Chlorophyll Definition and Role in Photosynthesis

Chlorophyll is the name given to a group of green pigment molecules found in plants, algae, and cyanobacteria. The two most common types of chlorophyll are chlorophyll a, which is a blue-black ester with the chemical formula  C55H72MgN4O5, and chlorophyll b, which is a  dark green ester with the formula C55H70MgN4O6. Other forms of chlorophyll include chlorophyll c1, c2, d, and f. The forms of chlorophyll have different side chains and chemical bonds, but all are characterized by a chlorin pigment ring containing a magnesium ion at its center. Key Takeaways: Chlorophyll Chlorophyll is a green pigment molecule that collects solar energy for photosynthesis. Its actually a family of related molecules, not just one.Chlorophyll is found in plants, algae, cyanobacteria, protists, and a few animals.Although chlorophyll is the most common photosynthetic pigment, there are several others, including the anthocyanins. The word chlorophyll comes from the Greek words chloros, which means green, and phyllon, which means leaf.  Joseph Bienaimà © Caventou and Pierre Joseph Pelletier first isolated and named the molecule in 1817. Chlorophyll is an essential pigment molecule for photosynthesis, the chemical process plants use to absorb and use energy from light. Its also used as a food coloring (E140) and as a deodorizing agent. As a food coloring, chlorophyll is used to add a green color to pasta, the spirit absinthe, and other foods and beverages. As a waxy  organic compound, chlorophyll is not soluble in water. It is mixed with a small amount of oil when its used in food. Also Known As: The alternate spelling for chlorophyll is chlorophyl. Role of Chlorophyll in Photosynthesis The overall balanced equation for photosynthesis is: 6 CO2 6 H2O → C6H12O6 6 O2 where carbon dioxide and water react to produce glucose and oxygen. However, the overall reaction doesnt indicate the complexity of the chemical reactions or the molecules that are involved. Plants and other photosynthetic organisms use chlorophyll to absorb light (usually solar energy) and convert it into chemical energy. Chlorophyll strongly absorbs blue light and also some red light. It poorly absorbs green (reflects it), which is why chlorophyll-rich leaves and algae appear green. In plants, chlorophyll surrounds photosystems in the thylakoid membrane of organelles called chloroplasts, which are concentrated in the leaves of plants. Chlorophyll absorbs light and uses resonance energy transfer to energize reaction centers in photosystem I and photosystem II. This happens when energy from a photon (light) removes an electron from chlorophyll in reaction center P680 of photosystem II. The high energy electron enters an electron transport chain. P700 of photosystem I works with photosystem II, although the source of electrons in this chlorophyll molecule can vary. Electrons that enter the electron transport chain are used to pump hydrogen ions (H) across the thylakoid membrane of the chloroplast. The chemiosmotic potential is used to produce the energy molecule ATP and to reduce NADP to NADPH. NADPH, in turn, is used to reduce carbon dioxide (CO2) into sugars, such as glucose. Other Pigments and Photosynthesis Chlorophyll is the most widely recognized molecule used to collect light for photosynthesis, but its not the only pigment that serves this function. Chlorophyll belongs to a larger class of molecules called anthocyanins. Some anthocyanins function in conjunction with chlorophyll, while others absorb light independently or at a different point of an organisms life cycle. These molecules may protect plants by changing their coloring to make them less attractive as food and less visible to pests. Other anthocyanins absorb light in the green portion of the spectrum, extending the range of light a plant can use. Chlorophyll Biosynthesis Plants make chlorophyll from the molecules glycine and succinyl-CoA. There is an intermediate molecule called protochlorophyllide, which is converted into chlorophyll. In angiosperms, this chemical reaction is light-dependent. These plants are pale if they are grown in darkness because they cant complete the reaction to produce chlorophyll. Algae and non-vascular plants dont require light to synthesize chlorophyll. Protochlorophyllide forms toxic free radicals in plants, so chlorophyll biosynthesis is tightly regulated. If iron, magnesium, or iron are deficient, plants may be unable to synthesize enough chlorophyll, appearing pale or chlorotic. Chlorosis may also be caused by improper pH (acidity or alkalinity) or pathogens or insect attack.

Higher Education Is Feeling The Financial Pinch - 1125 Words

Higher education is feeling the financial pinch. Public higher education programs have seen a decrease in government funding. Adding to the burden, is financial pressure to keep tuition rates down as well as aging brick-and-mortar facilities, as well as career fields and technology evolving rapidly. According to Jeff Dennen (as cited by Selingo, 2013) â€Å"Expenses are growing at such a pace that colleges don’t have the cash or the revenue to cover them for much longer†. Higher tuition rates for out of state students and endowments are not going far in making up the financial shortfall. As a result, many high education organizations have had to get creative in order to exist. One of those ways has been to outsource faculty (Driscoll, 2012). Another, has been to impose fees on students, some of which stem from technology as the collegiate environment transitions from brick-and-mortar to virtual. As a result, some of the financial burden from higher education organization shifts to student which allows colleges to remain active and functional while providing the experiences and convenience 21st century learners need. Additionally, the benefits of these fees may outweigh the cost for students. Increasing Costs for Higher Education Organizations The financial pinch felt by many high education organizations is caused by decreased income and increased costs. Mounting pressure from federal and state governments to keep tuition prices low as well as lowering funding levels haveShow MoreRelatedOrganizational Management and Operations Paper1690 Words   |  7 Pagesor federal agencies. Just as people are different; so too are supervisory roles, mission statements and leaderships. Every policing organization has its common goals to adhere to such as, crime prevention, traffic, crime control, safety, and education, to name a few examples. 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Budgeting Practices and Performance in Small Healthcare Businesses Free Essays

string(115) " adoption of budgets and the extent of their use for our sample of small Australian primary healthcare businesses\." Management Accounting Research 21 (2010) 40–55 Contents lists available at ScienceDirect Management Accounting Research journal homepage: www. elsevier. com/locate/mar Budgeting practices and performance in small healthcare businesses Robyn King a , Peter M. We will write a custom essay sample on Budgeting Practices and Performance in Small Healthcare Businesses or any similar topic only for you Order Now Clarkson a,b,? , Sandra Wallace c a b c UQ Business School, The University of Queensland, Brisbane 4072, Australia Faculty of Business Administration, Simon Fraser University, Burnaby, Canada V5A 1S6 Department of Accounting and BIS, The University of Melbourne, Victoria 3010, Australia a r t i c l e i n f o a b s t r a c t We present evidence linking primary healthcare business characteristics, budgeting practices, and business performance. Based on a sample of 144 responses from a survey of members of the Australian Association of Practice Managers (AAPM), we ? nd that factors identi? ed by contingency-based research are useful for predicting a business’s budgeting practices. Speci? cally, we ? nd the adoption of written budgets to be related to size and structure, and for businesses using written budgets, the extent of use is related to business structure, strategy and perceived environmental uncertainty. Finally, we ? nd evidence of a relationship between budgeting practice and performance. Here, we initially ? nd a business’s performance to be positively associated with the use of written budgets. More re? ned tests of the â€Å"? t† between business contingency factors and extent of operating budget use then provide evidence of a positive association between the extent of â€Å"? t† and performance. Crown Copyright  © 2009 Published by Elsevier Ltd. All rights reserved. Keywords: Budgeting SME Healthcare businesses . Introduction This study investigates the relationship between contextual factors identi? ed from contingency-based research, the adoption and extent of use of budgets, and business performance within the Australian primary healthcare setting. 