Corporate community involvement disclosures in annual report: A measure of corporate community...

Corporate community involvementdisclosures in annual reportA measure of corporate community

development or a signal of CSR observance?

Kemi YekiniDepartment of Accounting and Finance, Leicester Business School,

De Montfort University, Leicester, UK, and

Kumba JallowDepartment of Strategic Management and Marketing,

Leicester Business School, De Montfort University, Leicester, UK

Abstract

Purpose – The purpose of this study is to examine whether corporate community involvementdisclosures (CCID) in annual reports can be construed as a measure of corporate communitydevelopment (CCD) or a mere signal of corporate social responsibility (CSR) observance.

Design/methodology/approach – Using content analysis and a quality score index, the studyexamined a panel data set covering the period from 1999 to 2008. The data was collected from a sampleof 270 annual reports of 27 UK companies taken from the top 100 companies for corporateresponsibility (BITC ranking, 2008). The research framework involves the use of signalling theory toinvestigate the information content of CCID.

Findings – It is found that the volume of corporate community disclosure (CCID) has a significantassociation with its total quality score (TQS) although the impact was found to be very small. CCIDwas also found to be strongly and positively associated with the volume of total CSR disclosed inannual reports. Hence the quantity and quality of CCID in annual reports increased significantly as thequantity of CSR disclosure also increased. Furthermore, the TQS was found to respond to companysize and Corporate Governance measures such as audit committee size and board composition, and theexistence of standalone CSR Reports, while other measures of public pressure such as leverage,profitability and industrial sector were not statistically significantly related with TQS.

Originality/value – This paper contributes to CSR literature in general and CCID literature inparticular. The originality stems from the fact that it employs a signalling framework and a panelstudy approach as opposed to cross-sectional only or time-series only data to examine a lessresearched social disclosure – corporate community involvement.

Keywords United Kingdom, Corporate governance, Social responsibility, Annual reports,Community involvement, Disclosures, Signalling theory

Paper type Research paper

The current issue and full text archive of this journal is available at

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The authors wish to acknowledge the suggestions and comments received on earlier versions ofthis paper from colleagues at the De Montfort University, British Accounting andFinance Association (BAFA) Doctoral Colloquium held at Aston University Birmingham, theCorporate Governance (CROG) Conference and other seminars held at the Leicester BusinessSchool, De Montfort University. The authors are particularly grateful for the helpful suggestionsand comments of Dr Ismail Adelopo and Dr Panagiotis Andrikopoulos on an early draft of thispaper. Finally, the valuable comments and suggestions from the two anonymous reviewers aregreatly appreciated.

CCIDs in annualreport

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Sustainability Accounting,Management and Policy Journal

Vol. 3 No. 1, 2012pp. 7-32

q Emerald Group Publishing Limited2040-8021

DOI 10.1108/20408021211223534

1. IntroductionIn the first paper published by the journal Accounting, Auditing & Accountability,Gray et al. (1988) argued that corporate social responsibility (CSR) reporting was linkedto accountability and the social contract, despite the latter concept being ill-defined andunder-developed. Nevertheless, we can see from this the idea that an organisationobtains, in effect, a licence to operate which may be revoked if society believes it to beoperating outside of what is acceptable to society. Hence, societal pressure to act onbehalf of members of that society may inform the development of a CSR response orstrategy. In this way, organisations align their values with that of society (Garriga andMele, 2004). They then have a responsibility to ensure that the actions they take and theactivities they engage in are to the benefit of society. One such set of actions iscommunity involvement, or community development activities, which is the focus ofthis paper.

Managers are able to enhance their value maximization strategy by reporting thecompany’s financial performance through the annual reports. Can they also demonstratetheir moral responsibility by reporting on the community activities undertaken by thecompany? Can disclosures of corporate community involvement activities (CCID) inannual reports be read and interpreted as measures of corporate communitydevelopment (CCD)? Or are they mere signals of adherence to societal expectationsaccording to signalling theory? Signalling theory requires that a quality signal shouldfulfil the condition of being inimitable to produce by a false image builder. To this end,for the disclosure of community involvement in annual reports (CCID) to qualify as aquality signal of community development, the disclosure should give specific details ofquantifiable and verifiable projects which are difficult to mimic (Toms, 2002).

The research question posed by this paper is whether or not corporate communityinvolvement disclosures (CCID) in annual reports can be construed as a measure of CCDor whether such disclosures are merely motivated by the pressures on managers todisclose CSR activities. To this end, the relationship between the quality (shown by thetotal quality score, or TQS) and the volume of community disclosures (VOLCCID) inannual reports was investigated using a panel study approach, as discussed later in thepaper. The research focuses on disclosures in the annual reports; the sample for thestudy was taken from the top 100 companies for corporate responsibility (BITC ranking)taking from the article “Companies that Count” (BITC, 2008, Sunday Times, May).The volume of disclosure was measured using word count as the unit of measurement.The quality of community disclosure was measured using a TQS obtained on afive-element index. The relationships between TQS and measures of public pressure,corporate governance (CG) and the existence of standalone reports were also examined.

We find that the volume of corporate community disclosure (CCID) has a significantassociation with its TQS although the impact was found to be very small. Nevertheless,CCID was found to be strongly and positively associated with the volume of total CSR(i.e. the total of all disclosures of responsible behaviour) disclosed in the annual reports.Hence the more a company discusses its responsible behaviour, the more it has to sayabout its community involvement activities. This indicates that the quantity andquality of CCID in annual reports increased significantly, as we find that the quantityof CSR also increased during the period under review. Furthermore, the TQS wasfound to be associated with company size and CG measures such as audit committeesize (ACS) and board composition (BC), and the existence of standalone CSR

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reports (CSRR), while other measures of public pressure such as leverage, profitabilityand industrial sector were not statistically significantly related with TQS.

The rest of the paper is structured as follows. The next section presents a brief review ofthe literature on the motivations for community and social disclosures. Section 3 discusesthe theoretical underpinnings and the development of the study’s hypotheses. Section 4describes the methodology and findings, while Section 5 relates the applicability of thefindings to the explanatory power of the theories, and concludes on the study.

