1 6 4

w o u l d m

a k e i t e a s i e r t o a l i g n d e p a r t m

e n t a l g o a l s w i t h o r g a n i s a t i o n

a l g o a l s

c o m p a r e d t o o t h e r p e r f o r m a n c e m e a s u r e m e n t s y s t e m s

P E U 8

0 . 6 8 1 7

w o u l d m

a k e i t e a s i e r t o u n d e r s t a n d h o w w e l l w e a r e a l i g n i n g o u r o b j e c t i v e s w i t h

p e r f o r m a n c e i n d i c a t o r s

P E U 9

0 . 4

3 7 5

w o u l d b

e e a s y t o u n d e r s t a n d a s p l e n t y o f g u i d a n c e h a s b e e n p r o v i d e d a b o u t

w h a t t h e B S C i s a l l a b o u t

P E U 1 0

0 . 7 2 6 4

w o u l d b

e e a s y t o u s e b e c a u s e w e a l r e a d y c o l l e c t d a t a a n d r e p o r t o n

t h e a r e a

P E U 1 1

0 . 8 6 1 8

O v e r a l l , I w o u l d f i n d t h e B S C e a s y t o

u s e

D e g r e e o f P

a r t i c i p a t i o n

A t m y o r g

a n i s a t i o n :

P A R 1

0 . 8 7 5 1

m y o p i n

i o n s a n d / o r s u g g e s t i o n s a r e s o u g h t w h e n s e t t i n g B S C K e y P e r f o r m a n c e

I n d i c a t o r s ( K P I s )

P A R 2

0 . 8 2 2 7

I w i l l b e

i n v o l v e d i n d e s i g n i n g a n d s e t t i n g s o m e B S C K P I s

P A R 3

0 . 8 0 4 3

I h a v e a

s i g n i f i c a n t d e g r e e o f i n f l u e n c e i n s e t t i n g B S C K P I s

P A R 4

0 . 6 8 0 3

I w i l l b e

a b l e t o d e c i d e w h a t B S C K P I s a r e u s e d

P A R 5

0 . 7 3 0 4

I w i l l b e

i n v o l v e d i n c o m m u n i c a t i n g t h e B S C t o o t h e r e m p l o y e e s

P A R 6

0 . 8 5 8 8

I w i l l b e

i n v o l v e d i n c o n d u c t i n g r e v i e w s o f t h e B S C

P A R 7

0 . 7 8 1 8

I w i l l b e

i n v o l v e d i n m e e t i n g s o n t h e B S C

P A R 8

0 . 7 8 8 5

I w i l l b e

i n v o l v e d i n d e v e l o p i n g a n i m

p l e m e n t a t i o n p l a n f o r t h e B S C

P A R 9

0 . 8 3 8 4

O v e r a l l , I h a v e a s i g n i f i c a n t a m o u n t o f p a r t i c i p a t i o n a n d i n v o l v e m e n t i n t h e

B S C p r o j e c t





A P P E N D I X B

( C o n t i n u e d )

I t e m s

L o a d i n

g s

D e s c r

i p t i o n

G e n e r a l A t t i t u d e s

I n m y o r g a n i s a t i o n :

A T T 1

0 . 8 3 3 4

I l i k e t h e i d e a o f u s i n g a B S C

A T T 2

0 . 6 9 2 4

t h e r e a s o n s p r o v i d e d f o r u s i n g e a c h o f

t h e B S C m e a s u r e m e n t s a r e s

o u n d a n d / o r

l o g i c a l

A T T 3

0 . 8 0 5 4

u s i n g t h e B S C w o u l d b e a p l e a s a n t e x p e r i e n c e

A T T 4

0 . 5 7 0 9

e v e n i f a

B S C s a v e s t i m e , I w o u l d p r e f

e r a n o t h e r p r e v i o u s o r n e w p e

r f o r m a n c e

m a n a g e

m e n t s y s t e m

A T T 5

0 . 4 8 3 9

I f e e l I n

e e d m o r e s u p p o r t i n m a n a g i n

g p e r f o r m a n c e a g a i n s t t a r g e t s

A T T 6

0 . 1 0 1

3

i f m o r e t

r a i n i n g c o u r s e s f o r B S C a r e a

v a i l a b l e , I a m i n t e r e s t e d i n t a

k i n g t h e m

A T T 7

0 . 4 9 8 2

u s i n g t h e B S C l e a d s t o u p p e r m a n a g e m e n t w a n t i n g m o r e a n d m o r e