1 We focus on budgets because they are considered to be one of the main management control systems This study is based largely on Robyn King’s Honours thesis completed in the UQ Business School at the University of Queensland. The authors would like to thank the editor and the two anonymous referees, as well as workshop participants at Monash and Swinburne Universities, the 2007 AFAANZ Annual Conference, and especially Aldonio Ferreira, Axel Schultz, Shannon Anderson and Julie Walker for comments on an earlier version of the manuscript. ? Corresponding author at: UQ Business School, The University of Queensland, Brisbane 4072, Australia. Tel. : +61 7 3346 8015; fax: +61 7 3365 6788. E-mail address: P. Clarkson@business. uq. edu. au (P. M. Clarkson). Primary healthcare is the initial care of a patient as an outpatient excluding diagnostic testing; tertiary healthcare is that provided in a hospital setting. (MCS) in organisations, have been found to be the earliest MCS that a business adopts, and continue to receive signi? cant attention in the research literature and in teaching material (e. g. , Davila and Foster, 2005, 2007; Sandino, 2007). We select the Australian primary healthcare sector as our experim ental setting both because of its importance socially and economically, and because it is likely to be comprised of businesses that vary broadly in their budgeting practices. Contingency-based research proposes that there is no single MCS suitable for all businesses. Instead, the suitability of a particular MCS is argued to be contingent upon characteristics of a business including its size, strategy, structure, and also management’s perceptions of the uncertainty of the environment within which the business operates. We begin by examining the relationship between a business’s budgeting practices and these four contextual factors. In so doing, we view the development of a budgeting practice as consisting of two stages, the initial decision regarding adoption and the subsequent decision regarding extent of use. Here, the term ‘adoption’ re? ects the decision by a business to use a formal process to project its future 1044-5005/$ – see front matter. Crown Copyright  © 2009 Published by Elsevier Ltd. All rights reserved. doi:10. 1016/j. mar. 2009. 11. 002 R. King et al. / Management Accounting Research 21 (2010) 40–55 41 ?nancial performance (Davila and Foster, 2005). Alternatively, the term ‘extent of use’ refers to both the number of different types of budgets the business uses and the frequency of their use. In our analysis, we develop arguments for, and investigate, these two stages separately. We then turn to consider the relationship between a business’s budgeting practice and its performance. The relative â€Å"? t† of the business’s MCS with its contingency factors is argued to impact on performance, with performance increasing with the degree of â€Å"? t† (Chenhall, 2003). Thus, ceterus paribus, businesses using a practice which does not â€Å"? t†, whether by â€Å"over-budgeting† or â€Å"under-budgeting†, are expected to experience weaker performance. We argue that not all of our sample businesses are likely to exhibit â€Å"best budgeting practice† because of the dif? ulties associated with identifying and implementing best practice, and the discontinuous nature of upgrades (Luft, 1997). We examine the relation between â€Å"? t† and performance using the method proposed by Ittner and Larcker (2001) and classi? ed as a Cartesian/Contingency approach (Gerdin and Greve, 2004). Degree of â€Å"? t† is measured as the difference between the extent of budget use and that predicted by the business’s contingency factors. This approach assumes that at any point, not all businesses will in fact have implemented their optimal practice. To conduct our investigation, a written survey of 988 members of the Australian Association of Practice Managers (AAPM) was undertaken. In brief, we ? nd that larger, more decentralised healthcare businesses are more likely to adopt written budgets. Further, for the subset of businesses that use written budgets, we ? nd that the extent of budget use is positively associated with structure (decentralisation) and strategy (cost leadership), and negatively associated with perceived environmental uncertainty (dynamism). Finally, we document a relationship between choice of budgeting practice and performance. Here, we initially ? nd performance to be positively associated with the use of written budgets. More re? ned tests then provide evidence of a positive association between the degree of â€Å"? t† and performance. Our study makes several contributions. First, we present evidence that contingency factors do indeed provide insights into both the adoption of budgets and the extent of their use for our sample of small Australian primary healthcare businesses. You read "Budgeting Practices and Performance in Small Healthcare Businesses" in category "Papers" Interestingly, the results suggest that size and structure capture the business’s initial decision to adopt a formal budgeting practice. However, once a business has adopted a formal practice, strategy, structure, and perceived environmental uncertainty appear to be the primary determinants underlying the subsequent decision regarding the extent of budget use. We also present evidence that the â€Å"? t† of our sample business’s budgeting practices is associated with performance. To our knowledge, there has been relatively little empirical evidence on this relationship documented in the literature to date. Second, contingency-based research has predominantly been conducted in the large business sector. We extend this work by examining a small business setting. We argue that our setting has the advantage of allowing for an examination not only of the extent of budget use but also of the initial decision to adopt a budgeting practice. In con- junction, it also provides an opportunity to examine more closely the different underlying theoretical constructs of size that the two most commonly used proxies, gross fees and full-time equivalent employees, may be capturing. Finally, from a practical perspective the healthcare sector is under continuing pressure to increase its ef? iency. This study contributes by examining the contexts in which the use of budgets is associated with enhanced performance in primary healthcare. The results should be of bene? t to both practitioners and those who advise practitioners on MCS design. The remainder of this paper is structured as follows: Section 2 presents background material; Section 3 describes our experimental setting; Section 4 presents the hyp otheses, Section 5 the method and Section 6 the results; and Section 7 provides a summary and conclusions. 2. Background MCS have been de? ed as all devices and systems that businesses use â€Å"to ensure that the behaviours and decisions of their employees are consistent with the organisation’s objectives and strategies† (Malmi and Brown, 2008). We focus on a business’s decision regarding its budgeting practices. Budgets are considered an MCS because they can in? uence the behaviours and decisions of employees by translating a business’s objectives into plans for action, communicating the objectives, and providing a benchmark against which to assess performance. We view budgets as both an important and appropriate focus given that they are considered to be one of the main MCS in organisations, have been found to be the earliest system adopted in startup businesses, and continue to receive signi? cant attention in the research literature and in teaching material (Davila and Foster, 2005, 2007; Sandino, 2007). Within the MCS literature, the term ‘budgeting’ is used to refer to a broad range of topics (see Chapman et al. (2007) for a review). In this study, we de? e a budget as a forward looking set of numbers which projects the future ? nancial performance of a business, and which is useful for evaluating the ? nancial viability of the business’s chosen strategy or deciding whether changes to the overall plan are required (Davila and Foster, 2005). Budgets have been identi? ed as playing a number of roles which include making goals explicit, coding learning, facilitating co-ordination, promoting accountability, facilitating control, and contracting with external parties (Davila et al. 2009). Bene? ts of budgeting include increasing ef? ciency through planning and co-ordination, supporting both control and learning through the comparison of actual results with plans, and more globally â€Å"the ability to weave together all the disparate threads of an organisation into