Journal of Business Ethics
© Springer Science+Business Media B.V. 2012
10.1007/s10551-012-1487-z

Fixing the Game? Legitimacy, Morality Policy and Research in Gambling

Rohan Miller  and Grant Michelson 
(1)
University of Sydney Business School, Sydney, NSW, 2006, Australia
(2)
School of Management, Edith Cowan University, Perth, WA, 6027, Australia
 
 
Rohan Miller
 
Grant Michelson (Corresponding author)
Received: 7 September 2012Accepted: 11 September 2012Published online: 23 September 2012
Abstract
It is a truism that some industries are controversial either because the processes employed or the resulting products, for instance, can potentially harm the well-being of people. The controversy that surrounds certain industries can sharply polarise public opinion and debate. In this article, we employ legitimacy theory and morality policy to show how one industry sector (the electronic gaming machine sector as part of the wider gambling industry) is subject to this reaction. We suggest that the difficulty in establishing legitimacy surrounding CSR practices in this sector is related to morality policy, whereby ideology and personal values play important roles in dividing opinion. Thus, gambling is often framed as an activity that is morally and ethically problematic. To show how this can manifest, we examine the veracity of two state-funded studies in Australia used in the development of gambling policy and their subsequent adoption in academic research. We highlight methodological, analytical and reporting issues in these studies that normally should be identified by those using such findings. The significance is that when morality policy and conflict surrounding legitimacy are involved, then it can explain why adherence to normative research standards is potentially lowered. Our theoretical posture leads us to further speculate that the adoption and communication of CSR in electronic gambling will be contested by opponents of the industry.
Keywords
Stakeholders Electronic gambling Legitimacy theory Morality policy Corporate social responsibility

Introduction

In different societies a range of ‘unmentionable’ products, services or concepts exist ‘that for reasons of delicacy, decency, morality, or even fear, tend to elicit reactions of distaste, disgust, offense, or outrage mentioned or openly presented’ (Wilson and West 1981, p. 92). This does not mean that all objects and actions that are ‘unmentionable’ or controversial are undesirable or that societal values will not change over time; rather, that items classified in this way can generate strong dissensus among societal members.
If certain products, services and concepts generate societal controversy, as a corollary there will be controversial industry sectors. For these industries, questions pertaining to their legitimacy and any ‘genuine’ corporate social responsibility (CSR) efforts will likely be raised. This is apparent for certain products or activities characterised as ‘sin’ (e.g. gambling, tobacco, alcohol, pornography) (see Meier 1994). Should the products or activities even be available? In the case of gambling at least, the activity is often sponsored by governments in many countries in the form of state lotteries and produces significant tax revenues, investment and employment (Schwartz 2003, p. 206). Thus, there are considerable benefits for governments to endorse gambling. While mindful of the financial advantages, governments are also conscious that gambling can potentially result in adverse social and economic consequences (Hancock et al. 2008).
The objective of this article is to examine one sub-sector of the gambling industry: electronic gaming machines (EGMs) or poker/slot machines in one national context. In Australia, it was estimated that for 2008–2009, almost A$161.2 billion was spent nationally by way of turnover on all gambling, of which approximately 71 % (A$114.2 billion) was gambled on poker machines (OESR 2011). Thus, EGMs are responsible for most gambling turnover in Australia. There is very little discussion of the electronic gambling industry in the CSR literature which might imply that it is not typically associated with maintaining at least reasonable socially responsible standards.
In this article, we explore why this sub-sector remains controversial by drawing on legitimacy theory, in particular, pragmatic and moral types of legitimacy (Suchman 1995). However, because there is an active role played by governments in the area of gambling regulation and control, we also refer to the concept of ‘morality policy’ from the field of political science to develop a theoretical explanation to show how gambling, and the contestation for legitimacy, can be framed and communicated by various opponents. To illustrate this, we examine two cases of state-funded research in Australia since these studies, while subject to a range of methodological, analytical and reporting problems, have nonetheless been widely adopted in academic (and other) research on gambling. We conclude, similar to McGowan (1997), that bias in gambling research appears to be an enduring issue and we further contend that normative standards of ‘science’ may be reduced where opponents of gambling mobilise arguments pertaining to legitimacy and morality policy (see Suchman 1995).1 The major implication is that while the many and varied electronic gambling operators in Australia do not appear unwilling to consider more responsible gambling policies (Hing 2001), communication of current or future CSR practices by the operators are likely to face substantial challenge from industry opponents.