2. Literature reviewCorporate social and environmental disclosure (CSD) as a recent phenomenon hasgenerated much debate in the accounting literature (Bebbington et al., 2008b). A range ofstudies has demonstrated that CSR disclosure varies across companies in quantity andquality; it may be affected by industry type or size and may develop over time. Earlierstudies include Hackston and Milne (1996), Gray et al. (1995a, b), Deegan and Rankin(1996), Deegan (2002), Holland and Boon Foo (2003). More recently a range of papershave addressed cross-country and developing economy comparison and has examinedCSR reporting from differing ideologies and theoretical frameworks (Ball and Craig,2010; Bessire and Onnee, 2010; Burritt and Schaltegger, 2010; Cho et al., 2010; De Villiersand van Staden, 2010) and for its relationship with accountability (Adams, 2004; Cooperand Owen, 2007). The volume of literature on CSD has focused on the analysis of CSD asto its relevance and the rationale of its provision, and consequently a number of theorieshave been examined in the literature as possible explanations for the disclosure of socialinformation. Such theories include the political economy of accounting (Cooper andSherer, 1984; Campbell, 2000); legitimacy theory (Adams et al., 1998; Campbell, 2000;Deegan, 2002; Bebbington et al., 2008a); stakeholder theory (Collison et al., 2003; van derLaan Smith et al., 2005; Galbreath, 2006; Cooper and Owen, 2007); signalling theory(Toms, 2002; Hasseldine et al., 2005); and the reputation risk management hypothesis(Bebbington et al., 2008a, b; Unerman, 2008; Adams, 2008).

Given that accounting disclosures represent the business language ofcommunication ( Jain, 1973; Belkaoui, 1978), specifically exploring signalling theoryto examine the disclosure of CCI information in the annual report could help decipherwhether or not such disclosures are mere signals of adherence to societal expectationsin the light of the clamour for CSR, or whether such disclosures could actually beconstrued as a measure of the social impact of CCD. The fact that language influencesthinking and hence behaviour has been well argued in the fields of linguistics andpsychology (Gumperz and Levinson, 1996; Lucy, 1992, 1997). Lucy (1997) classified themanner in which language can influence thoughts into three: semiotic or semiology[1],structural, and functional[2]. The argument is that when language is used in aparticular way it may influence thinking and hence have an effect on perception (Lucy,1992, 1997). To this end, one may argue that CCID is a communication strategyadopted by corporations either as a form of measuring their corporate communityinvolvement (CCI) achievements or a way of signalling observance with CSR.

CCI was one of the strategies adopted by various national governments for economicand social regeneration after Second World War as businesses were encouraged tobecome involved with community development (Bush et al., 2008). Consequently,corporations moved from the philanthropic activities prior to Second World War,such as donations and public relations, to community development. It suffices to say


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some development works had, and still have, legislative motivation to comply with agovernment’s regeneration plan. Today some corporations engage in communitydevelopment because of their expressed commitment to observe CSR requirements,while others do so to gain the licence to operate safely in their local community(Adams et al., 1998; Campbell, 2000; Deegan, 2002; Bebbington et al., 2008a).

Nevertheless, there is a dearth of literature on CCID, despite the fact that “thecommunity” has been identified as one of the important members of the stakeholdersystem (Clarkson, 1995; Altman, 2000). The first mention of community involvement bybusiness organisations was in 2002 (Hess et al., 2002), although this did not discussdisclosure. Branco and Rodrigues (2008) attempted to include an analysis of communityinvolvement disclosures within, but not separately to, general CSR disclosure. Otherstudies too have examined CCID along with other social disclosures (Guthrie and Parker,1989; Campbell, 2000), while others have mentioned it as a CSR theme (Patten, 1991).Most studies on CSR have concentrated on other aspects of social and environmentaldisclosures such as human resources, pollution, and so on. Studies on CCID are indeedonly recent; one such is by Campbell et al. (2006) which investigated the pattern andfrequency of CCID. It is therefore the motivation of this paper to rectify this omission.

Epstein and Freedman (1994) conducted experiments to examine communitydisclosure along with other CSR themes; their results indicated that nearly half of therespondents wanted community involvement disclosed in the financial report, andanother quarter want it not only disclosed but also audited. Over 60 per cent of therespondents also required that corporations disclose the social impacts of their activitieson the community groups that they affect. Cowen et al.’s (1987) study found thatcommunity disclosure responded to company size and industry type with 64 per cent ofcompanies (mostly in the chemical industrial sector) disclosing it. In contrast to Cowen’sfindings however, Patten (1992) found that community involvement is disclosed in lowervolume than other categories of social disclosures. A more recent study by Campbell et al.(2006), investigating the reporting behaviour of companies over a longer period, found thatvolume and frequency of community disclosure is positively associated with high publicprofile companies, consistent with Cowen et al. (1987). These studies however, merelyindicated the motivation and importance of community disclosure; no study has actuallyresearched the reality of this information, i.e. that there is “active” involvement incommunity development. This therefore provides the motivation for the current study.

3. Theoretical underpinnings and hypothesesSignalling theory is used in accounting research because it enables us to reflect uponthe information asymmetry inherent in a system of reporting where managers have theability to make decisions about what may or may not be disclosed, especially wherethis information is voluntarily disclosed, i.e. not subject to legislative requirements(van der Laan Smith et al., 2005; Watson et al., 2002; Abd-Elsalam and Weetman, 2003).Generally, however, it has not been widely employed in CSR disclosure analysis – anexception being Hussainey and Salama’s (2010) study examining corporateenvironmental reputation.

The decision by managers to disclose can stem from inter alia either the compellingmotivation to respond to public pressure of CSR performance and reputation-building(Toms, 2002; Hasseldine et al., 2005) or to convince community stakeholders of theircommunity development performance. Whatever the case, the contention is that CCID

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is a signal of some sort. One of the conditions for the applicability of signalling theoryis information asymmetry in a competitive environment (Spence, 1973; Watts andZimmerman, 1986), in which case the corporation possesses some qualities of which thestakeholders’ are not aware. The theory posits that if information asymmetry existsbetween the managers of a corporation and its stakeholders, the managers can reduceor eliminate the information asymmetry by providing the necessary information toassist stakeholder in their decision making process (Spence, 1973).

However, for a signal to be accepted as true it must satisfy the condition of beinginimitable to produce by a false image builder. To this end, for CCID to qualify as aquality signal of CCD the disclosure should give specific details of quantifiable andverifiable, difficult to mimic projects (Toms, 2002), such that a non-performer and a mereimage builder without the requisite knowledge and active engagement in such area ofsocial and community programmes would find it difficult to imitate. For instance suchCCD projects may involve investing in an expensive and hard to imitate project such asbuilding schools, health centres and other community projects. A CCID that containsgeneral statements of policy that cannot be matched with the actions of the reportercannot be said to be a quality signal of CCD.