a comprehensive plan that serves many purposes† (Hansen and Otley, 2003). Given these various roles and potential bene? ts, one might expect all businesses to adopt a formal budgeting practice. In fact, this view appears to underlie much of the empirical MCS research predominantly conducted in a large business setting, as it is often assumed that large businesses will already have formal MCS that can be readily examined (Chenhall, 2003). Here, the focus has typically 42 R. King et al. / Management Accounting Research 21 (2010) 40–55 been on the adoption and performance implications of speci? c MCS â€Å"innovations† like activity based costing (ABC) (Ittner et al. , 2002). However, it is argued in the MCS literature that a rational adoption decision should require an evaluation of the associated costs as well as the bene? s (Davila and Foster, 2005). Costs of a formal MCS include the easily measured out of pocket costs associated with implementing and operating the system (Hansen and Otley, 2003; Hansen and Van der Stede, 2004). Other costs that are not so easily measured are the possibility that budgets create rigidity thereby limiting co-operation and creati ve response, over-emphasise short-term cost control and top down authority, encourage gaming, and de-motivate employees (Hansen and Otley, 2003; Hansen and Van der Stede, 2004). Further, arguably the bene? s and costs associated with adopting a formal budgeting practice will not be the same for every business but will depend on business-speci? c contextual factors. Thus, ex ante, it is not clear that adoption of a formal budgeting practice is necessarily a rational outcome for all businesses. Our study distinguishes itself from the majority of the MCS literature by focusing on the small business setting, speci? cally the primary healthcare business sector. Within this sector, we argue that it is likely that for some businesses, the costs of budgeting will outweigh the perceived bene? s whereas the converse will likely be true for others. Thus, we argue that this setting provides the opportunity to gain insights into both the decision regarding the adoption of budgets as well as the subsequent decision regarding the extent of their use. We investigate the contextual factors that delineate the costs and bene? ts associated with budgeting from a contingency framework perspective. Our initial focus is on the contextual factors that differentiate adopters from non-adopters. We then consider those factors that drive the decision regarding the extent of budget use and conclude with an investigation into the relationship between the ? t of a business’s budgeting practice with its contextual factors and its performance. In adopting a contingency framework perspective, we acknowledge that research referring to contingency theory has been subject to the criticism that contingency is a general idea rather than a theory as â€Å"there is no a priori intuition of its own as to what the pertinent factors are and as to their likely consequences† (Spekle, 2001). In this study, we do not consider contingency as a theory but rather as a framework for investigating identi? ed factors for which we have a priori intuition based on other organisational, economic and sociological theories. A further criticism of the contingency-based literature is its simplistic nature of investigating one contextual factor or MCS at a time (Fisher, 1995). In the face of this criticism, some studies have begun investigating multiple contextual variables simultaneously (Hansen and Van der Stede, 2004; Cadez and Guilding, 2008). We also consider multiple contextual factors, speci? cally those identi? ed in Chenhall’s (2003) review of the MCS literature since 1980. This review con? rms environment, technology,3 structure and size as â€Å"the descriptors of the fundamental generic elements of context†. Strategy is also included as it emerged in the 1980s as an important factor that in? uences the design of MCS (Lang? eld-Smith, 1997). Finally, contingenc y-based research has its roots in sociology. The underlying premise of sociology is that humans are boundedly rational and satis? cing (March and Simon, 1958). Bounded rationality can impede decision-making as not all possible alternatives are known with certainty at a given point in time. One identi? ed role of MCS is to assist managers in decision-making (Lawrence and Lorsch, 1967). However, the decision on the optimal MCS is itself restricted by bounded rationality, as well as the personal incentives of the manager. Thus, while conceptually organisations may be expected to use the optimal MCS, this is not always possible. Businesses facing the same contextual factors may therefore choose different MCS, with the differences re? cted in their performance. In our study, we investigate the effect of a mismatch between the contextual variables and the extent of use of budgets on performance. 3. Experimental setting We adopt the Australian primary healthcare sector as our experimental setting for two reasons. First, we view it as an inherently interesting research setting in its own right, given its importance both socially and economically. S econd, we believe it to be an ideal setting within which to conduct an investigation into budgeting because as argued below, it is a sector within which there are likely both signi? ant incentives and disincentives to budget. Thus, this setting provides the advantages of the controls that arise from working within a single industry while at the same time, one within which variation in budget use can reasonably be expected. 4 In detail, the Australian primary healthcare sector plays a uniquely important role in terms of both the services it provides and its place in the economy. In terms of services, it represents the gateway through which patients most typically enter the health system. Initial contact with For a sample of 57 organisational units, all with budgeting practices in place, Hansen and Van der Stede (2004) undertook an exploratory study focusing on four contextual factors (strategy, structure, environment, and size) as possible antecedents to identi? ed reasons to budget. Alternatively, for a sample of large businesses, mostly manufacturing from Slovenia, Cadez and Guilding (2008) ? nd that superior performance results from an appropriate match between the contingent factors strategy, size, and market orientation, and strategic management accounting applications. We initially considered technology as an additional contextual variable but decided against its inclusion given our choice of experimental setting. Recent advances in medical technology have impacted on diagnostic specialties such as pathology and radiology (specialties not included in our study) to a much greater degree than primary healthcare. With the exception of three opthalmologists, the only specialists included in the survey were those that provide outpatient services from private rooms and like GP’s, still largely rely on their skill and basic instruments such as the stethoscope. From an administrative and medical records perspective, the use of technology is widespread, with computers being used in 89. 8% of all GP practices and 94. 5% of all specialist practices in 2002 (ABS, 2002). 4 In this regard, Merchant (1981) suggested that a desirable extension of budgeting studies guided by contingency frameworks would be to collect data from samples chosen to magnify the variation on the dimensions of interest while controlling for the many possible interacting factors which obscure or distort the ? ndings. R. King et al. Management Accounting Research 21 (2010) 40–55 43 the health system is through a general medical practitioner (GP). For specialised care, patients are then referred to specialist medical practitioners. GPs and specialists provide this primary care from private consulting rooms and refer on to other providers for diagnostic tests. 5 Economically, the sector contributed 1. 71% of GDP in 2007 (AIHW, 2008). 6 Faced with rising costs, doctor sh ortages and increasing waiting times, primary healthcare businesses are increasingly under pressure to become more ef? ient (Department of Health and Ageing, 2005). Here, budgeting has been identi? ed as a management accounting tool that enhances ? nancial performance and improves ef? ciency (Davila and Foster, 2007). Further, the majority of the existing management accounting research has been focussed on in-hospital care so little is known about MCS in the outpatient setting (Abernethy et al. , 2007), with the exception of a recent U. S. -based study of the relation between performance-based compensation and ownership of primary healthcare businesses (Ittner et al. 2007). Taken together, these facts reinforce our view that the primary healthcare sector within Australia is an important and potentially fruitful setting for the conduct of MCS research generally, and research into budgeting practices more narrowly. More directly to the current investigation, we seek an experimental se tting with variation in budget use, including the presence of both adopters and non-adopters. We believe that a number of factors conspire to make the primary healthcare sector a reasonable choice, ex ante. First, the small business sector has been argued to have lower levels of formal planning and control (Chenhall and Lang? eld-Smith, 1998). As such, it might also be expected to include non-adopters. 