r e p o r t s ,

s o m e o f w h i c h m a y b e u n n e c e s s a r y

A T T 8

0 . 7 1 4 0

t h e B S C

w i l l m a k e w o r k m o r e i n t e r e s t i n g

A T T 9

0 . 8 1 0 0

t h e B S C

s h o u l d h a v e b e e n i m p l e m e n t e d a l o n g t i m e a g o

A T T 1 0

0 . 8 5 6 0

O v e r a l l ,

I h a v e a p o s i t i v e a t t i t u d e a b o u t t h e B S C

I n t e n t i o n t o U s e

I n m y o r g a n i s a t i o n :

I N T E N 1

0 . 8 8 0 0

I i n t e n d

t o s u p p o r t t h e a d o p t i o n o f t h e B S C

I N T E N 2

0 . 9 1 5 5

i f I w e r e

a s k e d t o e x p r e s s m y o p i n i o n r e g a r d i n g t h e a d o p t i o n o f t h e

B S C , I

i n t e n d t o s a y s o m e t h i n g f a v o u r a b l e

I N T E N 3

0 . 8 4 1 9

i f I c o u l d

m a k e t h e d e c i s i o n t o a d o p t t

h e B S C , I i n t e n d t o

I N T E N 4

0 . 6 9 9 5

I i n t e n d

t o u s e B S C f o r o t h e r a r e a s o f

m y d a i l y e m p l o y m e n t r e s p o n s i b i l i t i e s





NOTES

1 According to the authors cited, there are three levels of a successful system implementationincluding but not limited to the technical level, where the focus is on the accuracy and efficiency of a system.

2 A recent study that provides empirical evidence on the BSC’s effect on performance is DeGeuser et al. (2009).

3 Exchange rate is US$1:A$.9252 as at 23 October, 2009.4 The survey instrument is available on request from the corresponding author.5 The authors thank one of the anonymous referees for this comment.6 A two-sample T -test was used to compare the means of survey responses of early and late

respondent groups. The results indicated that there were no statistically significant differencesin all the constructs at the 5% level of significance.

7 A method for estimating population parameters by repeatedly ‘resampling’ the same sample,computing test statistics on each aspect of the sample and then examining the distribution of the test statistic across the entire sample.

8 As there is no proper overall goodness-of-fit measures in the PLS approach, Hulland (1999)suggests that the r-squared ( R2) for all dependent constructs should be reported. Unfortunately,as far as we know, there is no similar research in the BSC area to this project, so it is difficultto compare the current study’s R2s with previous research. Hulland (1999), however, statedthat the spread of R2s in previous studies that used PLS had ranged from 12% to 64%. So, ingeneral, the R2s for General Attitudes (67.7%) and Intention to Use (70.2%) are more thansatisfactory and the R2s for PUI (54.7%), PUO (36%) and PEU (17.8%) are at a reasonablelevel.

9 Tests of multicollinearity among Functional Speciality, Job Level and Participation (not shown)indicated that there are no multicollinearity problems with tolerances above 0.20 and the

variation inflation factor (VIF) less than 5.

REFERENCES

Abernethy, M.A., A.K.-D. Schulz and S. Bell (2007), ‘Translating Organizational LearningOrientation into Performance: The Role of Management Control System’, Working Paper(The University of Melbourne).