Following from the above discussion therefore, this paper contends that the qualityof a reliable signal of actual CCD performance must be consistent with the volume ofCCID (VOLCCID) over a period of time. In other words if, for instance, the qualityof CCID (measured by variable TQS) is obtained by allocating weights to each mentionof specific verifiable projects (as suggested by Toms, 2002; Hasseldine et al., 2005;Freedman and Stagliano, 2008), then a positively strong association should existbetween TQS and VOLCCID. It follows that community involvement disclosures(CCID) characterised by mere general statements of policies would have very lowquality score which would either have a weak or no relationship at all with VOLCCID,or that VOLCCID will have only a small influence on TQS if few inimitable projects arementioned. Thus, we propose that:

P1. The quality of CCID contained in annual reports as measured by variableTQS will increase as VOLCCID increases across time if CCID is a truemeasure of CCD (i.e. that disclosure reflects reality), while we control for theeffect of corporate governance (CG) and other public pressure measures.

This proposition was operationalised in the null form as follows:

H1. There is no relationship between TQS and VOLCCID.

Furthermore, if CCID is a true measure of CCD, the general pattern of CCID as a signalof CCD is expected to be independent of other disclosures of CSR (OCSRD) over time.This is because public pressure (Patten, 1991, 1992; Hackston and Milne, 1996) andreputation-building (Toms, 2002; Bebbington et al., 2008a) have been documented asmotivations for CSR disclosures in annual reports. To this end we argue that if themotivation for CCID is CSR observant-led, there should be a positive associationbetween the measures of public pressure and the volume, and not with the quality ofCCID. Thus, leading to our second and third propositions:

P2. The volume of CCID will increase as the volume of other CSR disclosures(OCSRD) increases across time, while controlling for the effect of corporategovernance (CG) and public pressures.


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P3. The volume of CCID contained in annual reports will increase as the pressurefor CSR increases, while the quality does not.

Therefore, leading to the following hypotheses in the null form:

H2. There is no relationship between VOLCCID and OCSRD.

H3. There is no relationship between VOLCCID and CG measures.

H4. There is no relationship between TQS and CG measures.

H5. There is no relationship between VOLCCID and measures of Public Pressure.

H6. There is no relationship between TQS and measures of Public Pressure.

4. Research method and analysisAs this study is measuring the relationship that exists between CCID and the factorsmotivating CSR, one-time cross sectional data is unsuitable. In the literature, it has beenargued that to study social behaviour, cross-sectional data will not be enough toestablish changes in social behaviour; rather that combining it with longitudinal datawould be necessary for a better study of social phenomena (Singer and Willett, 2003).Dougherty (2007) argued that although a cross-sectional observation may give an idea ofhow and why changes occur, such results would be spurious and invalid due tounobserved heterogeneity. Therefore, to solve this problem both a cross-sectional andlongitudinal data sets (also called panel data) were collected for this study from theannual reports of 27 companies over a period of ten years. One of the greatest features ofpanel data is its ability to control for this unobserved heterogeneity that could also affectcommunity disclosures, which could otherwise be omitted if solely cross-section datawas used. Unobserved variables could be either those that are constant over time such ascultural orientation or the geographical location of the company, or those that vary overtime such as ethical values (Dougherty, 2007).

The sample for the study was taken from the top 100 companies for corporateresponsibility as shown in the BITC ranking (2008). The data collection method adoptedwas content analysis, which according to Weber (1988) is a method of codifying thecontent of a narrative report using selected criteria or decision rules, thereby deriving aquantitative scale, which then permits further analysis. Effort was therefore made todefine categories as precisely as possible with well defined decision rules and criteria inorder to ensure that they are mutually exclusive and that classification into categories isnot discretionary (Ingram and Frazier, 1980), which of course will allow forreproducibility. Appendix 1 has the details of the decision rules. Previous studies ofsocial disclosure that used content analysis include Ernst & Ernst (1978), Ingram andFrazier (1980), Guthrie and Parker (1990), Gray et al. (1995b), Hackston and Milne (1996),Holland and Boon Foo (2003). Guthrie et al. (2004) provide the justification for the use ofcontent analysis in the present study as they recommend that annual reports “are a goodinstrument to measure comparative positions and trends” (p. 285) and so allow scrutinyvia content analysis. This enables the researcher to explain differences in amountsdisclosed in a set or sample of annual reports in a quantifiable way. Therefore,content analysis is recognised as an acceptable method with which to frame the analysisof our data.

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4.1 Measurement of variables(a) Quality of disclosure. Although the literature supporting disclosure qualitymeasurement is limited, Freedman and Stagliano (1992, 1995) maintained thatmeasuring quality rather than quantity is more important as this conveys the truemeaning and importance of the message. Furthermore, Walden and Schwartz (1997)argued that quality measures provide a systematic and numerical basis for comparingobjectively the content of social disclosures. In addition, quality measure is a valuabletool in a signalling theory framework study (Toms, 2002; Hasseldine et al., 2005) to assistin determining whether CCID is indeed quality signals of CCD. To establish thisrequires a scoring system that allows us to give greater weight to inimitable verifiabledisclosures and lesser weight to general statements (Hasseldine et al., 2005), in whichcase the higher the quality score a company obtains, the higher the signal of CCD.

Consequently, the quality of CCID was measured using a quality score (TQS)obtained on a five-element index. The construction of the index was patterned afterFreedman and Stagliano (1992, 2008) and Walden and Schwartz (1997). First wecategorized CCID into four – community activities, health and related activities,education and the arts, and other community activity (adopted from Ernst & Ernst, 1978;Gray et al., 1995b). Then for the purpose of allocating weight, disclosures were classifiedand weighted as shown in Table I.

Although the CSR literature provided no guidance as to the allocation of weight toclassifications of disclosures, Freedman and Stagliano (2008) argued that a differentialweighting scheme could be justified by the fact that some classifications of disclosurescontain more information than others. For instance, giving specific details of what thecompany has achieved in the area of education or health care is much more informativethan mere general statement about the company’s involvement in community activities;hence, for example, points are awarded for each mention of specific categories of CCIwhile no point is awarded for general statements.

(b) Volume of disclosure. The VOLCCID and OCSRD were measured using word countas the unit of measurement. Although this method of measurement has been used manytimes in the literature (Adams et al., 1998; Gray et al., 1995b), there has been debate on thebest unit of analysis. The arguments revolve around the best way of coding and countingnarrative disclosures (Milne and Adler, 1999). Various measurement methods identified inthe literature includes word count (Deegan and Gordon, 1996; Deegan and Rankin, 1996),sentence count (Hackston and Milne, 1996; Milne and Adler, 1999; Deegan et al., 2002) andpage proportion (Guthrie and Parker, 1990; Gray et al., 1995a, b; Campbell, 2000).Nevertheless, word count as a unit of analysis was adopted in this research to allow acomparison of the extent of disclosure and the quality of items disclosed.

