Journal of Marketing Management 2005, 21, 89-115

ISSN1472-1376/2005/1-2/00089 + 26 £8.00/0 ©Westburn Publishers Ltd.

Morven G. McEachern1 and Gary Warnaby2

Improving Customer Orientation Within the Fresh Meat Supply Chain: A Focus on Assurance Schemes

University of Salford

Eighty percent of primary food producers are currently involved in assurance schemes (McDougal 2000), the largest group of which belong to assurance labels sponsored by producer-led groups (e.g. Quality Meat Scotland, English Beef & Lamb Executive). Originally designed to enable producers to provide assurances of meat safety and animal welfare to consumers, this paper evaluates the extent to which producer-led assurance groups have adopted a true market orientation. Both in-depth, semi-structured interviews and a postal questionnaire with Scottish meat consumers were carried out. Subsequently, using structural equation modelling techniques, causal influences upon producer-led assurance label purchase behaviour were determined. The results conclude that producer-led logos are the preferred assurance labels to be purchased by consumers and that the most significant influences upon purchase behaviour are attitudes, past behaviour, assurance label knowledge and personal identity traits. Moreover, weaknesses are identified in terms of producer-led groups’ marketing communication strategies to consumers. Implications of those weaknesses in relation to improving market orientation are then discussed.

Keywords: quality assurance schemes, consumer purchase behaviour, fresh meat, Scotland

1 Correspondence: Morven G. McEachern, School of Management, University of Salford, Salford, Greater Manchester, M5 4WT, UK (tel: 0161 295 5594; fax: 0161 295 5556; e-mail: m.g.mceachern@salford.ac.uk). 2 Gary Warnaby, School of Management, University of Salford, Salford, Greater Manchester, M5 4WT, UK (tel: 0161 295 3654; fax: 0161 295 5556; e-mail: g.warnaby@salford.ac.uk).

90 Morven G. McEachern and Gary Warnaby Introduction In comparison to other market sectors, the agri-food chain has only recently been forced to become more market oriented (Morris and Young 2000; McInerney 2002). Consequently, few studies to date have evaluated the extent to which the fresh meat supply chain has responded to consumer demands for assured meat products (Fearne et al. 2001; Hawkins 2002; Nilsson et al. 2004). A benchmarking comparison of UK assurance schemes (see McEachern and Tregear 2000; Schrőder and McEachern 2002) identified that the majority of schemes offer little more than is already required by legislation (e.g. Welfare of Farmed Animals (England) Regulations 2000; Food Safety Act 1990). Moreover, few assurance schemes possess criteria regarding the quality of the finished product and continue to operate under inconsistent standards of transparency.

McEachern and Warnaby (2004) also conclude that consumers possess little understanding of what assurance labels actually signify. Consequently, questions are raised regarding the extent to which producer groups have adopted a market oriented approach, particularly with regard to communicating the added-value of their product offerings, in terms that are understood by their target markets. Using structural equation modelling (SEM) procedures, this research aims to identify the key variables responsible for predicting purchase decisions of fresh meat bearing producer-led assurance labels. The paper concludes by considering the implications of this for the development of a more meaningful market orientation by businesses in this sector. Very few papers adopting SEM techniques have been published within the marketing literature (Mackenzie 2001), thus potentially allowing this paper to provide a unique contribution towards improving the prediction of consumer purchase behaviour of assured fresh meat. Market Orientation The importance of developing a market orientation is well attested in the literature. Slater and Narver (1995, p. 63) emphasise the utility of a market-oriented approach as it focuses the organisation ‘on (1) continuously collecting information about target-customers’ needs and competitors’ capabilities and (2) using this information to create continuously superior customer value’. Whilst the terms market and marketing orientation are often used interchangeably (Shapiro 1988), Lambin (2000) distinguishes between them. He states that marketing orientation focuses on marketing’s functional role in co-ordinating and managing the 4Ps to make a firm more responsive to meeting the needs of its customers, whereas a market orientation highlights the role of all members of the organisation in developing customer relations

Improving Customer Orientation 91 and enhancing customer value. This has resonance with Day’s (1994) synthesis of earlier definitions of market orientation (e.g. Shapiro 1988; Kholi and Jaworski 1990; Narver and Slater 1990; Deshpande et al. 1993;), identifying three principal features of the concept as: a set of beliefs that puts the customer’s interests first; the ability of the organisation to generate, disseminate, and use superior information about customers and competitors; and the co-ordinated application of interfunctional resources to the creation of superior customer value.