Adams, E.F., D.R. Laker and C.L. Hulin (1977), ‘An Investigation of the Influence of Job Leveland Functional Specialty on Job Attitudes and Perceptions’, Journal of Applied Psychology, Vol.62, No. 3, pp. 334–43.

Agarwal, R. and J. Prasad (1998), ‘The Antecedents and Consequents of User Perceptions inInformation Technology Adoption’, Decision Support Systems, Vol. 22, No. 1, pp. 15–29.

Ahn, H. (2001), ‘Applying the Balanced Scorecard Concept: An Experience Report’, Long Range Planning,Vol. 34, pp. 441–61.

Aidemark, L.-G. (2001), ‘The Meaning of Balanced Scorecards in the Health Care Organisation’, Financial Accountability & Management, Vol. 17 No. 1, pp. 23–40.——— and E.K. Funck (2009), ‘Measurement and Health Care Management’, Financial

Accountability & Management, Vol. 52, No. 2, pp. 253–76. Anderson, S.W., J.W. Hesford and S.M. Young (2002), ‘Factors Influencing the Performance of

Activity Based Costing Teams: A Field Study of ABC Model Development Time in the Automobile Industry’, Accounting, Organizations and Society, Vol. 27, No. 3, pp. 195–211.

Andon, P., J. Baxter and H. Mahama (2005), ‘The Balanced Scorecard: Slogans, Seduction andState of Play’, Australian Accounting Review, Vol. 15, No. 1, pp. 29–38.

Assiri, A., M. Zairi and E. Riyad (2006), ‘How to Profit from the Balanced Scorecard: AnImplementation Roadmap’, Industrial Management and Data Systems, Vol. 106, No. 7, pp.937–52.

Atkinson, A.A. and J.Q. McCrindell (1997), ‘Strategic Performance Measurement’, CMA Magazine(April), pp. 20–23.Banker, R.D., H. Chang, S.N. Janakiraman and C. Konstans (2004), ‘A Balanced Scorecard Analysis

of Performance Metrics’, European Journal of Operations Research, Vol. 154, pp. 423–36.





Barki, H. and J. Hartwick (1994), ‘Measuring User Participation, User Involvement, and User Attitude’, MIS Quarterly, Vol.18, No. 1, pp. 59–82.

Bhattacherjee, A. and C. Sanford (2006), ‘Influence Processes for Information Technology Acceptance: An Elaboration Likelihood Model’, MIS Quarterly, Vol. 30, No. 4, pp. 805–25.

Bilkhu-Thompson, M.K. (2003), ‘A Process Evaluation of a Health Care Balanced Scorecard’,

Journal of Health Care Finance, Vol. 30, No. 2, pp. 37–64.Bradley, L. and R. Parker (2006), ‘Do Australian Public Sector Employees Have the Type of CultureThey Want in the Era of New Public Management?’, Australian Journal of Public Administration,

Vol. 65, No. 1, pp. 89–99.Butler, A., S.R. Letza and B. Neale (1997), ‘Linking the Balanced Scorecard to Strategy’, Long

Range Planning, Vol. 30, No. 2, pp. 242–53.Calantone, R.J., D.A. Griffith and G. Yalcinkaya (2006), ‘An Empirical Examination of a

Technology Adoption Model for the Context of China’, Journal of International Marketing, Vol.14, No. 4, pp. 1–27.

Campbell, D.T. and D.W. Fiske (1959), ‘Convergent and Discriminant Validation by the Multitrait-Multimethod Matrix’, Psychological Bulletin, Vo. 56, pp. 81–105.

Chan, Y.-C.L. (2006), ‘An Analytic Hierarchy Framework for Evaluating Balanced Scorecards of

Healthcare Organizations’, Canadian Journal of Administrative Sciences Vol. 23, No. 2, pp. 85–104.Chenhall, R.H. (2005), ‘Integrative Strategic Performance Measurement Systems, Strategic Alignment of Manufacturing, Learning and Strategic Outcomes: An Exploratory Study’, Accounting, Organizations and Society, Vol. 30, No.5, pp. 395–422.