Legitimacy Theory

There is little doubt that controversial products, services and concepts do not experience widespread societal acceptance and, therefore, require at least some justification and endorsement. To help account for this, we refer to organisational theory where the concept of organisational legitimacy has been discussed (e.g. Elsbach 1994; Suchman 1995) and then subsequently applied in areas including business ethics and CSR (e.g. Baur and Palazzo 2011; Johnson and Holub 2003; Phillips 2003; Schepers 2010). The majority of these studies have been concerned with organisational legitimacy, whereby legitimacy is: ‘a generalized perception or assumption that the actions of an entity are desirable, proper, or appropriate within some socially constructed system of norms, values, beliefs, and definitions’ (Suchman 1995, p. 574). It is far less common to find studies which explore industry legitimacy (for a notable exception, see Winn et al. 2008). We consider that the theoretical insights derived at the organisational level can be broadly applied to industries. For example, it has been noted that social disclosures by companies can be used as a legitimising tool in general, but is particularly prevalent in industries more exposed to public scrutiny (cf. Cho and Patten 2007). This section will, therefore, examine the concept of legitimacy for the electronic gambling sector.
Industries might seek the legitimacy implicit with the formal adoption of CSR for many reasons, such as to ensure its continuity and credibility. Continuity can occur as a result of key stakeholders supplying important resources to industries that seem desirable, proper and appropriate (Suchman 1995, p. 574), while credibility might enhance how people understand the industry in terms of meaning and trustworthiness. Since legitimacy is perceived, it is susceptible to re-interpretation and change over time (Johnson and Holub 2003). This can occur as a result of industry action (or inaction), manipulation and different goals among important stakeholder groups, for example. From this perspective, it demonstrates that legitimacy can be temporary in nature; once acquired, it can also be diminished or lost. Equally, an excessive focus on legitimacy-seeking behaviours can be counter-productive and, therefore, organisations and industries need to monitor how stakeholders crucial to their reputation and viability react to communications and actions designed to enhance their support (Elsbach 1994; Sonpar et al. 2010). This is also because in controversial sectors ‘bad’ actions could have an asymmetrically greater (more harmful) impact than ‘good’ actions (Winn et al., 2008, p. 37). Moreover, even socially ‘good’ actions such as devoting some gambling profits to the community, for example, could increase moral outrage and allegations of cynical hypocrisy.
In an important contribution, Suchman (1995) distinguishes between three types of legitimacy: pragmatic legitimacy (i.e. based on the self-interest of different gambling interest groups), moral legitimacy (i.e. normative evaluations of gambling), and cognitive legitimacy (i.e. the extent to which the gambling industry is taken for granted). The last of these—cognitive legitimacy—is perhaps the most powerful source of legitimacy because there is societal acknowledgement that the industry is necessary or inevitable and there is little need to question its outputs, procedures and structures. This type of legitimacy operates at the subconscious level, but is not evaluative like pragmatic or moral legitimacy. This is because cognitive legitimacy assumes there are mutual behavioural expectations upon which different interest groups have somehow agreed (Baur and Palazzo 2011). In the EGM industry, cognitive legitimacy is unlikely to be found given the level of disagreement surrounding its necessity and for this reason we focus only on pragmatic and moral legitimacy.
As noted, pragmatic legitimacy depends on self-interested calculations of the most important stakeholders and consists of economic exchange (the financial value gained from the electronic gambling industry) and influence exchange (the extent to which the electronic gambling industry is responsive to interests) (Suchman 1995, p. 578). Although debateable, a utilitarian point of view could argue that the economic benefits to society from gambling (e.g. greater shareholder wealth, extra government revenues, the creation of jobs) might outweigh the costs to society (Schwartz 2003, p. 206). At the firm level, there is longitudinal data (1995–2009) which shows that CSR engagement by US organisations across a wide range of controversial industries in general, including gambling, positively affects their firms’ value (Cai et al. 2012). Thus, it can be economically rational for such firms to consider social responsibility as part of their business case.
With regard to influence exchange, CSR in the electronic gambling industry is a more recent phenomenon and there is relatively little written about what it should entail and why (Hancock et al. 2008, pp. 65–66). There is some evidence in Australia that the industry in general is beginning to be more aware of its social responsibilities. For example, it commissioned a report in the early 2000s to understand international harm minimisation strategies in gambling (Blaszczynski circa 2001) and also developed a responsible gaming code (Australian Gaming Council 2001). Having said this, it also seems the electronic gambling industry prioritises economic and legal responses to CSR over more ethical and discretionary ones (Hing 2001). The strongest support for CSR actions within the industry is to allocate funding to co-ordinate, monitor and enforce responsible gambling. However, within the gaming industry, there is variation among individual operators in their willingness to provide more ethical actions including the provision of better information on products, advertising restrictions, staff training and liaison with community agencies (Hing 2001, p. 133). More recently, some operators in the industry have engaged the services of the Salvation Army, a global, Christian-based organisation, by allowing Salvation Army staff to visit gaming environments and assist those who may be struggling with a gambling problem. Such variation in CSR practices at the level of the gambling operator is also apparent in other countries such as the UK (Jones et al. 2009).
Applying moral legitimacy to the activity of electronic gambling is to consciously inquire whether gambling is ‘the right thing to do’. Moral legitimacy will evaluate the consequences, techniques and procedures, structures and leader behaviours (Suchman 1995, p. 579). For example, Newton (1993) contends gambling is wrong because it violates a person’s duty of stewardship of property. Rather than construing this as individual freedom to dispose of one’s property as they wish, Newton contends any losses incurred by the gambler will affect national wealth. Others see gambling as morally problematic in that it ostensibly contrasts the core values of capitalism (e.g. rational economic behavior, prudence, self-discipline and the assumed relation between effort and reward) (see Borna and Lowry 1987, p. 223). However, controversial sectors can seek to manage their moral legitimacy through reasonable argument or communication; it might just be that moral legitimacy is the most decisive type of acceptance for such industries (Palazzo and Scherer 2006, p. 74).
Perhaps one limitation of moral legitimacy as often conceptualised and deployed in previous CSR studies is that it tends to focus only on the actions or behaviours of the particular actor or industry under scrutiny. The construction of legitimacy can be quite complex and in the case of the EGM industry, for instance, is subject to considerable regulation and control by governments. By introducing the actor of government into the analysis, we believe that the concept of ‘morality policy’ can further our theoretical understanding of legitimacy. It is to this concept that we now turn.

Morality Policy

In public policy and political science, there is a special category of topic referred to as morality policy. These are issues which generate controversy because what is at stake are issues of ‘first principle’, in which the topic has been portrayed by at least some stakeholder or interest groups as one of morality or ‘sin’ and, therefore, rely on moral arguments. Moral values, rather than economic or political values are what matter which means that for some stakeholders there is no presumption of ‘innocence’ until proven otherwise; for them, the activity is morally problematic. Using deontological reasoning, the relevant question is: even if legal, is this activity or issue right? Not surprisingly, such controversial issues polarise public opinion and even within the polity there is no consensus on the values and morality underlying certain issues (Mooney 1999). Therefore, how a polity should proceed is not necessarily about the best means to achieve a particular policy goal but which values, including those pertaining to business, should be endorsed in society. Since the level of public consensus on certain values may differ over time, this can affect the politics of morality policy (Mooney 1999, p. 675).
Along with a range of other topics, gambling falls within morality policy (Meier 1994; Pierce and Miller 1999). This means gambling is subject to debate over ‘first principles’ about whether the activity is worth endorsing. Morality policy is characterised by high salience to a society with corresponding high citizen involvement and participation. Thus, values concerning gambling and other morality policy issues do not lend themselves to compromise as the different protagonists typically maintain their entrenched views. Consequently, morality policy debates are seldom settled as individuals and relevant stakeholder groups engage strongly in the discussions and policy-making process.
Due to the non-compromising nature of morality policy, issue definition is integral to determining the politics of the decision-making process. Small changes in definition can affect the extent to which debates are driven by exclusively morality-based values. For example, if some gambling revenues are devoted to social goods such as funding education then it can shift the politics of adoption away from merely the problems and ‘sin’ of gambling towards some positive outcomes (Pierce and Miller 1999). Therefore, the deontological approach to morality policy can be challenged so that consequences or utilitarian-based arguments are also considered.
This reveals framing issues are critical in the politics of morality policy. Indeed, it has been suggested that: ‘morality policies concern “threats to core values” not because the values are “core” but, at a more fundamental level, because those who frame the issues place adherence to moral principles above alternative considerations’ (Mucciaroni 2011, p. 191). It could be just as appropriate to label the issue as a civil liberty or rights issue. Supporters of gambling including the electronic gambling industry itself might frame the activity as protecting individual freedom; gamblers voluntarily engage in the activity, aware that they might lose money. To the extent that gamblers do lose money and impoverish themselves and their families, this result could equally occur if the gambler loses their job (Schwartz, 2003, p. 206). Others have framed gambling as a ‘normal’ impulse in people, resulting in higher creativity and personal satisfaction (Campbell 1976). In addition, gambling is legally endorsed in many national contexts generating numerous jobs and fiscal benefits for governments.
Rather than focusing on gambling per se, opponents of electronic gambling in Australia have re-cast the activity as a public health issue. Consequently, the discourse of problem gambling and the problem or at-risk gambler has emerged (Banks 2011; Hing 2001; Hing and McMillen 2002). Accepting this terminology appears a compromise position for a polity; acknowledging that gambling was an inherent social problem would be to risk important gambling revenues dominated by receipts from electronic gaming. For governments to proceed any further beyond addressing the case of ‘problem’ gambling would be contentious, since ‘the vast majority of society now accepts gambling as an entertainment activity’ (Schwartz 2003, p. 205). The number of problem gamblers in different settings remains small (varying between 0.5 and 1.5 % of the gambling population, although their expenditure on gambling has been claimed as disproportionately high (Adams 2011, p. 145).
We have briefly considered how morality policy in terms of the values to be endorsed by society and subsequently ‘managed’ by governments can impact controversial issues such as electronic gambling. The importance of articulating these values through morality frames does not exclude other ways to frame support for, or opposition against, a particular issue but it does extend our understanding of legitimacy. In other words, morality policy can be ‘a strategic approach to framing public policy issues’ (Mucciaroni 2011, p. 211). If this proposition is correct, then what supporters and opponents communicate about gambling’s consequences and the various CSR practices in this industry, is highly pertinent, as it is for moral legitimacy (Suchman 1995). Part of this communication process extends to research in gambling and the following section discusses this theme.

The Integrity of Research and Its Communication?