Disclosure classification Score

1. Provision of quantitative information about CCI 22. Provision of photographic information about CCI 13. Detailed description of any category of CCI (2 points for each category) 84. General statement of the company’s CCI policy/activities 05. CCI disclosure located in a separate CSR section of the annual reports 1Total quality score 12

Table I.Quality score index


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(c) Public pressure. Four proxy variables: size, profitability, leverage level, and industrywere used in this study to measure the possible pressure on managers to disclose CCIinformation, where such disclosures are borne out of increased awareness and clamourfor CSR by society. Although no absolute measures have been found for “publicpressure”, in the literature these variables have been used extensively as publicpressure indicators (see Cowen et al., 1987; Guthrie and Parker, 1990; Patten, 1991;Roberts, 1992; Gray et al., 1995a; Hackston and Milne, 1996; Deegan and Gordon, 1996;Garcia-Castro et al., 2010 for details).

“Size” is documented as an important proxy for public pressure in a number of studies(Patten, 1991, 1992; Gray et al., 1995b; Hackston and Milne, 1996; Adams et al., 1998;Campbell et al., 2006; Garcia-Castro et al., 2010). It was operationalised in this studyusing turnover, after exploring other measures of size such as market value, total assetsand total employees, which we found to be multicollinear (Tabachnick and Fidell, 2007).

We measured “profitability” using return on capital employed (ROCE) also definedas return on assets (ROA). ROCE has been used extensively in the literature as ameasure of profitability (Ho and Wong, 2001; Hasseldine et al., 2005), although thefindings on profitability/disclosure relationship are inconclusive. Some studies find anassociation between profitability and social disclosures (Haniffa and Cooke, 2002;Galbreath et al., 2008) while others do not (Ho and Wong, 2001; Hasseldine et al., 2005;Hackston and Milne, 1996). However, we included profitability in our model followingthe argument by Hackston and Milne (1996) that a CSR disclosure/profitabilityrelationship could be an indication of management’s ability to respond to and meetsocial pressure (Hackston and Milne, 1996).

“Leverage” was measured as the ratio of total debt to total equity. Theleverage/disclosure relationship is also inconclusive; some studies found evidence of anegative relationship (Belkaoui and Kahl, 1977; Rahman, 2002), arguing that debtholders demand less public information than do shareholders. On the other hand, whilesome studies found a positive relationship (Tsamenyi et al., 2007; Galbreath et al.,2008), others found no relationship at all (Mangena and Pike, 2005; Garcia-Castro et al.,2010). Watson et al. (2002) argued that signalling theory is inconclusive as to thedirection of leverage/disclosure relationship; however, we included it in our modelsince a recent study found some relationship (Galbreath et al., 2008). Data for turnover,ROA and leverage were collected from the Thomson Reuters DataStream.

“Industry” was operationalised using the Industrial Classification Benchmark(ICB)[3] structure and code index. The ICB classifies companies into sectors which arethen grouped into super sectors and then into industries (ICB Release 4.0, June 2009).The ten industries classification according to the ICB are Oil & Gas, Basic Materials,Industrials, Consumer goods, Health care, Consumer services, Telecommunications,Utilities, Financials and Technology. Each of the ten industrial sectors was representedin our sample, which was later bifurcated into High public profile and Low publicprofile industries patterned after Hackston and Milne (1996) and Campbell et al. (2006).High profile industries are those with high public sensitivity because they interactmore with consumers in their local communities (Campbell et al., 2006); these consist ofConsumer goods, Health care, Consumer services, Telecommunications and Financials.The low profile industries are Basic Materials, Industrials, Oil & Gas, Technologyand Utilities. These industries have less direct interactions with consumers and

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are therefore regarded as having lower public profile (Hackston and Milne, 1996;Campbell et al., 2006).

(d) Other control variables. Following the issues of accountability and controlbrought about by the agency problem because of the separation of power betweenmanagers and owners of companies ( Jensen and Meckling, 1976), Morris (1987) arguedthat agency and signalling theory are consistent theories, therefore one cannot beconsidered without controlling for the effect of the other. Therefore, to test the signallingeffect of CCID in annual reports the researchers considered controlling for the effect ofCG as important. In addition, a thorough review of the literature revealed that a numberof studies documented a positive relationship between CG and social disclosures(Forker, 1992; Ho and Wong, 2001; Wilson and Lombardi, 2001; Haniffa and Cooke, 2002;Galbreath et al., 2008). To this end, we controlled for two commonly used CGmeasures –BC and ACS. The presence of standalone CSRR was also controlled for.

BC was measured as the proportion of non-executive to executive directors (Forker,1992; Ho and Wong, 2001; Haniffa and Cooke, 2002; Webb, 2004; Galbreath et al., 2008).Since non-executive directors are considered as outsiders, it is generally believed thattheir presence in the board represents the interest of other stakeholders apart from theshareholders (Galbreath et al., 2008). Thus, they will have more effective monitoringpowers (Fama, 1980), and hence have a greater influence on the level and quality ofdisclosures in annual reports (Forker, 1992; Mangena and Pike, 2005).

ACS was measured as total number of audit committee members for each company(Mangena and Pike, 2005). Although there is a dearth of empirical evidence on theimpact of ACS on CSR disclosures, the evidence on the impact of ACS on disclosures inannual reports however is mixed. While some studies found no significant relationship(Abbott et al., 2004; Bedard et al., 2004; Mangena and Pike, 2005), some reported apositive relationship (Forker, 1992; Felo et al., 2003), others reported a negativerelationship (Song and Windram, 2004). Nevertheless, we controlled for ACS followingthe recommendation of both the Blue Ribbon Committee (1999) and the SmithCommittee (2003). Both recommended that audit committees should comprise of atleast three non-executive directors.

Existence of standalone CSRR. This variable was introduced in order to control forthe impact of producing a standalone report on the quality of CCI disclosures in theannual report. Gebauer and Hoffmann (2009) asserted that finding CSRR that are ableto achieve the strict (IOW) and “future” criteria[4] is an indication of performance, asmeeting such criteria represents a holistic view of the company’s social andenvironmental activity. Toms (2002) also argued that standalone CSRR are a form ofsignalling tool to communicate reputation in social responsibility. To this end, theresearchers contend that a company that is able to produce a comprehensivestandalone reports should be construed as signalling CSR performance.