Chin, W.W. (1999), ‘Structural Equation Modelling Analysis with Small Samples Using PartialLeast Squares’, in R.H. Hoyle (ed.), Statistical Strategies for Small Sample Research (SagePublications, Thousand Oaks, California).

——— (2007), Overview of the PLS Method (1997) (Available from http://disc-nt.cba.uh.edu/chin/PLSINTRO.HTM, accessed 2/9/07).

Chow, C.W., D. Ganulin, O. Teknika, K. Haddad and J. Williamson (1998), ‘The Balanced Score-card: A Potent Tool for Energizing and Focusing Healthcare Organization Management’,

Journal of Healthcare Management, Vol. 43, No. 3, pp. 263–80.

Davis, F.D. (1989), ‘Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology’, MIS Quarterly, Vol. 13, No. 3, pp. 318–40.———, R.P. Bagozzi and P.R. Warshaw (1989), ‘User Acceptance of Computer Technology: A

Comparison of Two Theoretical Models’, Management Science, Vol. 35, No. 8, pp. 982–1003.Davis, S. and T. Albright (2004), ‘An Investigation of the Effect of Balanced Scorecard

Implementation on Financial Performance’, Management Accounting Research, Vol. 15, No. 2,pp. 135–53.

De Geuser, F., S. Mooraj and D. Oyon (2009), ‘Does the Balanced Scorecard Add Value? EmpiricalEvidence on its Effect on Performance’, European Accounting Review, Vol. 18, No. 1, pp. 93–122.

Degeling, P. (2000), ‘Reconsidering Clinical Accountability: An Examination of Some DilemmasInherent in Efforts to Bolster Clinician Accountability’, International Journal of Health Planning and Management, Vol.15, No. 3, pp. 3–16.

———, J. Kennedy, M. Hill, M. Carnegie and J. Holt (1998), Professional Sub-cultures and Hospital Reform (The Centre for Hospital Management and Information Systems Research, University of New South Wales, Sydney).

DeLone, W.H. and E.R. McLean (1992), ‘Information Systems Success: The Quest for theDependent Variable’, Information Systems Research, Vol. 3, No. 1, pp. 60–95.

——— ——— (2003), ‘The DeLone and McLean Model of Information Systems Success: A Ten- Year Update’, Journal of Management Information Systems, Vol. 19, No. 4, pp. 9–30.

Ferguson, C. (1997), ‘The Effects of Microcomputers on the Work of Professional Accountants’, Accounting and Finance, Vol.37, No. 1, pp. 41–67.

Fishbein, M. and I. Ajzen (1975), Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research (Addison-Wesley Pub. Co, Reading, Massachusetts).

Fornell, C. and F.L. Bookstein (1982), ‘Two Structural Equation Models: LISREL and PLS Applied

to Consumer Exit-Voice Theory’, Journal of Marketing Research, Vol.19, No.4, pp. 440–52.———, P. Lorange and J. Roos (1990), ‘The Cooperative Venture Formation Process: A Latent

Variable’, Management Science, Vol. 36, No. 10, p. 1246.Hartwick, J. and H. Barki (1994), ‘Explaining the Role of User Participation in Information System

Use’, Management Science, Vol. 40, No. 4, pp. 440–65.





Hood, C. (1995), ‘The “New Public Management” in the 1980’s: Variations on a Theme’, Accounting,Organizations and Society, Vol. 20, No. 2/3, pp. 93–190.

Hrebiniak, L.G. (1974), ‘Effects of Job Level and Participation on Employee Attitudes andPerceptions of Influence’, Academy of Management Journal, Vol. 17, No. 4, pp. 649–62.

Hulland, J. (1999), ‘Use of Partial Least Squares (PLS) in Strategic Management Research: A

Review of Four Recent Studies’, Strategic Management Journal, Vol. 20, No. 2, pp. 195–204.Inamdar, N. and R.S. Kaplan (2002), ‘Applying the Balanced Scorecard in Healthcare ProviderOrganizations’, Journal of Healthcare Management, Vol. 47, No. 3, pp. 179–95.