The issue of establishing accountability, responsibility and integrity in the research process is not new (Cossette 2004; Woolf 1991). For example, survey evidence has found that business academics do not share any consensus on the ethics of issues such as weighing evidence, analysing data, reporting findings and how frequently this is done (see Cossette 2004, pp. 220–223). In other fields such as economics, different schools of thought ascribe to different sets of assumptions and motivations that can result in different conclusions and policies concerning the same phenomenon.2 That there is little uniformity in research standards or outcomes is not unknown. By referring to ideas of direct relevance to business ethics and CSR (legitimacy and value-framing through morality policy), we seek to move beyond the descriptive level by offering a theoretical explanation for why research in electronic gambling can be problematic and susceptible to some bias and impropriety (McGowan 1997). Indeed, researchers can affect moral legitimacy if they adopt acceptable techniques and procedures (Suchman 1995, pp. 579–580). Where governments are particularly sensitive to the values of important constituents and other actors, then scientific results which satisfy these actors’ frames stands more chance of being produced and reproduced (see Abraham 1995).
Against this backdrop, some researchers examining the efficacy of gambling studies are disappointed at the objectivity of gambling research (Grinols and Mustard 2001) and they find ‘conceptual and methodological flaws that are sufficiently serious to call the resulting estimates into question’ (Volberg et al. 1998, p. 360). Anti-gambling advocate, Bernard Horn, dismisses the need for quality evidence as ‘a rough estimate stated in plain English and based on available knowledge, is infinitely more valuable to the policy process than no estimate at all’ (Horn 1997, p. 305).
There is little doubt that gambling research published in peer-reviewed journals and research commissioned by policy-makers and regulators is widely but selectively used in the justification of extant gambling policy in Australia (Banks 2011; Productivity Commission 2010). For example, an estimated 40 % of electronic gaming revenue is believed to come from problem gamblers (Productivity Commission 2010). However, as we have just illustrated, if the potential for research bias is apparent when interpreted through the lens of legitimacy and morality policy, then where does the public interest lie? Providing an answer to this question for the polity could be made more difficult when the research is not independent, but is commissioned or based on sponsorship. Commissioned research is conducted in a range of fields, the findings of which ‘are more likely to be taken into account by policy makers and activists than are findings from apparently pure academic research’ (Cowen and Goulbourne 1998, p. 5). This implies there is a politics of research where managing the expectations of the commissioning/funding body (and other potential users) are taken into account. Being impartial and the appearance of being impartial and objective can prove important considerations for researchers involved in commissioned research dealing with controversy (Brown 1998). In the case of gambling, the source of income to fund research is, therefore, not inconsequential as Adams (2011) has recently argued.
It is not our position that the politics and sensitivities of sponsored or commissioned research should necessarily render the findings unhelpful for policy and practice. We believe that research in general can and should be of relevance to various users (Mohrman et al. 2001). Commissioned studies should still ascribe to the same standards as academic research with its assumptions of rigour and integrity. Different disciplines are now increasingly considering and adopting codes of ethics for their members. As one example, the Academy of Management provides clear guidelines for the conduct and accurate reporting of research including full disclosure of methods, analyses and findings, even if the results contradict expected outcomes (Schminke 2009). Such guidelines are helpful as they prescribe ways in which commissioned or sponsored research could help defend itself from allegations of bias. The extent to which codes of research ethics are adopted and enforced in sponsored-research remains unclear.
We now turn to examine two recent state-commissioned studies in the Australian EGM industry to show how the problems contained within these studies may be more than inadvertent mistakes or oversights. Given the number of other researchers and interest groups who have subsequently relied upon the findings of the two studies without identifying or correcting the errors,3 we suggest why normative research criteria such as critical scrutiny and verification might be suspended or ignored when questions of legitimacy and morality policy are at stake.

The Gambling Studies: A Brief Background

One common focus of gambling policy in Australia is large government-funded prevalence studies that seek to determine the level of ‘problem’ gambling at a given point in time. Members of the academic community who also publish peer-reviewed articles are often commissioned to undertake or act as consultants on these studies (e.g. Centre for Gambling Research (CGR) 2004; Rodda et al. 2004; Schellinck and Schrans (Focal Research Consultants) 2003). Two such commissioned reports involving academics, the CGR (2004), 2003 Victorian Longitudinal Community Attitudes Survey, and Caraniche Pty Ltd’s (2005), Evaluation of Electronic Gaming Machine Harm Minimisation Measures in Victoria, are examined.
We limit our analysis to two studies conducted in the Australian state of Victoria,4 and justify this selection because the findings of both have had significant national impact in terms of policy debates. For instance, they were an important part of the context and motivation for the Poker Machine Harm Minimisation Bill (2008) in the Commonwealth Parliament of Australia, designed to promote responsible gambling and minimise problem gambling across the country. Thus, their results have effectively been generalised by various stakeholders to be representative of the electronic gambling sector in Australia. In addition, our choice of these studies based on the same state and concluded at roughly the same time in the early to mid-2000s, enables a better identification of their diffusion and communication effects over time than would be the case had we examined more recent examples of commissioned gambling research. Therefore, we consider it more important in light of supporting our theoretical framework (legitimacy and the framing of morality policy) to analyse two studies in some detail, rather than to increase the number of cases examined, but risk not demonstrating clearly enough their flaws.
The CGR study seems more intrinsically linked to academia. It was authored by academics with extensive publications and consulting interests in gambling (e.g. Doran et al. 2007; Marshall 2004; McMillen and Doran 2006; McMillen and Wenzel 2006) and was reviewed by the Gambling Research Panel, a body chaired by an academic with publications relevant to gambling (e.g. Hancock et al. 2008). Both of the funded studies were used to justify the claim that ‘problem or at-risk gamblers spent about 53 % (A$1.3 billion) of the money expended on hotel and club EGMs in 2005–06 in Victoria’ (Livingstone and Woolley 2007, p. 371) that has subsequently been cited by other articles published in peer-reviewed academic journals (e.g. Cosgrave 2010; Hancock et al. 2008; Millhouse and Delfabbro 2008), other gambling reports, and is prominent in the gambling policy debate. For instance, the Productivity Commission (2010) draws extensively from both the CGR and the Caraniche reports, as it does the Livingstone and Woolley (2007) estimates of expenditure by problem or at-risk gamblers.
The significance of these studies is that they continue to influence public policy by focusing on the very small proportion of electronic gamblers (see above with Adams’ 2011 estimate of between 0.5 and 1.5 % of the gambling population) who spend more than they should. The ‘problem gambler’ discourse has continued to influence the focus of policy makers and remains one of ‘harm minimisation’ by seeking to reduce the maximum bets on gaming machines, and for gamblers to set self-imposed limits on expenditure in any period (Banks 2011). The federal government in Australia currently holds power by a slim majority and is influenced by independent parliamentarians who hold strong views on preventing and reducing harm from gambling. Some commentators contend that, if implemented, these new measures could enhance electronic gambling operators’ commitment to CSR even if they are likely to incur economic costs (Prior Jonson et al. 2012). However, these same authors also note that gambling providers are opposed to any mandatory reforms and argue only for voluntary arrangements.

Study 1: The 2003 Victorian Longitudinal Community Attitudes Survey

One of the CGR’s research objectives was to determine prevalence or levels of problem gambling in Victoria. At the same time it employed three different problem gambling screens: the South Oaks Gambling Screen (SOGS), the Canadian Problem Gambling Index (CPGI) and the Victorian Gambling Screen (VGS). The rationale for doing so was to cross-validate the three gambling screens. It is fair to note the CGR acknowledge some of the limitations of their questionnaire survey. Specifically, CGR (2004, pp. 172–173) state there are factors that may impact their sample being inconsistent to the population including; potential sampling errors and self-selection bias, imperfections in the sampling frame, only one household member being interviewed, and a very low overall response rate of 34.2 %, which is regarded as much lower than other Australian prevalence studies (SACES 2008, vol. 1, p. xii). However, other limitations are overlooked by the CGR that could substantially impact on the results and the concomitant reference to its findings by other researchers and industry stakeholders.