7 In Australia, primary healthcare is largely provided by private businesses owned by the doctors working in the business as sole traders, in partnership, or through a company. Management of these businesses has traditionally been by the owner, although there now appears to be a trend towards the delegation of management to practice managers. 8 These primary healthcare businesses typically have fewer than 50 employees and would thereby most often be classi? ed as small businesses according to the OECD de? nition (Holmes and Kelly, 1989). 9 Second, prior research also ? nds that rapid growth small-to-medium enterprises (SME) provide more extensive future-oriented ? nancial reporting than matched non-growth concerns (McMahon, 2001). Arguably, small primary healthcare businesses are less likely to be rapidly growing as there is currently an undersupply of primary healthcare workers due to an ageing workforce and restrictions on training places. 0 These limited organic growth prospects further our expectations of ? nding non-adopters. Third, ? rms within the service sector typically do not need to account for stock, thereby eliminating one driving force behind the use of sophisticated MCS. Finally, research on small and family business supports the view that necessary management skills are required before planning can be initia ted (Gibb and Scott, 1985). Since there is a lower likelihood that primary healthcare owners, the majority of whom are doctors, have formal training in MCS, non-adoption is even more likely relative to other service sectors. Conversely, there are also economic incentives to adopt budgets. As noted, primary healthcare businesses are under increasing pressure to become more ef? cient. The National Health Performance Committee has adopted a framework speci? cally designed for measuring healthcare system performance, with one of the identi? ed components being ef? ciency. In response, a number of institutions and private management consultants now offer education on managing primary healthcare practices that includes training on budgeting. 1 In conjunction, there is the added incentive to undertake these education programs in that continuing education is a requirement of the accreditation process for GP practices. Further, only accredited practices can access Practice Incentive Payments (PIP) from the Federal Government which can represent signi? cant additional revenue. 12 5 There is currently no Medicare funding for diagnostic services provided within the primary healthcare setting and so, these businesses do not invest in the associated technology. The specialist practices included in the study were the private rooms of orthopaedic surgeons, opthalmologists, ermatologists, gynaecologists, and gastroenterologists. These specialists conduct initial consultation and post operative follow up from their private rooms but perform procedures in hospitals or day surgeries for which they have visitation rights, using equipment supplied by the hospital/day surgery where a fee is charged directly to the patient for its use. 6 Overall, the healthcare industry contributed 8. 98% of GDP in 2006–2007, with 19% of recurrent health expenditure on medical services provided by GP’s and specialists. In 2006–2007, Medicare paid $4029. million for GP services, representing an average 4. 93 items per capita (AIHW, 2008). 7 In contrast, international studies of formal budget use have focused on large businesses, ? nding the vast majority use annual ? xed budgets (Horngren et al. , 2006 ). Australian evidence is consistent, with 97% of large businesses found to use budgets (Chenhall and Lang? eld-Smith, 1998). 8 The exact extent to which management is being delegated to practice managers is uncertain given a lack of studies into the prevalence or role of practice managers (Department of Health and Ageing, 2005). There were 9600 private GP practices operating in Australia at the end of June 2002. Of these, 68. 5% were single practitioner practices employing 2. 9 persons on average, and only 100 employed more than 10 practitioners. At the same time, there were 9864 private specialist practices, 89. 7% of which were solo specialist practices employing an average 3. 2 persons and only 19. 2% had greater than 10 practitioners (ABS, 2002). 10 There has been a recognised unfavourable long-term trend since 1999 towards an increasing percentage of primary care practitioners aged 55 years or over. In conjunction, the World Health Organisation (WHO) predicts a global workforce shortage of 4. 25 million health workers over the next decade (Cresswell, 2007). 11 The 2008–2009 Federal Government Budget proposal includes administered Program 5. 3—‘Primary Care Policy, Innovation and Research’ which among other things, â€Å"funds initiatives to improve service delivery and help GPs access current best business practice. † As a part of their response, the Australian Medical Association (AMA) and the AAPM have made available speci? cally tailored business education programs for healthcare managers. 2 The Royal Australian College of General Practitioners (RACGP) standards for general practice include: â€Å"Our administrative staff can describe (and there is evidence of) training undertaken in the past 3 years that is relevant to their role in our practice. † The practice manager is speci? cally mentioned in the standard, as is the term practice management training. In order to access Practice Incentive Payments, GP practices must have complied with the RACGP standards. In 2007, 80% of GP practices were accredited (AIHW, 2008). By way of context, an accredited practice with 44 R. King et al. / Management Accounting Research 21 (2010) 40–55 However, notwithstanding, for some businesses, given their lack of size and sophistication, these incentives are unlikely to outweigh the costs of budgeting which include the initial investment in software, skills, and the added labour hours. 4. Hypothesis development 4. 1. Overview This study investigates the relationship between factors identi? ed from contingency-based research, the adoption and extent of use of budgets, and business performance. The speci? contextual factors we consider are size, structure, strategy, and perceived environmental uncertainty (Chenhall, 2003). We argue, based on how each identi? ed factor is expected to impact both a business’s needs for and thereby the bene? ts it derives from budgeting, and also its ability to meet the costs of a budgeting practice, that the four contingency factors play different roles relative to the tw o stages of the budgeting decision. Speci? cally, we predict that a business’s adoption decision primarily related to its size and to a lesser extent, its structure (decentralisation). For businesses that make the threshold adoption decision, we predict that those that are more decentralised, employ a cost leadership strategy, and for which management perceives a lower level of environmental uncertainty will use budgets to a greater extent. Quite clearly, however, the roles played by the various factors ultimately remain an empirical question and as such, we give consideration to each when we empirically model the two stages of the budgeting decision. Finally, we predict that the match between the contextual factors and extent of budget use will be re? ected in business performance. These predictions are formalised below. . 2. Determinants of budgeting practice 4. 2. 