Ittner, C.D. and D. Larcker (1998), ‘Innovations in Performance Measurement: Trends andResearch Implications’, Journal of Management Accounting Research, Vo. 10, pp. 205–38.

——— ——— and M.V. Rajan (1997), ‘The Choice of Performance Measures in Annual BonusContracts’, Accounting Review, Vol. 72, No. 2, p. 231.

Islam, M and F.W. Kellermanns (2006), ‘Firm- and Individual-Level Determinants of BalancedScorecard Usage’, Canadian Accounting Perspectives, Vol. 5, No. 2, pp. 181–207.

Jacobs, K. (2005), ‘Hybridisation or Polarisation: Doctors and Accounting in the UK, Germany and Italy’, Financial Accountability & Management, Vol. 21, No. 2, pp. 135–61.

Jarrar, N.S., M. Smith and C. Dolley (2007), ‘Perceptions of Preparers and Users of Accounting

Change: A Case Study in an Australian University’, Managerial Auditing Journal, Vol. 22, No.1,pp. 80–94. Johnsen, A. (2001), ‘Balanced Scorecard: Theoretical Perspectives and Public Management

Implications’, Managerial Auditing Journal, Vol. 16, No. 6, pp. 319–30. Jones, C.S. and I.P. Dewing (1997), ‘The Attitudes of NHS Clinicians and Medical Managers

Towards Changes in Accounting Controls’, Financial Accountability & Management, Vol. 13, No.3, pp. 261–79.

Joreskog K.G. and H. Wold (1982), ‘The ML and PLS Techniques for Modelling with Latent Variables: Historical and Comparative Aspects’, in H. Wold and K.G. Joreskog (eds.), SystemsUnder Indirect Observation: Causality, Structure, Prediction, Part1 (Amsterdam, North Holland).

Kaplan, R.S. and D.P. Norton (1992), ‘The Balanced Scorecard – Measures That DrivePerformance’, Harvard Business Review, Vol. 70, No. 1, pp. 71–79.

——— ——— (1996), The Balanced Scorecard: Translating Strategy intoAction (Harvard Business SchoolPress, Boston, Massachusetts).——— ——— (2001), The Strategy-Focused Organization: How Balanced Scorecard Companies Thrive in

the New Business Environment (Harvard Business School Press, Boston, Massachusetts).Karahanna, E., R. Agarwal and C.M. Angst (2006), ‘Reconceptualizing Compatibility Beliefs in

Technology Acceptance Research’, MIS Quarterly, Vol. 30, No. 4, pp. 781–804.Kasurinen, T. (2002), ‘Exploring Management Accounting Change: The Case of Balanced

Scorecard Implementation’, Management Accounting Research, Vol. 13, pp. 323–43.Kellermanns, F.W. and M. Islam (2004), ‘US and German-Based Activity Costing: A Critical

Comparison and System Acceptability Propositions’, Benchmarking: An International Journal, Vol. 11, No. 1, pp. 31–51.

Kim, S. (2002), ‘Participative Management and Job Satisfaction: Lessons for Management

Leadership’, Public Administration Review, Vol. 62, No. 2, pp. 231–41.Kloot, L. and J. Martin. (2000), ‘Strategic Performance Management: A Balanced Approach toPerformance Management Issues in Local Government’, Management Accounting Research, Vol.11, No. 2, pp. 231–51.

Le Grand, J., N. Mays and J. Dixon (1998), ‘The Reforms: Success or Failure or Neither?’ in J. LeGrand, N. Mays and J. Mulligan (eds.), Learning from the NHS Internal Market (King’s Fund,London).

Macdonald, M. (1998), ‘Using the Balanced Scorecard to Align Strategy and Performance inLong-Term Care’, Healthcare Management Forum, Vol. 11, No. 3, pp. 33–38.