Measurement Instrument Background

In a clinical environment, problem gambling is often defined by the American Psychiatric Association’s DSMIV. The basic premise of this screen is that respondents who score 5 or more from the 10 ‘yes/no’ questions are classified as a pathological gambler. Lesieur and Blume (1987) later developed an alternative scale called the South Oaks Gambling Screen (SOGS) that asked more questions but was intended to be simple enough to use on the South Oaks Hospital’s drug and alcohol afflicted clients in Long Island, New York. The SOGS has been widely used to estimate problem gambling prevalence across different national settings.
Gambling epidemiology is largely concerned with problems allegedly caused by or associated with EGM or poker machine gambling (e.g. Dickerson et al. 1997; Shaffer and Hall 2001). However, most gambling diagnostic screens (including SOGS) only focus on the generic act of ‘gambling’. They do not ask about the various forms of gambling a person may consume (e.g. lotteries, poker, horse betting, roulette, etc.) or metrics typically used to describe consumer behaviour such as purchase incidence and amount spent by product. Moreover, these gambling screens do not indicate whether gambling losses are relevant to respondents’ household disposable income or how long and often gambling expenditure may occur at unsustainable levels. Thus, any such claims are drawn from outside the measurement instrument.

The Validity and Reliability of SOGS in Australia

Measurement validity is the degree to which a measure accurately represents what it is supposed to (Hair et al. 1998). When used as a measure to estimate problem gambling prevalence, SOGS is a summated scale that implicitly has the characteristic that multiple responses are more accurate than a single question.
At the time of the CGR (2004) survey, SOGS was a popular screen but there was increasing realisation that its scale was seriously flawed. When Livingstone and Woolley’s (2007) article, which drew on the CGR study, was published, SOGS was widely regarded as invalid for use in Australia (see McMillen and Wenzel 2006, p. 186). The SOGS generated a high proportion of false positives (Ladouceur et al. 2000) as the power to detect pathological gambling (positive predictive value) does not reach 90 % until scores of 9 or higher on the SOGS (Gambino 2005). The only apparent empirical verification of SOGS with problem gamblers in Australia recommended a cut-off score of 10+, but acknowledged the cut-off score may be lowered to 7 and would likely capture 97 % of problem gamblers (Dickerson et al. 1996). Yet, problem gambling researchers maintained the SOGS score of 5+ to determine levels of problem gambling in society.
The volatility of SOGS is demonstrated by two prevalence studies that used SOGS in Victoria during 1999. The 1999 Seventh Survey undertaken in Victoria reported SOGS 5+ estimated problem gambling prevalence was at 0.8 % of the adult population. In the same year, the Productivity Commission used SOGS to estimate problem gambling in Victoria at 2.14 %. Using an estimate of 3.4 million adults in the state of Victoria as the reference point, the 1999 SOGS studies estimate that between 27,200 (0.8 %) and 71,400 people (2.1 %) may have gambling-related problems. The variance between the two studies is over 260 % (or 44,200 persons) which suggest SOGS in Victoria is an unreliable scale. Ladouceur et al. (2000, p. 20) conclude that the
false positives generated by SOGS in the studies reported here could have massive repercussions for prevalence estimates of pathological gambling worldwide…it would seem imperative that more attention is given to constructing screening tests where response bias is minimised by using balanced question designs and that interview procedures be constructed where the possibility of misinterpreting meaning is reduced.

The Problem or At-Risk Gambler

The problem or at-risk concept posits that ‘as gambling increases so does the incidence of harm’ (Livingstone and Woolley, 2007, p. 364). However, this concept has not been validated with reference to the American Psychiatric Association’s DSM measures and SOGS was not intended for precise measurement or to diagnose ‘at risk’ over the telephone (the research technique typically used in problem gambling prevalence studies). When introducing SOGS, Lesieur and Blume (1987) only use the term ‘at risk’ in their Appendix 1 (p. 5) which states ‘Scores on the South Oaks Gambling Screen itself are determined by adding up the number of questions that show an “at risk” response’. Thus, this diagnostic tool only determines when respondents may be ‘at risk’ of problem gambling rather than clearly defining levels of pathology.
The imprecise measurement tools and the determination that problem gambling only exists above a certain cut-off score suggests there are too many challenges for the concepts of pathological progression and at-risk to be realistically measured by SOGS.

Sampling

The CGR make conflicting claims about their sampling methods. Initially, the CGR claim to use a ‘random sample of responses from 8,479 Victorian residents’ (CGR 2004, p. 167) but then reveal a selected sample approach whereby ‘regular gamblers were over-sampled providing reasonable numbers for analysis purposes…at the same time selecting only a proportion of non-gamblers (1 in 3) and non-regular gamblers (1 in 6)’ (CGR 2004, p. 168). With no theoretical rationale or empirical support, regular gamblers were defined as those who gambled at least weekly or 52 times per year in gambling activities other than lottery games or ‘instant scratch’ tickets. The CGR makes the assumption that lottery products have no link to problem gambling.
The veracity of the assumption that ‘problem’ gambling is predominantly linked with EGMs is doubtful and there is considerable support to the contrary. For instance, the CGR (2004, p. 52) state different categories of gambling consumer have been identified based on their propensity to consume more than one gambling product (e.g., regular gamblers = 3.43 gambling products; young people between 18 and 24 years = 2.62 gambling products). The Productivity Commission’s 1999 Report identified that problem gambling losses on lottery represented 25 % of problem gambling share losses and that problem gambling losses on wagering represented 33 % of problem gambling share losses (CGR 2004, Table P.6). When combined, lotteries and wagering exceeded the share of losses by EGMs (Table P.6). LaPlante et al. (2009) suggest that the range of gambling involvement needs to be considered and empirically demonstrated that no specific form of gambling is a predictor of gambling disorders; rather, greater gambling involvement better characterises disordered gambling.
The selected sampling technique used by CGR (2004) will result in unspecified or unknown levels of error if the results are extracted as proportional to the population. The sampling frame used by the CGR very likely overstates one group of consumers (EGM players) and understates others.

Sample Size

One of the CGR’s goals was to test three different methods (or screens) of defining and measuring problem gambling. This meant the CGR divided their sample into three subsets, one for each of the screens being tested. Thus, from the total sample of 8,479 Victorian residents, a subset of 433 ‘regular’ gamblers was randomly divided into three cohorts to evaluate the different gambling screens (CGR 2004, p. 171).
Table 1 shows the prevalence rates claimed for each of the gambling screens and the total number of ‘problem gamblers’ (n = 68) captured by this method (CGR 2004, p. 92). From these data it is possible to estimate the number of respondents classified as problem gamblers by each screen (VGS: n = 18, CPGI: n = 23, SOGS: n = 27). These figures are small and considerable doubt must, therefore, exist that the results form a statistically reliable base from which to extrapolate levels of problem gambling prevalence in a general population of approximately 3.4 million adults, or as any base for generalisations across other states and nationally.
Table 1
Problem gambling comparison and sample sizes: 2003 longitudinal community attitudes survey
Problem gambling screens
Sample
Preval (%)
Total (n)
Regular gamblers only
433
  
 Victorian Gambling Screen (VGS)
149
0.74
18
 Canadian Problem Gambling Index (CPGI)
141
0.97
23
 South Oaks Gambling Screen (SOGS)
143
1.12
27
Number of ‘problem’ gamblers in the sample
  
68

Context Effects

Situational cues are known to affect the interpretation of behaviours (Trope 1986), therefore, adding a series of questions before the SOGS and other measurement scales is likely to distort responses. The SOGS (and its successor as a tool to estimate gambling prevalence, the CPGI) were designed and validated as stand-alone instruments. Yet, immediately preceding the SOGS questions the CGR (2004) ask a range of gambling policy-related questions that present EGMs in a negative way (e.g. ‘Gaming machines should give on-screen warnings about problem gambling’; ‘The Victorian Government should reduce the number of poker machines’). With these questions as the immediate lead, context effects are likely to impact the study and contribute towards contaminating the reliability and validity of the data collected. The CGR (2004), and subsequent users of the findings, did not inform readers of the possibility of context effects and the use of survey instruments outside their validated stand-alone environment.