4.2 Method of analysisOne simple way of analysing a cross-sectional/longitudinal data is to use the pooledOLS regression, which simply ignores all the special structure of the panel data andtreats it as a pooled regression of y on x using all the control variables (Wooldridge,2009). However, there is the tendency that important invariant unobserved variablesmight be omitted (Baum, 2006). Therefore, to account for all unobservablefirm-specific variables and thus ensure the robustness of our results (Halaby, 2004;


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Garcia-Castro et al., 2010), we controlled for the possible influence of unobservedvariables by using the generalized least square random effect regression model(GLS-RE). Although, as with the pooled OLS, the GLS-RE regression models assumesno correlation between error term (ai) and any of the explanatory variables, but theGLS-RE models take into consideration the fact that some unobserved variables maybe constant over time but vary between companies, while others may be constantbetween companies but vary over time. Furthermore, as panel data set by its verynature (i.e. longitudinal/cross-sectional) will definitely violate least square assumption 5of no autocorrelation (Appendix 2), the GLS-RE regression is particularly relevant inorder to control for the effect of time-constant variables, which will normally lead toserial correlation in the error term (for further details on RE estimators Dougherty,2007; Baum, 2006; Wooldridge, 2009).

Similarly, since assumptions 2 and 4 of the least square regression (Appendix 2)may not hold given the nature of our data, the researchers, in addition to the basicassumptions stated in Appendix 2, assumed as follows in order to use the RE estimatorfor this analysis:

. that the error term e it consist of both a time-constant firm specific variable (ai)and a time-varying firm specific variable (mit). Where e it is the composite errorterm and e it ¼ ðai þ mitÞ and are uncorrelated across time;

. that the idiosyncratic errors ai and the unobserved effects mit that affectcommunity disclosures are uncorrelated with other explanatory variables in alltime periods (the assumption of strict exogeneity); and

. that each of the observed and unobserved variables is drawn randomly from agiven distribution (Dougherty, 2007).

In addition the researchers performed some checks on the data in order to ensure thatassumption 7 (Appendix 2) of normality has been met. To do this, the researcherspredicted the studentized residuals (r) with the predict command in STATA, andplotted a histogram of r for visual analysis. The histogram in Appendix 3 showed r aslooking normally distributed. However, for the sake of clarity, the researchers followedup with the Shapiro-Wilk W-test. The W statistics of the Shapiro-Wilk test indicatesnormality if it is equal to one or as close to one as possible, while the null hypothesis isthat r is normally distributed (Shapiro and Wilk, 1965). The test statistics inAppendix 4 failed to reject the null hypothesis that r is normally distributed as the testshowed p value greater than 0.05.

Nevertheless, to ensure the validity of our results, further tests were carried out tocheck the validity of the random-effect estimator using the Breusch-Pagan Lagrangemultiplier (LM) – (Appendix 5). The Breusch-Pagan LM test is a diagnostic testdesigned specifically to test the efficiency and validity of the RE models in handlingunobserved heterogeneity by Breusch and Pagan (1980, cited in Baum, 2006). In theBreusch-Pagan LM test the squared residuals are regressed on the explanatoryvariables. The null hypothesis is that variances across entities are zero, stated asH0 ¼ no significant differences across entities. If the test fail to reject this hypothesis,the RE model will be considered unsuitable for this analysis (Baum, 2006). However,the test was significant at 1 per cent level in favour of the Random-effect estimator.Nevertheless, in addition to the LM test all regressions were run with the robuststandard error, which automatically adjust all standard errors and p-values for any

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possible effect of heteroskedasticity, outliers and any other irregularities and thusimprove the validity of the results.

4.3 Model specificationTherefore, in order to test the four hypotheses formulated in the previous section thefollowing GLS-RE regression models were specified:

TQSit ¼aþb1VOLCCIDitþb2ACSitþb3BCitþb4CSRRitþb5LnTurnoveritþb6ROAitþb7Leverageitþb8Industryitþuitþ e it ð1Þ

VOLCCIDit¼aþb1OCSRDitþb2ACSitþb3BCitþb4CSRRitþb5LnTurnoveritþb6ROAitþb7Leverageitþb8Industryitþuitþeit ð2Þ

The definitions of the variables in these models as well as their sources are shownin Table II.

Variables Definitions

PredictedRelationship to

TQS Source

TQS TQS obtained by allocating points to thenature of disclosure (Table 1)

Company annualreports

VOLCCID Volume of CCI disclosure based on wordcount

þ /2 Company annualreports

aLnVOLCCID Natural log of VOLCCIDb þ Transformation ofVOLCCID

OCSRD Other CSR disclosures also based on wordcount

þ /2 Company annualreports

aLnOCSRD Natural log of OCSRDb þ Transformation ofOCSRD

ACS Number of Audit Committee members ? Company annualreports

BC Ratio of non-executive to executiveDirectors

þ Company annualreports

CSRR Dichotomous: where 1 ¼ presence ofstandalone CSRR and 0 otherwise

þ Company annualreports

aLnTurnover bNatural log of turnover þ Thomson DatastreamROA ROCE ? Thomson DatastreamLeverage Leverage: ratio of debt to equity 2 Thomson DatastreamIndustryprofile

Dichotomous: where 1 ¼ High publicprofile and 0 ¼ Low public profile

þ (ICB) code index

A The constantB The intercept or coefficients to be

estimatedI The subscript for each organisationT The subscript for time periode Composite error term

Notes: aLn is prefix to denote log transformed variables; bLogarithmic transformations were appliedto these variables in order to reduce skewness; the sign ? denotes the fact that the empirical evidencesis inconclusive on these variables

Table II.Summary and sources of

variables for the study


17

4.4 Results and analysis(a) Descriptive statistics. Tables III and IV show the descriptive statistics for bothdependent and independent variables. The average quality score for communityinvolvement disclosed in the annual reports during the period was 4.86 while averagevolume of disclosure was 486 words with a maximum of 3,271 words. On the otherhand, average disclosures on other CSR themes such as human resources, health andsafety, environmental performance, etc. was 2,068 words with a maximum of9,302 words. On the average, most companies in the sample have four Audit Committeemembers with some companies having a minimum of two and others a maximum ofeight. The board members of most of the companies sampled have an average ofapproximately 60 per cent non-executive directors with a maximum of 89 per cent insome companies.