Lings (1999) argues that market orientation can be considered in two ways. First, as a set of management behaviours by which the principal features outlined above are achieved. Second, it can be regarded as a business philosophy ‘directing behaviour and action which translates the philosophy into business strategies’ (1999, p. 248). Linking the philosophical and behavioural viewpoints, Slater and Narver (1995, p. 67) define market orientation as the organisational culture ‘that (1) places the highest priority on the profitable creation and maintenance of superior customer value while considering the interests of other key stakeholders; and (2) provides norms for behaviour regarding the organisational development of and responsiveness to market information’. They go on to argue that, ‘Because of its external emphasis on developing information about customers and competitors, the market-driven business is well positioned to anticipate the developing needs of customers and respond to them through the addition of innovative products and services’ (1995, p. 67).

However, it has been argued that market orientation is overtly externally oriented (Lings 1999). Indeed, various authors (e.g. Hollensen 2003; De Wit and Meyer 2004) contrast market-oriented perspectives with more internally oriented ‘resource-based’ views of marketing and strategy, which focus on the ‘proactive quest for markets that allow exploitation of the firm’s resources’ (Hollensen 2003, p. 30). Here, a key strategic issue for agri-businesses is the acquisition and development of difficult-to-imitate competences and exclusive strategic assets (De Wit and Meyer 2004) as a fundamental source of competitive advantage.

If the resource-based view advocates that strategy should be based around a company’s strengths, the market-oriented perspective argues that firms ‘should not be self-centred, but should continuously take their environment as the starting point when determining their strategy’ (De Wit and Meyer 2004, p. 250). Thus, the agri-business’s resources are adapted to meet market conditions and the competitive environment. De Wit and Meyer articulate the notion of the paradox of markets and resources that firms face when developing strategy; namely the need to balance the demand for market adaptation with the demand for resource leveraging. However, regarding these perspectives as a dichotomy would be an oversimplification –

92 Morven G. McEachern and Gary Warnaby Hollensen (2003) advocates a ‘value chain based’ view of strategy which incorporates elements of both as a means of ‘bridging the gap’ between them. The nature of the possible competitive advantage that could accrue from a more customer-oriented approach by firms in the agri-food sector in relation to assurance schemes is considered below. Assurance Schemes – Oriented Towards the Consumer? Notwithstanding the above, this paper considers in more detail the issue of customer knowledge with particular reference to the agri-food chain. As noted above, a key feature of a market orientation is the acquisition of knowledge about customers. For food products in particular, the label is often the only way in which the marketer may attract attention to a product and subsequently influence the consumer to purchase the product (Dibb et al. 1997; Kotler et al. 1999). Labelling information and knowledge about food is becoming increasingly important to the British consumer (Ilbery and Kneafsey 2000). However, if customers’ knowledge of many pertinent issues relating to market conditions is incomplete and partial, what are the implications for the development of the competitive advantage that is a key aim of a market orientation?

A key issue for consumers of fresh meat in the UK is food safety and quality assurance, particularly since the Bovine Spongiform Encephalopathy (BSE) crisis of the early 1990s, E-coli incidents in 1998 and the recent outbreak of Foot and Mouth Disease in 2001. Consequently, the principal motivation for livestock producers to join assurance schemes was to regain consumer trust in fresh meat (Bennett and Blaney 2003; Lindgreen 2003). This was attempted via assurance logos, informing consumers of the product’s country-of-origin, animal welfare characteristics adhered to during production and/or overall product quality. Recent research has indicated the perceived importance of Quality Assured (QA) or Farm Assured (FA) labels among customers as an indication of the safety of the food they are eating. However, while QA/FA labels for fresh meat are important to customers and many wish to know more about them, a widespread ignorance of the underpinning criteria governing such labels exists (MAFF3 2000; McEachern and Warnaby 2004). Moreover, Van Trijp and Steenkamp (1998) and Nilsson et al., (2004) maintain that labelling formats alone are not sufficient to communicate a product’s unique selling points, and that quality labels should be supported by communication strategies.

To enable consumers to make informed decisions about what they eat, 3 Reference to both the Ministry of Agriculture, Fisheries & Food (MAFF) and the Department for the Environment, Food & Rural Affairs (DEFRA) varies depending on the year of publication.