Conclusions About the CGR’s Claims

Interpreting statistical inferences requires the specification of acceptable levels of statistical error (Hair et al. 1998). Commenting specifically about gambling prevalence studies, Volberg et al. (1998) state confidence intervals should be calculated to establish statistical rigour in gambling prevalence studies. However, the CGR (2004) makes no remark about estimated levels of error. It was also not stated why CGR chose to claim levels of problem gambling against SOGS rather than the newer and designed-for-purpose CPGI; although, the probable effect of using CPGI data would have been to lower the rate of problem gambling prevalence in Victoria. Some methodological limitations were stated by CGR (cf. pp. 172–173). However, these limitations do not seem to inhibit or restrict subsequent claims made throughout the report, or on those who have sought to use the findings to influence gambling policy. For example, Livingstone and Woolley (2007) make no reference that the CGR research had any limitations or that the low numbers of respondents for the gambling screens, and the low levels of statistical power that suggest the probability the reported level of problem gambling occurred by ‘chance’, cannot be ruled out.

Study 2: The Caraniche Study

Data from the 2005 Caraniche study, Evaluation of Electronic Gaming Machine Harm Minimisation Measures in Victoria, has commonly been used to establish levels of gambling expenditure (Livingstone and Woolley 2007; Productivity Commission 2010). This presumes Caraniche’s self-report data about expenditure is accurate and suitable for inference across the Victorian population. The only limitation of the Caraniche report noted by the Productivity Commission Report (2010) was that the ‘sampling method—while appropriate for Caraniche’s analysis—was based on a non-random (unweighted) sample of patrons, which favoured selection of higher frequency gamblers (who tend to spend more)’ (Productivity Commission 2010, B.26). We identify a range of other limitations below.

‘Opportunistic’ Sampling Method

Non-random sampling is useful to make descriptive comments about the sample itself but it is erroneous to draw conclusions about the population from a non-random sample as it will likely be unrepresentative of the population. Caraniche (2005) sampled approximately 418 people from 11 of Victoria’s 510 gaming venues. They described their methods as ‘opportunistic, particularly in relation to the sample of EGM players…Hence, the findings are suggestive and not definitive’ (p. 75). However, elsewhere Caraniche (2005) claim that ‘The venue sample also was representative of a cross-section of Victorian gaming venues (as defined by the data held by the Victorian Commission for Gambling Regulation), based on population, the numbers of EGMs in the area/region, the average annual expenditure/revenue figures, and with regard to the varying socioeconomic profiles of regions’ but that ‘In reality, the final selection of venues was marginally determined by the willingness of the industry operators and their venues to participate in the study’.5
In Victoria (as elsewhere in Australia), there are two types of venue. Clubs are community-based non-profit organisations, and pubs or hotels operate as commercial profit-oriented venues. The differences in the venue type are typically manifest by variations in liquor and gambling licensing conditions (such as entry conditions). To examine the veracity of Caraniche’s claims that their venue sampling frame was a representative cross-section, the number of EGMs and the expenditure for each category of venue in Caraniche’s sample is compared with Victorian state averages. Table 2 shows the number of clubs and hotels in Victoria, and their rural and metropolitan categorisation. This table suggests considerable over sampling of metropolitan hotels compared to country hotels and all clubs.
Table 2
Location of sample and venue type
Location
Club
Hotel
Country area
118
66
Metropolitan area
148
178
Population total
266
244
Caraniche sample (country)
3
1
Caraniche sample (metropolitan)
2
5
Table 3 compares Caraniche’s club and hotel sample against Victorian state averages. This reveals the levels of annual expenditure for Caraniche’s club sample exceeds the general population’s average by 84 %. Furthermore, the number of EGMs in Caraniche’s club sample exceeds the general population by 66 %. Similarly, a comparison of hotels shows the average expenditure for the Caraniche hotel sample exceeded the hotel population by 65 %, while the average number of EGMs in the Caraniche sample exceeded the average number of EGMs in the Victorian hotel population by 75 %.
Table 3
Club and hotel EGM expenditure and numbers: sample versus population averages
Location
A$ Expend. 2005–2006
No. of EGMs
Royal Oak—Richmond Football Club
 Metro
$4,765,832
80
Shepparton RSL Club
 Country
$5,033,717
80
Turfside Tabaret Club
 Country
$5,432,571
75
Geelong Combined Leagues Club
 Country
$7,503,352
105
Dandenong RSL Club
 Metro
$5,387,782
65
Caraniche club sample averages
$5,624,651
81.0
All club averages
$3,053,744
48.8
Rosstown Hotel
 Metro
$14,184,654
103
Manhattan Hotel
 Metro
$9,942,077
103
Village Green Hotel
 Metro
$13,709,283
103
Wodonga Hotel (Elgins)
 Country
$5,707,799
80
Highpoint Taverner
 Metro
$8,307,250
86
Excelsior Hotel-Motel
 Metro
$13,387,331
105
Caraniche hotel sample averages
$10,873,066
96.7
All hotel averages
$6,571,750
55.3
These data provide evidence that Caraniche’s (2005) venue sampling frame is considerably larger in terms of expenditure and EGM numbers than the state averages. Thus, the sample is not representative of the overall population.

Respondent Selection

It is a well-known industry rule-of-thumb in the Victorian hospitality industry that more people frequent clubs and hotels on Friday nights and on Saturdays than at other times in the week. Empirical support for the rule-of-thumb about patronage is drawn from Roy Morgan Research (RMR) data for the metric ‘time of day you last played poker machines’. Between January 2004 and June 2009 approximately 30 % of people last played EGMs on a Saturday, over 20 % last played on a Friday night, and approximately 10 % played on Sunday. By contrast, Caraniche’s Table 4.5 collected only 17 % of their responses from Friday and Saturday, and no data were collected from Sunday players. In making these comparisons, two key differences between the Caraniche and RMR data must be noted. First, RMR ask about the last time someone played. By comparison Caraniche require people to recall each purchase incident over 12 months and calculate an average. Second, the RMR data are drawn from throughout Australia compared to the state-level focus by Caraniche. Nonetheless, it is extremely unlikely that Caraniche’s data is representative of Victoria and on the data presented seems biased towards gaming venues with more EGMs and expenditures substantially higher per machine than the state’s average.