1. Size The construct of size has frequently been viewed as re? ecting two dimensions, complexity and availability of resources, with both argued to be increasing with size (Fredrickson and Mitchell, 1984; Mintzberg, 1994). While small, single-business organisations can often be controlled with largely informal mechanisms such as direct supervision and oral communications, larger organisations require more formal controls as the increased complexity associated with a larger number of employees creates problems in social control, communication, and co-ordination (Lawrence and Lorsch, 1967). Here, Davila (2005) argues, following Levitt and March (1988), that to regain ef? ciency in managing the organisation, co-ordination and control mechanisms are formalised with the objective of coding and documenting organisational learning and reducing the demand that routine activities impose on the management team’s time. Further, in terms of a business’s ability to invest in a formal budgeting practice, it is widely accepted that larger businesses are better positioned given their greater resources, ? nancial and otherwise. Larger businesses not only have the resources required to acquire software and skills but they can also more ef? ciently achieve these administrative tasks through economies of scale and the greater technical specialisation of their employees (Merchant, 1981). Chenhall (2003) ? nds that size has been considered as a contextual variable in only a few MCS studies as most examine relatively large businesses. Such a ? nding ? ts well with Banbury and Nahapiet’s (1979) argument that there should only be a relationship between resource availability and the introduction of formal systems in organisations of relatively small size. Consistent with these types of arguments, small business studies reveal size as in? uencing the acquisition and preparation of accounting information including budgets (Holmes and Nicholls, 1989; McMahon, 2001). Further, evidence from longitudinal studies of startup businesses suggests that size in? uences the decision to adopt operating budgets, with larger ? rms adopting the budgets sooner (Davila and Foster, 2005, 2007). They ? nd that when the business is small, control and co-ordination happens through frequent informal interactions but that the ef? iency of an informal system requiring direct contact with employees rapidly decreases with increasing size, thereby making it more ef? cient to use a formal control system. In the primary healthcare setting, we view the ? xed costs associated with the adoption of a budgeting practice to be signi? cant and thus, following the arguments of Davila and Foster (2005), propose size as a determining contingency factor underlying the adoptio n decision. In this sector, businesses are required to comply with substantive â€Å"red tape† that places onerous demands on their resources (Productivity Commission, 2003). As such, it is likely that only larger businesses have both the need for and the resources to devote to budgeting. Smaller practices are unlikely to be able divert resources away from their primary revenue generating clinical activities. Thus, we predict a positive relationship between business size and use of budgets. However, we also argue consistent with Banbury and Nahapiet (1979) that once a business has reached a critical size and uses a budget, size is unlikely to play a signi? cant further role in the determination of budgeting practice. Thus, our ? st hypothesis, expressed in the alternative, is: H1 . The adoption of written budgets by primary healthcare businesses is positively associated with business size. 4. 2. 2. Structure The structure of a business relates to â€Å"the formal speci? cation of roles for organisational members or tasks for groups to ensure that the activities of the organisation are carried out† (Chenhall, 2003). While two components, differentia tion and integration, have been identi? ed in the literature, we focus only on differentiation because of the small size of our sample businesses. Differentiation is de? ed as the extent to which managers act as quasi-owners and is achieved through decentralisation of authority (Lawrence and Lorsch, 1967). A centralised busi- 4 FTE urban GPs would receive $60,000 per annum from PIP (Medicare, 2009). R. King et al. / Management Accounting Research 21 (2010) 40–55 45 ness structure is characterised by decision-making that is restricted to owners and upper management whereas a decentralised business delegates decision-making to lower levels of management and operational staff. Given the closer links between the ownership and control of the business, ecision-making in centralised businesses should require relatively fewer MCS. Herein, existing evidence reveals centralised businesses as having relatively few administrative controls and less sophisticated budgets while decentralise d businesses have more formal controls (Bruns and Waterhouse, 1975; Merchant, 1981). We thus argue that structure has the potential to play a role in a business’s initial decision to adopt a budgeting practice, although we view its role as secondary to size since it is unlikely that the business’s ability to meet the ? ed costs associated with a budget practice will be directly related to its structure. Further, we argue that for small healthcare businesses that have reached the threshold size and use budgets, structure also has the potential to play a role in its subsequent decision as to the extent of use. As the business becomes more differentiated, decentralisation increases and thereby so does the need for formal MCS (Lawrence and Lorsch, 1967; Merchant, 1981). Our second hypothesis, expressed in two parts and in the alternative, is then:13 H2a . The adoption of written budget by primary healthcare businesses is positively associated with business structure (decentralisation). H2b . The extent of written budget use by primary healthcare businesses which opt to use written budgets is positively associated with business structure (decentralisation). 4. 2. 3. Strategy Business strategy, de? ned as how a business chooses to compete within its particular industry (Lang? eld-Smith, 1997), has been the focus of much of the research on MCS as opposed to corporate or operational strategy (Chenhall, 2003). While there are a number of different typologies of business strategy, we use Porter’s typology which focuses on cost leadership and product differentiation strategies. Porter’s cost leaders are characterised by competitive prices, consistent quality, ease of purchase, and a relatively restricted product selection. In contrast, differentiators offer the market something perceived as unique. Different types of MCS will be suited to different strategies due to their differing information and feedback requirements. Cost leadership strategies are argued to require speci? operating goals and budgets to facilitate cost containment at an operational level (Chenhall and Morris, 1995). Alternatively, product differentiator strategies would require more outward focussed, broadscope, MCS to collect infor- mation on competitors for planning purposes (Simons, 1987). Since primary healthcare businesses have constraints on the total number of services they can provide such as opening hours and the number of medical practitioners, a cost leadership strategy should require that tighter cost controls be in place in order to maintain overall pro? ability. In contrast, a product differentiator strategy operating with higher margins under the same constraints should require fewer controls. While we do not expect strategy to be determinative of the threshold decision to use budgets as it is unlikely to either affect the business’s ability to meet the initial ? xed costs or contribute suf? ciently to the business’s primary need for a budgeting practice, based on the arguments above we do expect it to impact on the desire to invest in marginal costs associated with a greater extent of budget use. Thus, formally, our third hypothesis, expressed in the alternative, is: H3 . For small healthcare businesses, which opt to use written budgets, those following a cost leadership strategy will use budgets to a greater extent than those following a product differentiation strategy. 4. 2. 