(b) Correlation analysis for independent variables. Table V shows the correlationmatrix for the explanatory variables. Correlation matrix measures the extent to whichtwo variables change in conjunction with each other. Perfectly or strongly correlatedvariables violate the least square regressions assumption and may lead tomulti-collinearity. Tabachnick and Fidell (2007) explain that when a set of variablesare multicollinear it is an indication that one or more of the variables are redundant andtherefore gives no additional information and would end up inflating the size of theerror terms which may in effect weaken the analysis (Tabachnick and Fidell, 2007).However, since Table V shows low correlation between our explanatory variables, theresearchers are of the opinion that, multicollinearity if any should be minimal.Nevertheless, since a degree of multi-collinearity may still occur with slightly highcorrelation and could affects confidence intervals for coefficients and hence thesignificant levels of variables (Tabachnick and Fidell, 2007), the researcher consideredit wise to check further whether multicollinearity is an issue and if so whether or

Variables Mean SD Minimum Maximum

TQS 4.86 3.20 0 12VOLCCID 486.1 553.54 0 3,271

Table III.Descriptive statistic fordependent variable

Variables Mean SD Minimum Maximum

OCSRD 2,068.3 1,608.9 0 9,302ACS 4.42 1.36 2 8BS 10.7 3.0 5 22BC 0.63 0.12 0.38 0.89CSRRa 0.47 0.50 0 1LnTurnover 14.89 1.80 9.35 19.09ROA (%) 11.54 17.44 244.39 148.71Leverage (%) 50.36 45.57 0 512.71Industryb 0.48 0.50 0 1

Notes: aDichotomous variable for the existence of CSRR where 1 ¼ presence of this variables and 0otherwise; bDichotomous variable for industry sectors where 1 ¼ Industry with High public profileand 0 for those with low public profile

Table IV.Descriptive statistic forindependent variables

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not it is severe. Therefore, the researcher computed variance inflation factor (VIF) inaddition to the correlation matrix. VIF of ten and above are usually regarded asindications of severe multi-collinearity (Baum, 2006). The results are presented alongwith the regression shows in Table VI. The results showed that all the VIF computedwere far below the thresholds of ten. Consequently, the researchers ruled out thetendency of severe multi-collinearity in the research model and are rest assured of theefficiency of the regression results.

(c) Regression results. The regression results are shown in Table VI (panels A and B).The result shown in Table VI (Panel A) shows the regression result for model 1, whereTQS is the dependent variable and Panel B shows the result for model 2 whereVOLCCID is the dependent variable in its natural log form. The regression resultsshow an overall R2 of 44 and 41 per cent for Panels A and B, respectively. The R2 is ameasure used to determine the explanatory power of models. Our results indicated thatthe model specified in equation (1) above was able to explain 44 per cent of thevariation in the total quality of community activities disclosed in annual reports, whileequation (2) model was able to explain 41 per cent of variation in the VOLCCID inannual reports. The results also show rho of 0.35 and 0.36 for Panels A and B,respectively. The rho measures the intergroup correlation (Greene, 2008). The resultsrevealed that about 35 per cent of variations in the quality of community disclosurescannot be explained by differences across entities, while 36 per cent of variations in theVOLCCID are not due to differences across entities. However, the results of testing thevarious hypotheses are discussed in subsequent sections.

Quality and volume of community disclosures. Our first hypothesis was to testwhether any relationship exists between the quality (TQS) and the volume (VOLCCID)of community involvement activities disclosed in the annual reports of the sampledcompanies. Signalling theory postulates that for community disclosures to qualify as atrue signal or as a measure of actual community development, disclosures shouldconsist of verifiable activities. It is therefore expected that as the volume of CCIdisclosures increases the quality should also increase, indicating disclosures of moreverifiable community projects rather than general statements. The regression result inPanel A produced a positive co-efficient of 0.004, indicating that, although the qualityof CCI disclosures of sampled annual reports increased as the volume of CCID

Variables 1 2 3 4 5 6 7 8 9

1.VolCCID 1.002. OCSRD 0.38 1.003. ACS 0.03 0.06 1.005. BC 0.34 0.27 0.36 1.006. CSRRa 0.25 0.35 0.34 0.38 1.007. LnTurn 0.34 0.32 0.42 0.34 0.43 1.008. ROA 0.24 0.07 0.04 0.17 0.04 0.13 1.009. Leverage 0.01 20.07 0.00 0.07 20.03 0.12 0.48 1.00

10. Industry 20.23 0.09 20.11 20.24 20.34 20.17 20.08 20.19 1.00

Notes: aDichotomous variable for the existence of CSRR where 1 ¼ presence of this variables and0 otherwise; bDichotomous variable for industry sectors where 1 ¼ Industry with High public profileand 0 for those with low public profile

Table V.Correlation matrix


19

increased across time, the impact of VOLCCID on TQS is very small, as the coefficientof VOLCCID is almost zero. This indicates that a greater amount of the CCID insampled annual reports were of more general statements than specific details ofachievements.

Volume of community disclosures and other CSR disclosures. Our results in Panel Bshowed a significantly strong positive relationship at 1 per cent level between thevolume of community involvement disclosures and other CSR disclosures. Theimplication of this is that as disclosures of other CSR information such as humanresources, environmental performance, health and safety and other social informationincreased in annual reports, information on community involvement also increased.

Variables Coefficients Robust std. error z-start p-value VIF

Panel A. Random-effect GLS regression result with TQS as dependent variableConstant 20.062 2.118 20.03 0.977VolCCID 0.004 0.001 7.23 0.000 * * * 1.38ACS 0.277 0.161 1.72 0.085 * 1.38BC 0.707 1.658 0.43 0.670 1.41CSRRa 0.894 0.360 2.49 0.013 * * 1.49LnTurnover 0.067 0.159 0.42 0.676 1.55ROA 20.017 0.008 22.09 0.037 * * 1.43Leverage 0.000 0.003 0.06 0.949 1.43Industryb 20.161 0.623 20.26 0.796 1.23R2:

Within 0.43Between 0.51Overall 0.44

x2 164.8 * * *

r 0.35n ¼ 270Panel B. Random-effect GLS regression result with LNVOLCCID as dependent variableConstant 24.803 1.776 22.70 0.007 * * *

LNOCSRD 0.487 0.167 2.91 0.004 * * * 1.40ACS 20.187 0.111 21.70 0.090 * 1.32BC 4.826 1.168 4.13 0.000 * * * 1.41CSRRa 0.216 0.208 1.04 0.297 1.50LnTurnover 0.263 0.163 1.62 0.106 1.63ROA 0.005 0.006 0.81 0.419 1.34Leverage 20.008 0.003 22.42 0.016 * * 1.38Industryb 0.964 0.459 2.10 0.036 * * 1.21R 2:

Within 0.31Between 0.51Overall 0.41

x2 98.16 * * *

r 0.36n ¼ 270

Notes: Significant at: *10 per cent ¼ p # 0.10; * *5 per cent ¼ p # 0.05;a * * *1 per cent ¼ p #0.01;Dichotomous variable for the existence of CSRR where 1 ¼ presence of this variables and 0otherwise; bDichotomous variable for industry sectors where 1 ¼ Industry with High public profileand 0 for those with low public profileTable VI.