Improving Customer Orientation 93 they must have access to clear and trustworthy information from producers, manufacturers, retailers and government bodies (Lazenby 2000). Despite labelling being one of the easiest routes for potential consumer learning in relation to food products (Grunert 1997; Acebron and Dopico 2000), MAFF (2000) revealed that 38% of consumers viewed labelling information on labels as being misleading and 31% had difficulty in understanding both the label and the technical terms used. Many government-based studies (i.e. MAFF 2000; SEERAD 2002; FSA 2004) revealed that the information most commonly looked for by consumers when shopping included: the freshness/sell by date; ingredients/nutritional information (e.g. fat content); and product quality/value for money. They also conclude that consumer concerns relating to country-of origin and animal production methods are of secondary concern. Moreover, Ilbery and Kneafsey (2000) question the value of assurance schemes and their emphasis on animal welfare as many consumers maintain more focus on price rather than systems of production for meat animals. Country-of-origin labelling per se has encountered significant criticism over the last few years, particularly regarding assurance schemes’ use of misleading labelling tactics (Bedington 2002; Consumer Association 2003). Currently, EU labelling legislation permits many imported food products to be sold as British, English or Scottish as long as the processing or packaging process takes place in the UK.

Baines and Harris (2000, p. 463) criticise the communication efforts of the agri-food supply chain, describing current communication activities as mere ‘kiteflying’ as opposed to establishing “a quality real brand”. Browning (2001, p. 24) appeals to meat producers to move towards greater branding efforts and adds “in the long term, if you have no branding, you have no strength in the marketplace”. Conversely, Pearce (1999, p. 34) argues that supermarkets employ labelling formats such as ‘premium’ and ‘traditional’, in order to “dissuade reflection”. Consequently, as 75% of all buying decisions are made in-store (Vaughan 2003), point of purchase communications are critical to aiding consumer purchase decisions. Despite criticisms regarding assurance schemes’ communication activities and the fact that many labelling studies (i.e. MAFF 2000; SEERAD 2002; Consumer Association 2003; FSA 2004) conclude that consumers possess little understanding of assurance schemes, producer-led scheme co-ordinators have neither adjusted their labelling formats nor their marketing communication tactics. Underwood and Ozanne (1998) propose a set of rules surrounding the design of packaging/labelling (i.e. truthfulness, sincerity, comprehensiveness, legitimacy), claiming that if adopted, marketers should benefit from reduced consumer cynicism and greater potential to enhance buyer-seller relationships. Nancarrow et al., (1998) warn marketers of the danger in simply measuring consumer awareness of communication

94 Morven G. McEachern and Gary Warnaby campaigns and state that in order to methodically assess communication effectiveness, management must aim to understand consumer behaviour theory and decision-making models.

Due to the significance of attitude theory and its influence upon buying behaviour, subsequent multi-attribute attitude models have been developed to improve predictions of consumer behaviour. Compared to other models, the Theory of Reasoned Action (TRA), developed and tested by Fishbein and Ajzen (1975) and the Theory of Planned Behaviour (TPB) led by Ajzen (1988; 1991) have encountered widespread application and testing, thus endorsing their predictive capability (Armitage and Conner 1999). Although support for the use of the TRA for food research exists (Tuorila 1987; Saap and Harrod 1989; Zey-Ferrell and McIntosh 1992), criticisms led Ajzen (1985; 1991) to extend the model (i.e. TPB) to deal with behaviour where the consumer has little volitional control. Similar to the TRA, where behavioural and normative beliefs are assumed to influence attitudes towards the behaviour and constitute the underlying determinants of subjective norms respectively, control beliefs provide the foundation for perceptions of perceived behavioural control. Despite the general acceptance of both models, both models are criticised for not including other variables that may influence an individual’s behaviour (Eagly and Chaiken 1993; Furnham and Lovett 2001). The TRA/TPB regard knowledge as a precursor to an individual’s attitude, but it is still unclear as to the role of information and its impact on attitudes and behaviour (Eagly and Chaiken 1993). Moreover, Breakwell (2000) recommends that personality, past experience and attitudes be taken into consideration when designing food labels or marketing communications. Consequently, given the proviso from Ajzen (1991, p. 199), that the TPB model is “open to the inclusion of additional predictors”, this paper adopts the TPB with a view to testing the inclusion of additional independent variables such as assurance label knowledge, past behaviour and personality. The remainder of this paper considers these issues, and evaluates their implications for the development of a more meaningful market orientation by assurance scheme co-ordinators (see Appendix 1 for the producer-led assurance schemes included in this study). Methodology A postal survey was conducted in August 2001 among 1000 Scottish female consumers of fresh meat. The questionnaire was structured according to the design principles advised by Ajzen and Fishbein (1980), when using the TRA/TPB. Response categories were informed by an earlier, qualitative study into consumer perceptions of QA/FA labels used by food retailers in Scotland (Schröder and McEachern 2002). Using a stratified random