4. Perceived environmental uncertainty (PEU) PEU is de? ned as a situation where managers perceive elements of the environment to be uncertain, with uncertainty distinguished from risk â€Å"as uncertainty de? nes situations in which probabilities are not attached† (Chenhall, 2003). In a general sense, PEU is seen to be an important contextual factor in the design of MCS because increased PEU makes managerial planning and control more dif? cult (Lawrence and Lorsch, 1967). PEU is, however, a general term and a number of researchers have provided more speci? c classi? cations of the environment (Waterhouse and Tiessen, 1978; Ouchi, 1979). In this study, we focus on the two most commonly researched elements of PEU, the dynamic nature of the environment (dynamism) and the level of competition (hostility). Contingency-based research in large business has found that greater dynamism is associated with a need for more externally focussed, broad scope and timely information (Chenhall and Morris, 1995). Planning becomes more dif? cult in more dynamic conditions as probabilities cannot as easily be attached to future events and controls such as static budgets may quickly become outdated. Thus, greater informal communication is required for effective decision-making and formal controls are less bene? cial or desirable (Chapman, 1997). Alternatively, large business research focussed n hostility has found that businesses facing greater competition rely on more formal controls and emphasise budgets (Khandwalla, 1972). Thus, in addition to con? rming PEU as an important contextual factor, these ? ndings also reveal the importance of specifying the dimension of interest (Chenhall, 2003). Consistent with the research on dynamism in large business but in contrast with that on hostility, Matthews an d Scott (1995) ? nd for small businesses, the sophistication of planning decreases with both increased dynamism and increased hostility. They argue from an economic perspective that for small businesses, the more uncertain 13 While it could be argued that it would be impractical for a small business consisting of a single medical practitioner and few administrative staff to have a decentralised structure, given the heavy demands of clinical work on the medical practitioner’s time, decentralisation is possible to the extent that operational and ? nancial decision-making is delegated to employees. This was con? rmed in discussions in the pilot study. 46 R. King et al. Management Accounting Research 21 (2010) 40–55 the environment the less likely the manager is to expend scarce resources on budgets with an unproven effect on performance. The rational manager trying to meet the fundamental goal of making a pro? t will weigh up the bene? ts against the costs associated with budgeting. Based on these arguments and ? ndings, we propose that as with strategy, neither dimension of PEU is likely to impact the mana ger’s threshold decision to adopt a budget as it does not directly impact on the ability to meet the costs. However, for businesses that have already identi? ed the need and ability to budget, both dynamism and hostility will impact on the decision to incur the added marginal costs of increased budget use. Consistent with Matthews and Scott (1995) and the large organisation literature, given the relatively small nature of our sample businesses and the likely resource constraints that they face, we predict a negative association between the PEU dimension of dynamism and the extent of use. 14 However, contrary to the large rganisation literature but consistent with Matthews and Scott (1995), we also predict a negative association between the PEU dimension of hostility and the extent of use. Our fourth hypothesis, expressed in the alternative, is then: H4 . The extent of written budget use by primary healthcare businesses which opt to use written budgets is negatively associated with the PEU elements of dynamism and hostility. 4. 3. Performance and budgets Budgets have been recommended for pl anning, monitoring, and controlling business activities, with each thought to assist businesses to achieve pro? ability (Horngren et al. , 2006). However, the effect of budgets on pro? tability has not as yet been clearly demonstrated in the literature (McMahon, 2001). There is evidence of a positive association between the use of budgets and performance as proxied by growth in small and medium enterprises (Gorton, 1999). Even without extensive empirical evidence, planning and the use of appropriate budgets are promoted by academics, educators and accounting practitioners as a means of enhancing ? ancial performance (Hansen and Otley, 2003; Gorton, 1999). Thus, we might expect that primary healthcare businesses using budgets experience better performance than those that do not. More carefully, according to ‘contingency-based’ research, a state of equilibrium in the relationship between the contingency factors and the type of MCS is best described by â€Å"? t† (C ovaleski et al. , 2003). â€Å"Fit† occurs when the organisation designs its practices in such a way that it has a positive impact on performance relative to alternative practices. Thus, there will be no universally effective ‘extent of budget use’, as each combination of contingency factors will â€Å"? t† with different practices. The positive impact on performance of attaining â€Å"? t† is due to the ef? ciencies that result from using the most suitable MCS. When there is a lesser â€Å"? t† between the extent of budget use and the contingency factors, performance will be â€Å"impaired†. Further, mis? t will be associated with lower performance irrespective of whether it arises from â€Å"over-budgeting† or â€Å"under-budgeting†. Thus, our ? th hypothesis, stated in the alternate form, is: H5 . A business’s performance is positively associated with the degree of ? t between the extent of budget use and its contingency factors. The inef? ciencies arise because the need for a budgeting practice is incongruent with the adopted practice. If the business over-commits to budgeting, it is likely to ha ve expended scarce human and ? nancial resources without enjoying commensurate bene? ts. Conversely, if it under-commits, its performance will likely suffer because of control and/or co-ordination problems. To illustrate, consider the various contextual factors identi? ed above. First, regarding size, a relatively small business that uses an extensive budgeting practice will have unnecessarily expended resources implementing and operating the practice when in fact informal communication is practical and likely preferred. Conversely, a relatively large business without a formal budgeting practice will likely ? nd both communication and co-ordination problematic given the complexity associated with a larger number of employees. In a similar fashion, a relatively centralised business with an extensive budgeting practice has likely expended resources on a level of control that is greater than required to encourage employees to make decisions that are in keeping with the organisational objectives. In terms of strategy, a cost leadership strategy requires more speci? c controls than a differentiation strategy. Thus, the adopted business strategy will likely be less effective if an incompatible budgeting practice is implemented to support the strategy. Finally, high levels of PEU make it much more dif? ult to plan with certainty, thereby reducing the need for and advantages of budgeting. Finally, notwithstanding equilibrium-type arguments, we believe that there will be variation in the degree of â€Å"? t† in our setting given the dif? culties that primary healthcare businesses likely face in identifying and implementing their best budgeting practices. Given their size and medical focus, it is likely that many of our sample businesses approach budgeting from a relatively unsophisticated perspective and/or view it as a lower priority. This makes the process of identifying a practice that â€Å"? s† relatively slow and involves ‘trial and error’ type of learning. Here, Luft (1997) argues that while static equilibrium theories â€Å"can predict the techniques the ? rm should end up with† they cannot predict â€Å"how long it will take to complete the process or what the path to the solution will be. † Thus, it is likely that there will be a lag between the need for and the use of a particular budgeting practice. Milgrom and Roberts (1992) also argue that organisations are dynamically learning and moving towards an optimal level of management accounting practice. The problem of implementing a budget practice that â€Å"? s† is further complicated by the fact that implementation of new budget practices is likely to 14 The variation in PEU of manag ers from different industries has typically been the focus of previous research. In this study, industry is a constant but arguably PEU is still of interest, as it has also been found to vary among the managers of businesses within the same industry (Boyd et al. , 1993). R. King et al. / Management Accounting Research 21 (2010) 40–55 47 occur in a â€Å"lumpy† fashion because when increasing the extent to which they use budgets, businesses are forced to do so in larger rather than smaller increments. As Luft (1997) argues, â€Å"changes in information systems are often sharply discontinuous†. 5. Method 5. 