Malmi, T. (2001), ‘Balanced Scorecard in Finnish Companies: A Research Note’, Management Accounting Research, Vol. 12, pp. 207–20.

Milani, K.W. (1975), ‘The Relationship of Participation in Budget-Setting to Industrial Supervisor

Performance and Attitudes’, The Accounting Review, Vol. 50, No. 2, pp. 278–84.Moulang, C. (2006), ‘Does “Style of Use” of Performance Measurements Systems Impact on

Individual Creativity? An Empirical Analysis’, Working Paper (Department of Accountingand Finance, Monash University, Melbourne).





New South Wales Department of Health (2008a) (Available at http://www.health.nsw.gov.au/aboutus/index.asp, Accessed 29/1/08).

——— (2008b), NSW Health System Performance Indicators August 2003 (Available athttp://www.health.nsw.gov.au/search.asp?qText=dashboardandGo=Goandcollection=wwwsiteandQueryMode=kword, Accessed 29/1/08).

New South Wales Hunter New England Health (2008a) (Available at http://www.HNEHhealth.nsw.gov.au/about_us/facts_and_figures, Accessed 29/1/08).——— (2008b), Health Service Strategic Plan towards 2010 (Available at http://www.health.

nsw.gov.au/pubs/2007/state_health_plan.html, Accessed 29/1/08).NSW Treasury (2007), Appropriation Bill 2007 (Available at http://www.treasury.nsw.gov.au/__

data/assets/pdf_file/0018/6363/bp5_general.pdf, Accessed 30/1/08).Norreklit, H. (2003), ‘The Balanced Scorecard: What is the Score? A Rhetorical Analysis of the

Balanced Scorecard’, Accounting Organisations and Society, Vol. 28, No. 6, pp. 591–619.Northcott, D. and N. France (2005), ‘The Balanced Scorecard in New Zealand Health Sector

Performance Management: Dissemination to Diffusion’, Australian Accounting Review, Vol. 15,No. 3, pp. 34–46.

Otley, D. (1999), ‘Performance Management: A Framework for Management Control Systems

Research’, Management Accounting Research, Vol. 10, No. 4, p. 363–82.——— (2003), ‘Management Control and Performance Management: Whence and Whither?’ The British Accounting Review, Vol. 35, pp. 309–26.

Pierce, B. and T. O’Dea (2003), ‘Management Accounting Information and the Needs of Managers:Perceptions of Managers and Accountants Compared’, The British Accounting Review, Vol. 35,No. 3, pp. 257–90.

Pineno, C.J. (2002), ‘The Balanced Scorecard: An Incremental Approach Model to Health CareManagement’, Journal of Health Care Finance, Vol.28, No. 4, pp. 69–80.

Phillips, L.A., R. Caltone and M.T. Lee (1994), ‘International Technology Adoption. BehaviourStructure, Demand Certainty and Culture’, Journal of Business and Industrial Marketing, Vol.9,No. 2, pp. 16–28.

Radnor, Z. and B. Lovell (2003), ‘Success Factors for Implementation of the Balanced Scorecard

in a NHS Multi-Agency Setting’, International Journal of Health Care Quality Assurance, Vol. 16,No. 2, pp. 99–108.Scapens, R.W. and J. Roberts (1993), ‘Accounting and Control: A Case Study of Resistance to

Accounting Change’, Management Accounting Research, Vol. 4, pp. 1–32.Singleton, R. and B.C. Straits (2005), Approaches to Social Research (4th ed., Oxford University Press,

New York).Speckbacher, G., J. Bischof and T. Pfeiffer (2003), Management Accounting Research, Vol. 14. No. 4,

pp. 361–88.Strebel, P. (1996), ‘Why do Employees Resist Change?’, Harvard Business Review (May-June),

pp. 86–92.Taylor, S. and P.A. Todd (1995), ‘Understanding Information Technology Usage: A Test of

Competing Models’, Information Systems Research, Vol. 6, No. 2, pp. 144–76.

ten Asbroek, A.H.A., O.A. Arah, J. Geelhoed, T. Custers, D.M. Delnoij and N.S. Klazinga (2004),‘Developing a National Performance Indicator Framework for the Dutch Health System’, International Journal for Quality in Health Care, Supplement, pp. 65–71.