The Risky Definition of ‘Spend’

Caraniche (2005) asked respondents about their gambling expenditure only in question 6: ‘Thinking over the last 12 months, how much money, on average, would you spend per session playing poker machines?’. Several points can be made about this question. First, there is a difficulty obtaining accurate estimates of self-report purchase behaviours. Respondents can misunderstand questions, misinterpret instructions and there is no guarantee respondents will be honest even if they do know their expenditures. Blaszczynski et al. (2006) regard the question ‘how much do you spend on gambling’ as ambiguous with multiple and different meanings. In gambling parlance, ‘spend’ can be interpreted as: stake, net losses, outlays, average bet, etc. In the absence of instructions estimating ‘expenditure’ half the sample uses net expenditure and the remainder use turnover (Blaszczynski et al. 2006). Similarly, McCready and Adlaf (2006, p. 8) conclude: ‘there is considerable doubt about subjects’ ability to accurately recall and estimate gambling frequency, duration and spending…causing a significant number to consider data unreliable’.
Second, Caraniche’s question 6 does not ask respondents about their last session or their current session of gambling or a direct question asking recall of a particular incidence. Rather, it is expected respondents be able to access long-term memory to calculate new knowledge of their average EGM ‘spend’ for each session played over a 12-month period. The existence of prior learning, the storage of accurate and unmodified memories and recall being consistent between individuals are important for the accuracy of self-report data. Except in extreme cases, it seems an insurmountable challenge to accurately recall each EGM session ‘spend’ over a 12-month period and calculate an average. Further, there is a significant tendency for recency effects, which is typically manifest by respondents selecting the last choice read to them in surveys (Bishop 1990; Mullner et al. 1982). This would suggest bias towards heavier expenditure per session of EGM activity. The literature confirms the unreliability of self-report gambling expenditure. Williams and Wood (2004), for example, find self-reported gaming expenditures are 2.1 times higher than actual revenues.

Caraniche’s Definition of Problem Gambling

Caraniche (2005) use the CPGI to define problem gamblers for their study. A 31-item scale, the CPGI is often reduced to 9 questions to solely assess possible levels of problem gambling. As a scale, the CPGI has been publicly scrutinised and undergone item, confirmatory and reliability analysis, plus test and re-test analysis as part of their development in Canada.
As was noted with the SOGS above, the CPGI is also not without criticism. In their review of the CPGI, McCready and Adlaf (2006, p. 23) comment that: ‘our understanding of gambling problems is so underdeveloped that no gambling instrument, including the CPGI, can be considered valid. They ask how we can measure something we don’t understand’. To our knowledge, there has been no reliability or validity testing of the CPGI in Australia against clinically determined problem gamblers.