1. Sample frame and description For this study, a cross-sectional research design is used and the quantitative measurement tool is a mail questionnaire. Recognised problems associated with the implementation of survey-based studies include the initial dif? culty of identifying and accessing appropriate respondents, and then of achieving acceptable response rates (Dillman, 2000). In light of this, since budgets are considered to be a traditional management tool and there is an identi? d trend towards delegating management responsibilities to practice managers, practice managers were chosen as the target subjects for the survey (Department of Health and Ageing, 2005). The further problems of contacting practice managers via a cost effective means and encouraging participation were addressed by approaching the Australian Association of Practice Managers (AAPM) for support. T he AAPM is the only recognised professional body for practice managers in Australia and consists of managers of dental, medical, and allied health businesses. Currently, there are 1200 members of the AAPM from medical practices, representing approximately 6% of the small private medical businesses operating in Australia. Membership in the AAPM is voluntary subject to an annual subscription fee. There are a number of bene? ts associated with membership including discounts for management education courses and national conferences. Thus, it is likely that members of the AAPM are interested in staying informed about current management trends, wish to become part of a professional network, and have the means to pay the membership fee. While no demographic data currently exist for practice managers who join the AAPM and those who do not, member businesses appear slightly skewed towards larger practices relative to the population of GP businesses. For 2005–2006, 83% of GP businesses had between one and ? ve GPs, with the remaining 17% having six or more (PHCRIS, 2008). In comparison, for our sample practices, 66. 4% had between one and ? ve GP’s and the remaining 33. 6%, six or more. However, notwithstanding this potential bias, the advantages of accessing the AAPM practice manager mailing list and having the AAPM recommend participation are considered to dominate. The questionnaire was initially developed from the existing literature and then pilot tested on a sample of 20 members of the AAPM and ? ve academic researchers. Based on this testing, a number of changes were made to wording and layout to enhance understandability in this setting. The ? nal questionnaire consisted of 35 questions presented in 10 sections and was estimated to require between 20 and 30 min to complete. Questions relating to each of the relevant constructs discussed in Section 4 were presented in dedicated and clearly labelled sections. The ? nal questionnaire was sent to a random sample of 1000 of the medical practice members of the AAPM. Of the 1000 surveys distributed, 12 were returned unopened. From the remaining 988 questionnaires, 144 complete and usable responses were received, representing a 14. 6% response rate which is comparable with those achieved in other studies of small businesses (Dennis, 2003). 15 Requested demographic data reveal 112 of the practices to be GP practices and 32 to be specialist practices, and that they employed between 2 and 42 full-time equivalents (FTE). Further, 98. 6% indicate that they use computers in some capacity. The average age of the practices is 23. 94 years, and for the 114 organisations that responded to the question, their average gross fees were $1,553,919, ranging from $206,816 to $11,000,000. 5. 2. Budgeting practice 5. 2. 1. Empirical model The ? rst stage of this study seeks insights both into the factors underlying a business’s decision to adopt a budgeting practice and into its subsequent decision as to the extent of budget use. To do so, it appeals to contingencybased research to identify four contextual factors (size (SIZE), structure (STRUC), strategy (STRAT), and perceived environmental uncertainty (PEU)) argued to drive the decisions, although in different combinations. Given this framework, we employ the following common empirical model to formally examine each of these two decisions: BUDGi = + 1 lnSIZEi 0 + 2 STRUCi + 3 STRATi + 4 PEUi + 5 TYPEi +? (1) where the various measures are described below. For the decision to adopt, based on H1 and H2a , 1 and 2 are predicted to be positive. For the decision as to the extent of use, based on H2b , H3 , and H4 , 2 and 3 are predicted to be positive and 4 negative. Practice type (TYPE) has been included in the model to control for potential structural differences (Hair et al. , 2006). Speci? cally, identi? ed differences in the pricing (higher) and supply (lower) of services by specialist versus general practices suggest that the market for specialist services may be relatively more heterogeneous (Department of Health and Ageing, 2005). We measure TYPE as a dichotomous variable, set equal to 1 for general practices and 0 for specialist practices. . 2. 2. Dependent variable measurement To examine the decision to adopt, we measure BUDG as a dichotomous variable set equal to 1 if the business indicates, in response to an explicit ‘yes/no’ question, that it 15 The mailing was restricted to 1000 questionnaires due to ? nancial constraints. Standard techniques to discourage non-response were employed including a per sonalised cover letter from the AAPM, promised con? dentiality, brevity of questions, the inclusion of a reply-paid envelope, a follow-up e-mail reminder, and a promise to make results available to participants (Dillman, 2000). Testing for non-response bias, undertaken by comparing 15 responses received in the ? rst month to the ? nal 15 responses received, revealed no statistically signi? cant differences. 48 R. King et al. / Management Accounting Research 21 (2010) 40–55 uses a written budget and 0 otherwise. We base this analysis on all 144 respondents and run Eq. (1) as a logistic regression. For this and all subsequent analysis, reported p-values are one-tailed since we have predictions relating to each of the contingency factors. To examine the extent of budget use, we restrict our tests to the 65 respondents using written budgets. Here, the survey questionnaire asked respondents to indicate on a 5-point Likert scale how systematically their business used operating budgets of various durations (annual, halfyearly, quarterly, monthly, and/or weekly), as well as cash ? ow, ? exible, rolling, long-term, or other budgets. These questions capture both the types of budgets used and the extent of their use, and are an adaptation of the alternative measurement approaches used in Chenhall and Lang? eldSmith (1998) and Jankala (2005). 16 The Cronbach’s alpha is 93. 8%. Panel A of Table 1 presents descriptive statistics for the responses relating to the usage of each type f budget. To construct the ‘extent of budget use’ measure, we apply exploratory common factor analysis with orthogonal rotation to the responses. Two uncorrelated factors with eigenvalues of 3. 845 and 1. 234 are extracted, explaining 64. 14% of the total variance. The factor loadings are presented in the ? nal two columns of Panel A of Table 1. The ? rst factor aligns with operating budgets and the second with other types of budgets. As such, we consider two sub-categories (‘operating budgets’ and ‘other budgets’) and measure BUDG for each as the average summated budget usage score across the relevant budgets in the subcategory. 7 Here, Eq. (1) is run using OLS. Given a consistent lack of signi? cance, we do not report or discuss results based on our analyses of the ‘other budgets’ measure. 5. 2. 3. Contingency factor measurement Following the majority of contingency-based MCS studies, we measure size (SIZE) as the number of full-time equivalent (FTE) employees (Chenhall, 2003). Respondents were asked to identify the number of FTE employees as administrative/reception staff, practice manager, nursing/allied health, medical, and other. Table 2 reveals that the mean (median) number of FTE employees for our sample businesses is 11. 31 (10. 500). For sensitivity purposes, we a lternatively consider gross fees as a measure of size. Data on gross fees were provided by 114 of the businesses, with a mean (median) value of $1,553,919 ($1,322,359). For the remaining three contingency factors, the measures are based on responses across 7-point Likert scales to dedicated questions in the questionnaire survey. Panel B of Table 1 presents the questions and descriptive statistics for the responses. For STRUC and PEU which involve multiple questions, exploratory common factor analysis is then applied o develop the empirical measures. The factor loadings are presented in the ? nal two columns of Panel B. 18 In detail, the measure of structure (STRUC) is based on responses to six questionnaire items asking the extent to which decision-making authority has been delegated within the business and at which level operating decisions are made. The six items, originally developed by Gordon and Narayanan (1984), have been subjected to considerable scrutiny and empirical testin g for reliability and validity in previous research (Chenhall, 2003). The Cronbach alpha is 82. 