Tuomela, T.-S. (2005), ‘The Interplay of Different Levers of Control: A Case Study of Introducinga New Performance Measurement System’, Management Accounting Research, Vol. 16, No. 3, pp.293–320.

Ukko, J., J. Tenhunen and H. Rantanen (2007), ‘Performance Measurement Impacts onManagement and Leadership: Perspectives of Management and Employees’, International

Journal of Production Economics, Vol. 110, Nos. 1 and 2, pp. 39–51. Vandenbosch, B. (1999), ‘An Empirical Analysis of the Association between the Use of Executive

Support Systems and Perceived Organizational Competitiveness’, Accounting, Organizations and Society, Vol. 24, No. 1, pp. 77–92.

Venkatesh, V. and F.D. Davis (2000), ‘A Theoretical Extension of the Technology AcceptanceModel: Four Longitudinal Field Studies’, Management Science, Vol. 46, No. 2, pp. 186–204.

———, M.G. Morris, G.B. Davis and F.D. Davis (2003), ‘User Acceptance of InformationTechnology: Toward A Unified View’, MIS Quarterly, Vol. 27, No. 3, pp. 425–78.





Villalbi, J.R., C. Casas, C. Borrell, J. Duran, L. Artazcoz, E. Camprubi, P. Rodriguez-Montuquinand J.M. Armengol (2007), ‘The Balanced Scorecard as a Management Tool in a PublicHealth Organization’, Gac Sanit,. Vol. 21, No. 1, pp. 60–65.

Vinz, V.E. (2007), ‘PLS Path Modeling Methodology and Software: Foundations and Re-cent Advances’ (Available at http://web.upmf-grenoble.fr/labsad/pages/ecas2007/pages/

Abstracts/abs11.pdf, Accessed 23/10/09). Voelker, K.E., J.S. Rakich and G.R. French (2001), ‘The Balanced Scorecard in HealthcareOrganizations: A Performance Measurement and Strategic Planning Methodology’, HospitalTopics, Vol. 79, No. 3, pp. 13–24.

Warren, W.H. (2006), ‘The Dynamics of Perception and Action’, Psychological Review, Vol. 113, No.2, pp. 358–89.

White, J.K. and R.A. Ruh (1973), ‘Effects of Personal Values on the Relationship BetweenParticipation and Job Attitudes’, Administrative Science Quarterly, Vol. 18, No. 4, pp. 506–14.

Wisniewski, M. and S. Olafsson (2004), ‘Developing Balaced Scorecards in Local Authorities: AComparison of Experience’, International Journal of Productivity and Performance Management, Vol.53, No. 7, pp. 603–10.

Woods, M. and S. Grubnig (2008), ‘Linking Comprehensive Performance Assessment to the

Balanced Scorecard: Evidence from Hertfordshire County Council’, Financial Accountability& Management, Vol. 24, No. 3, pp. 343–61.Wyatt, J. (2004), ‘Scorecards, Dashboards, and KPIs Keys to Integrated Performance Measure-

ment’, Healthcare Finance Management, Vol. 58, No. 2, pp. 76–80. Youngblood, A.D. and T.R. Collins (2003), ‘Addressing Balanced Scorecard Trade-off Issues

Between Performance Metrics Using Multi-Attribute Utility Theory’, Engineering Management Journal, Vol. 15, pp. 11–17.

Zelman, W.N., G.H. Pink and C.B. Matthias (2003), ‘Use of the Balanced Scorecard in HealthCare’, Journal of Healthcare Finance, Vol. 29, No. 4, pp. 1–16.


adoption of concept of balance scorecard in nsw health

Documents