Discussion and Conclusion

In the two state commissioned studies examined, we have identified a range of methodological and analytical limitations, with subsequent implications for reporting on gambling prevalence, particularly the ‘problem’ or at-risk gambler, and gambling expenditure. It was also noted that these two studies have since been widely cited in academic research and Australian government policy reports such as the Productivity Commission (2010) with disregard to their limitations. This is perhaps unsurprising given the high level of academic involvement in the reports, especially CGR (2004). Such academic contributors are no doubt well familiar with the gambling literature including McGowan’s (1997) discussion of bias in gambling research, and Blaszczynski et al. (2004) who argue that most gambling policy is not based on robust empirical data and that ‘only by confronting the reality of empirical data can the gambling industry develop and sustain long-term responsible gaming practices that assure harm minimisation’ (p. 306). Consequently, it could be difficult to establish what CSR should entail when the ‘problems’ associated with electronic gaming remain in dispute. However, we are drawn to conclude that many of the arguments made in the two studies are likely invalid and, therefore, unsuitable for generalisation and subsequent policy-making. The paradox is that the two studies have effectively been generalised by a number of industry stakeholders to account for the level or prevalence of problem gamblers (and their excessive expenditure on EGMs) in Australian society.
By discussing a number of limitations within these studies that ordinarily would reduce their utility, our interest lies in showing why they remain influential and highly cited reports in the Australian debates concerning EGMs. Exploiting methodological problems for the purposes of pursuing a particular objective is not limited to controversial industries, or sponsored research but theoretically explaining this behaviour is necessary.
We assume that gambling researchers, like other social scientists, are trained in suitable methodologies including sampling and statistical techniques, and are well versed with the shortcomings of their instrument measures, for example. Therefore, we are surprised that, on the balance of reason, this has not previously been raised by key gambling industry stakeholders as a matter of concern. To account for this ‘puzzle’, we drew on legitimacy theory (e.g. Suchman 1995), and ‘morality policy’ from political science (e.g. Meier 1994) to show why and how EGMs as a sub-sector of gambling can be subject to sharply polarised opinion and subsequent contestation among relevant stakeholder groups. Gambling is an activity that touches on those ‘core’ values to be supported (or not) by polity and while it may be considered ‘sin’ by some, we concur with Mucciaroni (2011) that the framing of an issue is of utmost importance in the area of morality policy.
Given the unlikely position that gambling will soon be criminalised in many countries including Australia due to the many benefits it provides government and its citizens (see Schwartz 2003), the framing deployed by opponents of electronic gaming has been to cast the activity as a public health issue. This is where at-risk or ‘problem’ gamblers and ‘harm’ minimisation (Banks 2011; Hing 2001; Hing and McMillen 2002) have become terms used extensively in policy debates. It appears difficult to refute such strong words and influences the polity to take steps to minimise gambling per se. Nonetheless, the association of ‘problem’ with gambling as well as recent pressure on the Australian government by independent parliamentarians to implement tighter mandatory safeguards on betting (see Prior Jonson et al. 2012) shows the contradictions within a polity that appears willing at times to cultivate values that denigrates certain activities, while simultaneously generating revenue from those same activities.
Consistent with the politics of morality policy, we expect a high level of participation in debates and discussion surrounding EGMs (see Mooney 1999). This could involve ‘repeating’ the same basic message by either supporters or opponents of gambling. The domain of research, seldom the topic of examination in legitimacy theory and morality policy, was chosen as the setting because this is one way of communicating and persuading others’ about the merits of one’s case. Indeed, adopting widely approved scientific procedures is part of the means of building or refuting moral legitimacy (Suchman 1995). Thus, according to our theoretical explanation, relying on arguably invalid results is not necessarily irrational when moral legitimacy and morality policy are at stake. If the findings can usefully serve the arguments of one side or the other in a polarised debate, they will likely be (repeatedly) diffused through different forums because it is politically expedient to do so. This shows the politics surrounding controversial industries such as electronic gambling. In the present case, we identify some anti-gambling interest groups who appropriated the results of the two studies to their ends (e.g. through peer-reviewed journals and other outlets). However, it is equally plausible this argument may similarly hold for the pro-gambling interest groups although we did not examine this here.
If our argument is correct, whether any research is actually conducted well with its adherence to the usual standards of science (e.g. rigour and verification) may be immaterial to either side of the debate. The appearance of being objective using scientific techniques, even if problematic, may be all that matters. Clearly establishing whether such action on the part of some was deliberate or intentional rather than mere carelessness or oversight is a notoriously difficult task (cf. Cossette 2004). But where morality policy is involved, as has been argued for electronic gambling, it is more likely that errors of commission or omission in research may be forgiven by those who seek to use such findings, and equally critiqued by those who adopt a contrary view of the industry. It is, therefore, little surprise that the task of gaining legitimacy is harder than the task of maintaining it (Suchman 1995, p. 593). We go further by suggesting that in the presence of morality policy and subsequent framing issues, that moral legitimacy (and by extension, issues concerning CSR) may never be satisfied.
As discussed earlier, there has been very little research which explores the CSR practices of firms in the EGM sector. This has compounded our difficulty to better understand the current state of CSR in this industry. For opponents of the industry at least, the lack of CSR data would reinforce the idea that operators are not highly engaged in socially responsible standards. Resistance by gambling operators to any new mandatory controls on limiting gambling expenditures currently being considered by the polity (Prior Jonson et al. 2012) would also support this conclusion. What research there is has tended to show that gambling operators in Australia are conscious of their economic and legal obligations but perhaps do not go much beyond this (Hing 2001). They prefer any additional CSR-type measures to be voluntary, and applied at the level of the operator rather than mandatory ones that uniformly cover the entire industry. The recent development of allowing Salvation Army staff into some gaming venues to assist those who appear to have problems with gambling is an example. This points to some resistance to the possibility of new legal obligations for the industry; from its perspective the proposed changes are disproportionate and driven by the very small percentage of problem gamblers in Australian society (no more than 1.5 %) who spend more than they should. It ignores the significantly larger number of people who gamble but are not deemed to have any ‘problem’.
The limited research data available does restrict our capacity to comment more definitively on current CSR practices in the industry. However, our analysis of research documenting the extent of gambling as a social ‘problem’ would predict that communicating about any CSR practice in electronic gambling is likely to suffer from credibility problems. This is because the two studies examined have become highly influential in academic and policy circles in Australia (e.g. Productivity Commission 2010). By successfully reinforcing the findings of these studies in different settings, opponents of the industry are able to create a ‘consensus’ of agreement concerning the problems created by electronic gaming. Gambling operators that engage in CSR practices will, therefore, need to appreciate the related issues of legitimacy, morality policy and framing when publicising their social actions. This introduces a necessary caution into the debates about the extent of CSR activities and processes in this sub-sector more broadly. Merely communicating any responsible actions undertaken by gambling operators will be insufficient as the receptivity of such information is subject to contestation by interests opposed to electronic gambling.
Finally, we recognise that our article, focused on a controversial industry sector, may generate controversy because it highlights why and how research in one area (electronic gambling) may not be as robust and independent of ideological views, as many could consider. Therefore, we inquire how codes of research ethics (see Schminke 2009) can be used to more adequately guide behaviour in fields which are subject to the politics of morality policy.
Acknowledgments
We sincerely thank the three anonymous referees for providing incisive and detailed comments on earlier versions of the article. Collectively, their comments and suggestions have helped to produce a much improved article.
Conflict of interest
The first author has previously worked for, and acted as a consultant to, the gambling industry and identified some of the issues raised in this paper during consulting roles. The second author has never been associated with or interested in gambling or the gambling industry. The authors declare no conflicts of interest and no financial support for the authorship of this article.
References
Abraham, J. (1995). Science, politics and the pharmaceutical industry: Controversy and bias in drug regulation. London: Routledge.
Adams, P. J. (2011). Ways in which gambling researchers receive funding from gambling industry sources. International Gambling Studies, 11(2), 145–152.CrossRef
Australian Gaming Council. (2001). Responsible gaming code: A framework for responsible gaming. Melbourne: Australian Gaming Council.
Banks, G. (2011). Evidence and social policy: The case of gambling. Presentation to the South Australian Centre for Economic Studies, Adelaide, 30 March.
Baur, D., & Palazzo, G. (2011). The moral legitimacy of NGOs as partners of corporations. Business Ethics Quarterly, 21(4), 579–604.CrossRef
Bishop, G. (1990). Issue involvement and response effects in public opinion surveys. Public Opinion Quarterly, 54(2), 209–218.CrossRef
Blaszczynski, A. (circa 2001). Harm minimisation strategies in gambling: An overview of international initiatives and interventions. Report prepared for the Australian Gaming Council.
Blaszczynski, A., Ladouceur, R., Goulet, A., & Savard, C. (2006). How much do you spend gambling? Ambiguities in questionnaire items assessing expenditure. International Gambling Studies, 6(2), 123–128.CrossRef