Improving Customer Orientation 95 sampling technique, respondents were selected from the General Register of Electors (2001). This involved the selection of every nth household from the Electoral Register. The sample was only directed at female purchasers of fresh meats since over 90% of females throughout the UK population take responsibility for household shopping and 80% make the final purchase decision (Fuller 1999; Leatherhead Food Research 2002). Four locations around Scotland were targeted: Argyll & Bute on the West of Scotland; the Borders in the South; the City of Edinburgh on the East; and the City of Glasgow, West of the Central Belt. Consumer purchase behaviour of fresh meat in Scotland is deemed as being representative in terms of both consumption levels and expenditure trends found across the rest of the UK (Keynote 2003). The total response rate was 42%. However, 7% were ineligible for inclusion, resulting in a final sample size of 353 (i.e. 35%). It is widely accepted that in order to provide a “good” structural equation model (SEM), three hundred cases are sufficient (Comrey and Lee 1992; Ullman 2001).

Data analysis was carried out using SPSS (Statistical Package for Social Sciences, Version 11.5). Fifty attitude statements relating to respondents’ beliefs surrounding meat production and the underpinning standards of assurance labels were informed by the qualitative data and measured using Likert scales. A confirmatory factor analysis (CFA) was then derived from these attitudinal questions and calculated using a Varimax rotation. In addition, SEM techniques were also adopted to examine a series of dependence relationships simultaneously and determine the goodness-of-fit between the hypothesised model and the data (Bentler 1989). As models are useful in facilitating marketing decisions, a multi-attribute attitude model of consumer purchase behaviour of producer-led QA/FA labels is constructed using EQS modelling software (Bentler and Weeks 1980). This model can not only facilitate the identification of causal influences upon consumers’ purchase behaviour of fresh meat bearing producer-led assurance labels, but also provide marketing practitioners responsible for assurance schemes with recommendations regarding the future market orientation of the fresh meat value chain. Appendix 2 illustrates the measurement tools used to identify the constructs that appear in the following SEM model and their reliability calculations.

Limitations are acknowledged regarding the research design. In particular, quantitative measures of habit, producer-led label purchase behaviour and control issues are all based on self-reported measures. However, Azjen (1991, p. 102) defends the view that biased self-reports are likely only in the case of “sensitive behaviours” (e.g. socially undesirable behaviour). In the event of behaviour not being sensitive, self-report methods are observed as being “quite accurate”. In support of this stance, Kirk-Smith

96 Morven G. McEachern and Gary Warnaby (1998) states that self-report validity is dependent upon the questions, the situation and the type of behaviour. It could therefore, be counter-argued that the topic of assurance schemes and their selection is not deemed to be of a sensitive nature. Thus, the responses reported throughout this study are likely to provide an acceptable “estimate of behavioural tendencies” (Ajzen 1991, p. 110). Results Respondent Demographics

Demographic profiles (i.e. age, socio-economic group, marital status) were generally statistically similar to that of the UK population (ONS 2001), apart from a slightly higher representation of married/cohabiting respondents’ (i.e. plus 30%) and those aged forty-five to sixty-four (i.e. plus 10%). Assurance Label Purchase Behaviour

Intended purchase preferences were clearly expressed for producer-led QA/FA labels, with 58% of respondents preferring such labels. Twenty-nine percent had no preference for any QA/FA label when purchasing meat and 13% expressed preferences for other QA/FA label types (i.e. 8% chose organic/added welfare labels and 5% chose retail QA/FA labels). Regarding specific label choice (see Table 1), 71% of respondents selected Quality Meat Scotland’s ‘Scotch Beef’ label as their first choice. The ‘Scotch Beef’ label however, slipped to second choice (i.e. 22%) when respondents intended to purchase a welfare-friendly meat product. Here, first choice was the Little Red Tractor label (i.e. 37%). No significant differences were identified between label choice and demographics. In relation to respondents’ past purchase experience of QA/FA labels, only 5% and 43% of consumers strongly agreed and agreed respectively, that they repeatedly purchased the same QA/FA label. Table 1. Respondent QA/FA Label Preference