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The contention of this paper is that if community involvement disclosures (CCID) arenot influenced by the clamour for CSR, the pattern of CCID should be independent ofOCSRD over time. As this was not the case, our hypothesis 2 of no relationship isrejected and the alternative hypothesis that a relationship indeed exists is accepted.

TQS, VOLCCID and CG. Hypotheses 3 and 4 tested whether there was anyrelationship between the quality and volume of community disclosures and CG. BothPanels A and B showed a marginally significant relationship at the 10 per centsignificance level between ACS and the two dependent variables TQS and VOLCCID;with a negative coefficient for ACS in Panel B. The implication of this is that there is aslight chance that companies with an average of four Audit committee members andabove will show more quality in their report of their community activities than companieswith a lower number of Audit committee members. This result is consistent with that ofForker (1992) who also found a moderate relationship between ACS and quality ofdisclosure. Ho and Wong (2001) and Felo et al. (2003) found similar relationships betweenACS and disclosures. Moreover, the ACS/quality disclosure relationship supports therequirements of the Blue Ribbon Committee (1999) and Smith Report (2003).

On the other hand, while BC has no statistically significant relationship with TQS inPanel A, a positively strong relationship at 1 per cent significant level was foundbetween BC and VOLCCID in Panel B. The implication of this is that while thecomposition of the board of directors has no effect on the quality of disclosures, theresults in Panels B revealed that companies with board constituting non-executivedirectors of 60 per cent and above will give more details of the company’s communityactivity than those with fewer non-executive directors. Although this result is consistentwith the findings of Webb (2004) and Ajinkya et al. (2005), earlier studies (Forker, 1992;Ho and Wong, 2001), reported no significant association between BC and disclosures.Forker explained that this may be due to measurement error, as not all the companies intheir sample disclosed non-executive directors. However, since the publication of theCombined Code by the Committee on Corporate Governance in June 1998, corporationsare obliged to disclose the non-executive directors to show compliance with therequirements of the code that non-executive directors should not be less than one-third ofthe board. In the same way, our results in Panel A also revealed that companiesproducing CSRR would give more quality disclosure in annual reports than those whodo not, thus supporting the assertions of Toms (2002) and Gebauer and Hoffmann (2009).

TQS, VOLCCID and public pressures. Since size, profitability, leverage and industryhave been documented as measures of public pressure, to which CSR disclosures haveresponded (Hackston and Milne, 1996; Hasseldine et al., 2005; Campbell et al., 2006;Garcia-Castro et al., 2010), our hypotheses 5 and 6 tested both TQS and VOLCCID forany relationship with these variables. Our results suggested a moderately statisticallysignificant relationship between volume and leverage with a negative coefficient but atthe 5 per cent significance level. The implication of this is that debt holders are likely todemand less community information than shareholders. Although the negativeleverage/disclosure relationship is consistent with previous studies such as Belkaouiand Kahl (1977) and Rahman (2002), who also found a negative relationship, otherstudies such as Tsamenyi et al. (2007) and Galbreath et al. (2008) found a positiverelationship, while others found no significant relationship (Mangena and Pike, 2005;Garcia-Castro et al., 2010). Although Watson et al. (2002) argued that signalling theory


21

is inconclusive as to the direction of leverage/disclosure relationship, a recent study byGalbreath et al. (2008) found there to be some relationship.

Furthermore, VOLCCID was also found to have a strong positively significantrelationship with Industrial profile with a coefficient of 0.964 and at the 5 per centsignificance level. As Industry dummy is a dichotomous variable with 1 representinghigh public profile industries and 0 low public profile industries, our results suggestedthat high public profile industries disclosed more than lower profile industry, althoughno significant relationship was found with TQS and industry. This finding is consistentwith that of Adams et al. (1998), Hackston and Milne (1996), and Campbell et al. (2006).The implication of this finding is that although companies with high public profiledisclose more community information, such disclosures are characterised by meregeneral statements of company policy rather than specific achievement.

Again, while a positive relationship was found between both dependant variables (TQSand VOLCCID) and turnover which we have used as proxy for company size, a negativerelationship was found between TQS and ROA (proxy for profitability). However,while the turnover/disclosure relationship is not statistically significant, a 5 per centsignificance level was reported between TQS and profitability. Although a number ofstudies have documented no statistical relationship (Ho and Wong, 2001; Hasseldine et al.,2005; Hackston and Milne, 1996), some studies found an association between profitabilityand social disclosures (Haniffa and Cooke, 2002; Galbreath et al., 2008). Our results are alsoconsistent with that of Freedman and Jaggi (1986) who found that disclosure wasnegatively correlated with net income, with disclosure increasing despite poor economicperformance. This suggests that the decision to disclose was not influenced by economicperformance but rather the pressure to meet up with societal expectation.

5. Relevance and conclusionThis study has contributed to the community disclosure literature, as the paper hasexamined the content of community disclosure which may be displaying a measure ofactive CCD, or may be a signal of CSR. The study is unique in that as opposed tocross-sectional or time-series only data, we used a combination of both in a panel datato examine a less researched area of social disclosure – Community Involvement. Alsocontrary to Campbell et al. (2006), this study employed the use of advanced parametrictechniques to ensure a robust analysis. The analysis provided for insights into thedisclosure of CCI as adopted by companies themselves and allows us to understandwhat activity companies may undertake in the category of “community involvement”and how seriously they regard this activity.

In general, we found that all disclosures increase, thereby signalling a response tosocietal requests for disclosure, but that active involvement – as measured by qualityof disclosure (i.e. specific and detailed accounts of involvement) – was not signalledwithin these responses. We had hoped to find that there may be additional clamour forcommunity involvement over and above the call for general CSR disclosure but thiswas not the case. Hence, community involvement is not regarded as a significant orspecial case of CSR activities, nor does it appear to be given prominence by companies.This is despite the result that companies producing separate CSRR (indicating agreater commitment to CSR, or at least to the reporting of it as a response to signallingrequests) tend to show better quality disclosure in the annual report. Hence, we can say

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that generally companies are not especially active in community involvement if theyare not active in CSR generally.

We argue that signalling is a response to societal pressure because we found thatpressure to disclose does not come from economic stakeholders (in this casedebt-holders). Profitable companies tend to produce better quality disclosure (echoingthe findings of Abd-Elsalam and Weetman, 2003) as it is likely that profit and other“good news” go together. In this way, profitable companies disclose more information tojustify the level of profits made and so use mechanisms to signal this. The managementof more successful, profitable organisations will wish to distinguish itself from lesssuccessful organisations, and so use disclosure to do this (van der Laan Smith et al.,2005). Signalling legitimacy can also be demonstrated by the fact that high profilecompanies produce more but not better disclosure (Watson et al., 2002). However, thecommunity involvement signal is not interpreted as important by the economicstakeholders, and so this tends towards supporting the proposition for societal pressureas being important in this case.