Blaszczynski, A., Ladouceur, R., & Shaffer, H. (2004). A science-based framework for responsible gambling: The Reno model. Journal of Gambling Studies, 20(3), 301–317.CrossRef
Borna, S., & Lowry, J. (1987). Gambling and speculation. Journal of Business Ethics, 6(3), 219–224.CrossRef
Brown, W. (1998). Funders and research: The vulnerability of the subject. In K. Whitfield & G. Strauss (Eds.), Researching the world of work: Strategies and methods in studying industrial relations. Ithaca, NY: Cornell University Press.
Cai, Y., Jo, H., & Pan, C. (2012). Doing well while doing bad? CSR in controversial industry sectors. Journal of Business Ethics, 108(4), 467–480.CrossRef
Campbell, F. (1976). The virtues of gambling. Business and Society Review, 17, 63–64.
Caraniche Pty Ltd (Caraniche). (2005). Evaluation of electronic gaming machine harm minimisation measures in Victoria. Melbourne: Department of Justice.
Centre for Gambling Research (CGR). (2004). 2003 Victorian longitudinal community attitudes survey. Melbourne: Gambling Research Panel.
Cho, C., & Patten, D. (2007). The role of environmental disclosures as tools of legitimacy: A research note. Accounting, Organizations and Society, 32(7/8), 639–647.CrossRef
Cosgrave, J. S. (2010). Embedded addiction: The social production of gambling knowledge and the development of gambling markets. Canadian Journal of Sociology, 35(1), 113–134.
Cossette, P. (2004). Research integrity: An exploratory survey of administrative science faculties. Journal of Business Ethics, 49(3), 213–234.CrossRef
Cowen, H., & Goulbourne, H. (1998). From academe to polity: Tensions in conducting policy and community research. Policy Studies, 19(1), 5–17.CrossRef
Dickerson, M., Allcock, C., Blaszczynski, A., Nicholls, B., Williams, R., & Maddern, R. (1996). An examination of the socio-economic effects of gambling on individuals, families and the community including research into the costs of problem gambling in New South Wales. Report for the NSW Government, Sydney.
Dickerson, M., McMillen, J., Hallebone, E., Volberg, R., & Woolley, R. (1997). Definition and incidence of problem gambling, including the socio-economic distribution of gamblers. Report for the Victorian Casino and Gaming Authority, Melbourne.
Doran, B., McMillen, J., & Marshall, D. (2007). A GIS-based investigation of gaming venue catchments. Transactions in GIS, 11(4), 575–595.CrossRef
Doucouliagos, H., Laroche, P., & Stanley, T. (2005). Publication bias in union-productivity research? Relations Industrielles, 60(2), 320–347.CrossRef
Elsbach, K. (1994). Managing organizational legitimacy in the California cattle industry: The construction and effectiveness of verbal accounts. Administrative Science Quarterly, 39(1), 57–88.CrossRef
Gambino, B. (2005). Interpreting prevalence estimates of pathological gambling: Implications for policy. Journal of Gambling Issues, 14, 26–44.CrossRef
Grinols, E., & Mustard, D. (2001). Management and information issues for industries with externalities: The case of casino gambling. Managerial and Decision Economics, 22(1/3), 1–3.CrossRef
Hair, J., Anderson, R., Tatham, R., & Black, W. (1998). Multivariate data analysis. Englewoods Cliffs, NJ: Prentice-Hall.
Hancock, L., Schellinck, T., & Schrans, T. (2008). Gambling and corporate social responsibility (CSR): Re-defining industry and state roles on duty of care, host responsibility and risk management. Policy and Society, 27(1), 55–68.CrossRef
Hing, N. (2001). Changing the odds: A study of corporate social principles and practices in addressing problem gambling. Journal of Business Ethics, 33(2), 115–144.CrossRef
Hing, N., & McMillen, J. (2002). A conceptual framework of the corporate management of social impacts: The case of problem gambling. Business and Society Review, 107(4), 457–488.CrossRef
Horn, B. (1997). The courage to be counted. Journal of Gambling Studies, 13(4), 301–307.CrossRef
Johnson, J., & Holub, M. (2003). Questioning organizational legitimacy: The case of US expatriates. Journal of Business Ethics, 47(3), 269–293.CrossRef
Jones, P., Hillier, D., & Comfort, D. (2009). Corporate social responsibility in the UK gambling industry. Corporate Governance, 9(2), 189–201.CrossRef
Ladouceur, R., Bouchard, C., Rhéaume, N., Jacques, C., Ferland, F., Leblond, J., et al. (2000). Is the SOGS an accurate measure of pathological gambling among children, adolescents and adults? Journal of Gambling Studies, 16(1), 1–24.CrossRef
LaPlante, D., Nelson, S., LaBrie, R., & Shaffer, H. (2009). Disordered gambling, type of gambling and gambling involvement in the British gambling prevalence survey 2007. European Journal of Public Health, 21(4), 532–537.CrossRef
Lesieur, H., & Blume, S. (1987). The South Oaks Gambling Screen (SOGS): A new instrument for the identification of pathological gamblers. American Journal of Psychiatry, 144(9), 1184–1188.
Livingstone, C., & Woolley, R. (2007). Risky business: A few provocations on the regulation of electronic gaming machines. International Gambling Studies, 7(3), 361–376.CrossRef
Marshall, D. (2004). Gambling, public health and the role of the federal government. Australian Journal of Primary Health, 10(1), 56–63.CrossRef
McCready, J., & Adlaf, E. (2006). Performance and enhancement of the Canadian Problem. Gambling Index (CPGI): Report and recommendations. Report for the Canadian Centre on Substance Abuse, Ottawa.
McGowan, R. (1997). The ethics of gambling research: An agenda for mature analysis. Journal of Gambling Studies, 13(4), 279–289.CrossRef
McMillen, J., & Doran, B. (2006). Problem gambling and gaming machine density: Socio-spatial analysis of three Victorian localities. International Gambling Studies, 6(1), 5–29.CrossRef
McMillen, J., & Wenzel, M. (2006). Measuring problem gambling: Assessment of three prevalence screens. International Gambling Studies, 6(2), 147–174.CrossRef
Meier, K. (1994). The politics of sin. Armonk, NY: M.E. Sharpe.
Millhouse, H., & Delfabbro, P. (2008). Investigating preferences for gaming machine features in problem and non-problem gamblers using a consumer choice methodology. Gambling Research, 20(1), 37–49.
Mohrman, S., Gibson, C., & Mohrman, A. (2001). Doing research that is useful to practice: A model and empirical exploration. Academy of Management Journal, 44(2), 357–375.CrossRef
Mooney, C. (1999). The politics of morality policy. Policy Studies Journal, 27(4), 675–680.CrossRef
Mucciaroni, G. (2011). Are debates about ‘morality policy’ really about morality? Framing opposition to gay and lesbian rights. Policy Studies Journal, 39(2), 187–216.CrossRef
Mullner, R., Levy, P., Byre, C., & Matthews, D. (1982). Effects of characteristics of the survey instrument on response rates to a mail survey of community hospitals. Public Health Reports, 97(5), 465–469.
Newton, L. (1993). Gambling: A preliminary inquiry. Business Ethics Quarterly, 3(4), 405–418.CrossRef
Office of Economic and Statistical Research (OESR). (2011). Australian gambling statistics (AGS) 1983–84 to 2008–09 (27th ed.). Brisbane: Queensland Treasury.
Palazzo, G., & Scherer, A. (2006). Corporate legitimacy as deliberation: A communicative framework. Journal of Business Ethics, 66(1), 71–88.CrossRef
Phillips, R. (2003). Stakeholder legitimacy. Business Ethics Quarterly, 13(1), 25–41.CrossRef
Pierce, P., & Miller, D. (1999). Variations in the diffusion of state lottery adoptions: How revenue dedication changes morality politics. Policy Studies Journal, 27(4), 696–706.CrossRef
Prior Jonson, E., Lindorff, M., & McGuire, L. (2012). Paternalism and the pokies: Unjustified state interference or justifiable intervention? Journal of Business Ethics, December 23, 2011 (online).
Productivity Commission. (2010). Gambling (Vol. 1 and 2). Canberra: Australian Government Productivity Commission.
Rodda, S., Brown, S., & Phillips, J. (2004). The relationship between anxiety, smoking, and gambling in electronic gaming machine players. Journal of Gambling Studies, 20(1), 71–81.CrossRef
SACES. (2008). Social and economic impact study into gambling in Tasmania. Adelaide: South Australian Centre for Economic Studies.
Schellinck, T., & Schrans, T. (Focal Research Consultants). (2003). Surveying all adults in a household: The potential for reducing bias in prevalence studies and the opportunity to study households with more than one problem gambler. Gambling Research, 15(1), 51–62.
Schepers, D. (2010). Challenges to legitimacy at the Forest Stewardship Council. Journal of Business Ethics, 92(2), 279–290.CrossRef
Schminke, M. (2009). The better angels of our nature – ethics and integrity in the publishing process. Academy of Management Review, 34(4), 586–591.CrossRef
Schwartz, M. (2003). The ‘ethics’ of ethical investing. Journal of Business Ethics, 43(3), 195–213.CrossRef
Shaffer, H., & Hall, M. (2001). Updating and refining prevalence estimates of disordered gambling behaviour in the United States and Canada. Canadian Journal of Public Health, 92(3), 168–172.
Sonpar, K., Pazzaglia, F., & Kornijenko, J. (2010). The paradox and constraint of legitimacy. Journal of Business Ethics, 95(1), 1–21.CrossRef
Suchman, M. (1995). Managing legitimacy: Strategic and institutional approaches. Academy of Management Review, 20(3), 571–610.
Trope, Y. (1986). Identification and inferential processes in dispositional attribution. Psychological Review, 93(3), 239–257.CrossRef
Volberg, R., Moore, W., Christiansen, E., Cummings, W., & Banks, S. (1998). Unaffordable losses: Estimating the proportion of gambling revenues derived from problem gamblers. Gaming Law Review, 2(4), 349–360.CrossRef
Williams, R., & Wood, R. (2004). The proportion of gaming revenue derived from problem gamblers: Examining the issues in a Canadian context. Analyses of Social Issues and Public Policy, 4(1), 33–45.CrossRef
Wilson, A., & West, C. (1981). The marketing of ‘unmentionables’. Harvard Business Review, 51(1), 91–102.
Winn, M., MacDonald, P., & Zietsma, C. (2008). Managing industry reputations: The dynamic tension between collective and competitive reputation management strategies. Corporate Reputation Review, 11(1), 35–55.CrossRef
Woolf, P. (1991). Accountability and responsibility in research. Journal of Business Ethics, 10(8), 595–600.CrossRef
Footnotes
1
A discussion of bias might be particularly problematic with issues where any harms are regarded as personal as well as social. Gambling, alcohol and tobacco are among the more well-known examples but there might be other less familiar ones. Hospital waiting lists could be one such example; it doesn’t really matter how long they are if you are the person waiting. Their mere existence is the ‘problem’.
 
2
For an example regarding the impact of trade unions on productivity, see Doucouliagos et al. (2005).
 
3
A search of Google scholar suggests the two reports have been cited in total in at least 80 academic studies as well as government reports, including the highly influential 2010 Productivity Report into Gambling. However, not all of the government reports, including references from various court and tribunal hearings are found in the Google search engine.
 
4
Victoria is located in the south east of the country and is the second largest state in terms of number of inhabitants, constituting approximately 25 percent of the national population.
 
5
The Caraniche report has no page numbers. For a point of reference these comments were found near Table 4.2.