7%. Application of exploratory common factor analysis to the response scores leads to the extraction of only one factor with an eigenvalue of 3. 261, explaining 53. 81% of the variance. Thus, STRUC is measured as the average summated scores across the six items. Organisational strategy (STRAT) is based on Porter’s classi? cation scheme (Porter, 1980) and measured by the response to a single question drawn from Govindarajan (1988). This question asks the respondents to indicate their belief as to the best description of the business’s strategic emphasis, ranging from product differentiation to cost leadership. This was found in the pilot testing to be the only question from previous research applicable to our setting, Finally, we initially measure perceived environmental uncertainty (PEU) based on the responses to nine questions developed by Gordon and Narayanan (1984) to capture the intensity of competition, the dynamic and unpredictable nature of the external environment, and the potential elements of change in the environment. Based on the correlations among the responses, only ? ve items were eventually used with a Cronbach alpha of 64. %, as the responses to four items had correlations of less than the 30% level recommended for inclusion in factor analysis (Hair et al. , 2006). 19 Consistent with previous research (Gordon and Narayanan, 1984), application of exploratory common factor analysis with orthogonal rotation led to the extraction of two factors with eigenvalues of 2. 327 and 1. 122 explaining 67. 70% of the total variance. Following the literature, we label these factors as à ¢â‚¬ËœPEU hostility’ (PEUhost ) and ‘PEU dynamism’ (PEUdyn ). PEUhost loads on the two questions relating to the competitiveness of the business environment whereas PEUdyn loads on the three questions relating to the predictability of the external environment. We include both in our empirical model, measuring each as the average summated response scores across the relevant questions. 16 Jankala (2005) prefers this measure of systematic use as a more reliable and precise measure of a business’s commitment to the use of budgets, rather than the more subjective scales used by, for example, Chenhall and Lang? eld-Smith (1998) that measure bene? t derived. 7 The yearly operating budgets did not load on either factor as a large majority of the businesses indicated that they used yearly operating budgets on a systematic basis. 18 The reported factor loadings, eigenvalues, and percentage variation explained are based on the full sample of 144 respondents. When the exploratory common factor analysis is repeated based only on the 65 respondent bus inesses using budgets, all measures are qualitatively identical. 19 The four items removed were competition for manpower, new services marketed, ability to predict preferences for customers, and change in legal, political, and economic environment. Their removal is perhaps not surprising that as there was little variation in the responses received with the sample small businesses drawn from the same industry. R. King et al. / Management Accounting Research 21 (2010) 40–55 Table 1 Descriptive statistics and factor loadings for survey questionnaire responses. Question Descriptive statistics Mean Panel A: Budgeting BUDG 1 Which of the following budgets are prepared and how consistently? (5-point scale: 1 = not used, 2 = seldom, 3 = at times, 4 = often, 5 = systematically) a. Operating budget, yearly b. Operating budget, half-yearly c. Operating budget, quarterly d. Operating budget, monthly e. Operating budget, weekly f. Cash ? ow budget g. Flexible budget h. Rolling budget i. Long-term budget Med SD Factor loadings #1 #2 49 2. 690 2. 064 2. 064 2. 092 1. 578 2. 079 1. 701 1. 704 1. 795 1 1 1 1 1 1 1 1 1 1. 899 1. 663 1. 659 1. 662 1. 315 1. 626 1. 317 1. 400 1. 473 0. 746 0. 614 0. 533 0. 668 0. 472 0. 149 0. 317 0. 168 0. 255 0. 322 0. 034 0. 346 0. 508 0. 823 0. 606 0. 715 Panel B: Contingency factors Structure (STRUC) STRUC 1 To what extent has authority been delegated to the manager or employee for each of the following decisions? Please indicate actual rather than stated authority) (7-point scale: 1 = no delegation, 7 = total delegation) a. Initiate ideas for new services b. Hiring and ? ring of personnel c. Selection of large investments d. Budget allocations e. Pricing decisions STRUC 2 Most operation decisions are made at what level? (7-point scale: 1 = owner level, 7 = manager level) 4. 999 5. 250 3. 173 3. 980 4. 311 4. 349 5 6 3 5 5 4 1. 517 1. 923 1. 963 2. 123 1. 900 1. 870 0. 531 0. 610 0. 732 0. 803 0. 761 0. 581 – – – – – – How would you best describe your practice’s strategic emphasis? 7-point scale: 1 = product differentiation; 7 = cost leadership) Perceived environmental uncertainty (PEU) PEU 1 How stable/dynamic is the external environment (economic and technological) facing your practice? (7-point scale: 1 = very stable, 7 = very dynamic) a. Economic environment b. Technological environment PEU 2 How would you classify the market activities of your competitors (i. e. , other healthcare practices) in the past 3 years? (7-point scale: 1 = becoming more predictable, 7 = becoming less predictable) How intense is each of the following in your industry, the healthcare profession? 7-point scale: 1 = negligible, 7 = intense) a. Bidding for purchases b. Price competition Strategy (STRAT) STRAT 1 2. 983 3 1. 127 n/a n/a 4. 134 4. 7 61 3. 691 4 5 4 1. 603 1. 596 1. 122 0. 968 0. 755 0. 369 0. 176 0. 185 0. 118 PEU 3 2. 446 3. 553 2 4 1. 352 1. 502 0. 147 0. 168 0. 676 0. 625 Panel C: Performance (PERF) PERF 1 Which best describes your response to the following statements over the past 3-year period? Compared to key competitors, my practice: (7-point scale: 1 = strongly disagree, 7 = strongly agree) a. Is more competitive b. Has more patients c. Is growing faster d. Is more pro? table e. Is more innovative f. Has more doctors 5. 082 5. 353 5. 105 5. 210 5. 320 4. 094 5 6 5 5 6 4 1. 607 1. 619 1. 644 1. 593 1. 643 2. 204 0. 660 0. 711 0. 849 0. 667 0. 592 0. 502 – – – – – For Panel A, the exploratory factor analysis was conducted based on the 65 businesses that produce written budgets. For Panels B and C, the exploratory common factor analysis was based on the full sample of 144 respondents. Items deemed to load on the identi? ed factor appear in bold. 50 R. King et al. / Management Accounting Research 21 (2010) 40–55 Table 2 Descriptive statistics for ‘contingency-based’ model variables. Measure ‘Operating budgets’ Mean Median Standard deviation Min max Size (SIZE) Mean Median Standard deviation Min max Structure (STRUCT) Mean Median Standard deviation Min max Strategy (STRAT) Mean Median Standard deviation Min max PEUhost Mean Median Standard deviation Min max PEUdyn Mean Median Standard deviation Min max Performance (PERF) Mean Median Standard deviation Min max Full sample (n = 144) 1. 948 1. 000 1. 376 15 11. 531 10. 500 6. 583 2 42 4. 344 4. 333 1. 381 17 1. 990 2. 000 1. 122 17 2. 987 3. 000 1. 199 16 4. 201 4. 333 1. 179 1 6. 6 4. 027 4. 083 1. 248 17 Written budget (n = 65) 3. 085 3. 000 1. 50 15 12. 893 12. 000 6. 367 3. 5 31 4. 862 5. 000 1. 212 1. 67 6. 83 1. 860 2. 000 1. 014 16 3. 231 3. 000 1. 183 16 4. 241 4. 333 1. 129 1 6. 67 4. 228 4. 333 1. 302 17 No written budget (n = 79) n/a p-Value – 10. 410 9. 250 6. 586 2 42 3. 918 3. 833 1. 372 17 2. 090 2. 000 1. 200 17 2. 785 2. 500 1. 181 1 5. 5 4. 169 4. 333 1. 224 1 6. 67 3. 861 3. 833 1. 184 1 6. 5 0. 024 0) or â€Å"under-budgets† (? 0), the impact of ‘lack of ? t’ on ? nancial performance should be the same. Formally, the model we employ, illustrated using |? |, has the following form: PERFi = 0 we also requested objective measures of pro? ability from our sample businesses (Govindarajan and Gupta, 1985). Unfortunately, less than one-third of our sample provided the data. Thus, the use of objective measures for robustness purposes is also impractical. 6. Results and analysis 6. 1. Preliminaries Descriptive statistics for measures used in tests of our hypotheses are presented in Table 2, both for the overall sample of 144 respondents and for the sample partitioned on the basis of whether a written budget is adopted, along with tests for differences in mean values between partitions. As revealed, there is considerable cross-sectional variation in each measure. Further, there appear to be several signi? cant differences between respondents adopting written budgets and those not. Speci? cally, businesses using budgets have more FTE employees (SIZE, p = 0. 024), are more decentralised (STRUC, p 0. 001), face a more hostile enviro How to cite Budgeting Practices and Performance in Small Healthcare Businesses, Papers