Hence, we conclude that the disclosure of community involvement is explained bysignalling theory, in that it adheres to societal expectations of such activity but does notreflect actual community development measures. First, the fact that most of the disclosuresconsist of general statements rather than specifics showed that most of the disclosurescontains only few inimitable projects and therefore do not qualify as a quality signal ofdisclosure. Second, the fact that disclosure and quality responded to public pressuresindicated there is no real motivation for community involvement; rather companies appearto disclose general statements of community involvement as a means of demonstrating amodel of corporate citizenship, which is motivated by a desire to demonstrate somecommitment to corporate responsibility. Finally, responses to CG measures confirms Toms(2002) assertion that the governance perspectives of monitoring and controlling contributeto the creation of environmental reputation, thus further corroborating signals of adherenceto societal expectations – so as to be seen as socially responsible.

These results have a number of implications both for management and for public policymakers. Currently it appears that general disclosures of community involvement aresufficient to appease societal expectations; however, if the clamour for such informationincreases, so will the demand for quality disclosure. Demand for CSR disclosure is notstatic; society looks for information that fits with the times and the context in whichcurrent occurrences take place. Hess et al. (2002) refer to the “new wave of corporatecommunity initiatives” (p. 110) as a response to the 9/11 disaster in 2001, where concernsmoved towards what contributions business organisations were making to their localcommunities. If this were to intensify, managers would be subject to greater pressure todisclose active participation in community development and would, therefore, beresponsible for developing reporting and disclosure mechanisms that would capture thespecific and detailed activities which their organisations were engaged in.

In terms of governance, the link between governance structures, reputation anddisclosure could be exploited to develop those structures which encouraged betterquality disclosure, so that meaningful information about a company’s communityinvolvement could be required to be provided. This may in turn develop society’sinterest in these higher quality disclosures so that aspects of accountability and thesocial contract can be established. A move towards mandatory disclosure, asadvocated by Adams (2004) and Cooper and Owen (2007) may enhance governance


23

mechanisms and thus, in turn, enhance the company’s motivation to be activeparticipants in their communities.

However, the research is not without its limitations. The researchers recognised thefact that other internal factors such as ownership structure, ethical orientation andqualification of management staff could also have influence on the quality of CCID, butthese factors were not examined due to non-availability of data. Additionally it will beinteresting to explore the effect of stakeholder engagement and media coverage oncommunity activities during the period. The research took a quantitative approach todisclosure measurement. A qualitative approach, using semiological analysis, may revealthe meanings behind the disclosure and may expose the reasons why quantity but notquality of disclosure is the norm. Further research is therefore required on these issues.

Notes

1. The term semiotics is more widespread in English-speaking countries, while semiology ispreferred by the French linguists.

2. The functional use of language developed from the concept of functional fixation. Thisconcept according to Jain (1973) states that once a person relates a meaning to a particularphenomenon or events through past experience, this meaning becomes fixed in their headand is related to subsequent phenomena or event irrespective of alternative meanings orcauses of that event.

3. The ICB is jointly owned by the FTSE International Ltd and the Dow Jones & Co (DJ). TheICB structure and code index is used in both the FTSE and DJ indices for the classification ofcompanies into sectors and industries. For the purpose of this study, the structure and codewas accessed on 7 October 2009 and available at: www.ftse.com/Indices/Industry_Classification_Benchmark/index.jsp

4. In Germany the Institute of Ecological Economy Research (IOW) and the business networkfutures “future” in a joint project set out 48 social and environmental reporting criteria withwhich they carried out evaluation and ranking of CSD reports of German companies.According to Gebauer and Hoffmann, the criteria form a set of reporting frameworks andthus enhance reports of actual performance (Gebauer and Hoffmann, 2009).

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29

Appendix 1

No.

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Table AI.Decision rules forcontent analysis

SAMPJ3,1

30

Appendix 2Basic assumptions for Least Square Regression:

(1) The error term (e i) has an expected value of zero.

(2) The independent variables are non-random and uncorrelated with the errors e i .

(3) The independent variables are linearly independent. That is, one independent variableshould not be too strongly correlated with another. A violation of this assumption maylead to multi-collinearity.

(4) The variance of the disturbances e i is constant for each observation (the homoscedasticassumption).

(5) The data are a random sample of the population, which means the disturbances(error term) associated with each observation are uncorrelated with each other (that is, noserial correlation or autocorrelation).

(6) The population model is linear.

(7) The errors are normally distributed.

Appendix 3

Figure A1.Histogram of residuals

Skewness = 0.168Kurtosis = 2.824

0.25

0.2

0.15

0.1

0.05

0

Den

sity

0 2 4 6 8 10

Residuals

Normality Check of Residuals

Figure A2.Normality tests

of residualsr 270 0.99162 1.627 1.137 0.12786

Variable Obs W V z Prob > z

Shapiro-Wilk W test for normal data

swilkr


31

Appendix 4

Appendix 5

About the authorsKemi Yekini is a Chartered Accountant with a first degree in Accounting. She also has a Mastersdegree in Finance from the University of Leicester. Currently, she is a doctoral student at theLeicester Business School, De Montfort University, UK. Kemi’s many years of experience inpractice and industry has largely driven her teaching and research interest(s), which includefinancial and managerial accounting/reporting, social reporting and accountability, corporategovernance and auditing. Kemi Yekini is the corresponding author and can be contacted at:[email protected]

Dr Kumba Jallow’s research and teaching activities have developed out of a combined interestin the natural environment and her experience in management. Kumba has developed a broadapproach to examining Corporate Social Responsibility and sustainability, examining companydisclosure, both words and images, to assess the accountability to these issues of the largemulti-nationals. She interprets CSR to encompass many aspects of social and environmentalinteractions and has a particular interest in development and developing world issues. Kumba isthe Founding Editor of The Journal of Applied Accounting Research, published by Emerald. Sheis a member of CSEAR, Invisio and CROG, and sits on the DMU Advisory Committee on theGrand Challenge of Living with Environmental Change; she is also a member of the University’sSustainable Development Task Force.

Figure A3.Breusch-Pagan LM testfor random-effect

Prob > chi2 = 0.0000chi2 (1) = 85.66

Test: Var(u) = 0

u 2.009734 1.417651e 3.742195 1.934475

tqs 10.26748 3.204291

Var Estimated results:

tqs [id, t] = Xb + u[id] + e[id, t]

Breusch and Pagan Lagrangian multiplier test for random effects

. xttest0

sd = sqrt(Var)

SAMPJ3,1

32

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