PRODUCER-LED QA/FA LABEL

RESPONDENT ORDINARY

PREFERENCE %

RESPONDENT WELFARE

PREFERENCE % British Meat 12 2 Scotch Beef 71 22 Scotch Lamb 3 20 Scottish Quality Pork 3 12 Assured British Pigs 2 7 Little Red Tractor 9 37

Improving Customer Orientation 97 Attitudes

From the confirmatory factor analysis (CFA), the following four attitude factors were extracted and assigned the following labels, on the basis of the loadings for each factor:

1. Meat safety (within the context of country-of-origin); 2. Animal welfare; 3. Quality assurances; 4. Media topics4.

These four Factors have revealed the central attitudes towards QA/FA meat purchase behaviour (see McEachern and Warnaby 2004, for a more in-depth review), and will be causally tested in the next phase of analysis and used to predict purchase behaviour of producer-led labels. Perceived Behavioural Control

There are many control variables that can impact upon meat purchase behaviour, both in relation to an individual’s attitudes (i.e. thus affecting purchase intention), and their actual behaviour. In relation to respondents’ use of QA/FA labels, 64% always read the label and 33% sometimes checked the label. Only 3% never checked the label before purchase. Most respondents (i.e. 71%) experienced difficulties when reading and understanding QA/FA labels. Consequently, only 22% of respondents strongly agreed that obtaining information about QA/FA labels, was under their control. Knowledge

As the Theory of Planned Behaviour (TPB) assumes that individuals will acknowledge all available information (Ajzen 1991), respondents’ knowledge was gauged regarding their recognition, claimed knowledge and actual knowledge (Table 2), of each producer-led scheme.

Recognition of producer-led labels was high apart from the little red tractor label, which was only recognised by 31% of respondents. However, actual knowledge of producer-led QA/FA labels was lower than claimed. Respondents generally interpreted all labels as stipulating far higher standards than they actually do. The main discrepancy between claimed and actual knowledge of producer-led QA/FA labels appeared to be due to the misinterpretation of country-of-origin labelling and lack of access to assurance standards. In contrast to all other producer-led labels, respondents’ actual knowledge of the little red tractor label was higher than claimed. In 4 This factor was named as such due to the inclusion of attitude statements that related to popular headlines that appeared regularly in the British media at the time of research e.g. producers use of antibiotics, genetically modified foods etc.

98 Morven G. McEachern and Gary Warnaby spite of the lowest level of recognition, more accurate responses were recorded due to respondents’ reduced associations between the label and country-of-origin. No significant differences were identified between label knowledge and demographics. Table 2 QA/FA Labels – Recognition, Claimed and Actual Knowledge

PRODUCER-LED QA/FA LABEL

RESPONDENT RECOGNITION

%

CLAIMED KNOWLEDGE

%

ACTUAL KNOWLEDGE

% British Meat 81 22 10 Scotch Beef 88 26 22 Scotch Lamb 76 23 21 Scottish Quality Pork 54 18 16 Assured British Pigs 45 13 8 Little Red Tractor 31 12 34 Modelling Additional Variables

Before investigating the impact of any additional variables (i.e. habit, knowledge, personal identity), the paper now reports the results of this dataset being tested against the variables found within the Theory of Planned Behaviour (TPB). To facilitate the theoretical extension of the TPB, both Pearson correlations and stepwise multiple-regression techniques helped to identify all significant factors to be included in the proposed models. Subsequently, the use of EQS software enabled the testing of causal pathways in each model. Modelling abbreviations for the independent variables used throughout the results section of this paper include: Behavioural intention (BI); Perceived behavioural control (PBC); Meat safety (ATMS); Farm animal welfare (ATAW); Quality assurances (ATQA); Media topics (ATMT); Past behaviour (HABIT); Knowledge (PRODK); Openness (OPEN); and Behaviour (PRODB).

Tables 3 and 4 show the correlation matrix and stepwise regression between the TPB variables (i.e. attitudes, perceived behavioural control, behavioural intention), and producer-led label buying behaviour.

These results indicate a significant positive relationship between: BI, ATMS and producer-led label buying behaviour (r =.28; p < 0.01); BI and PBC (r = .17; p < 0.01); and PBC, ATMS and ATMT (r = .32; p < 0.01). Table 3 also indicates a significant negative correlation between: ATQA and producer-led label buying behaviour (r = .28; p < 0.01); BI and ATQA (r = .15; p < 0.01); and PBC and ATQA (r = .14; p < 0.01).

Improving Customer Orientation 99 Table 3. Correlation Matrix Between TPB Variables and Producer-Led QA/FA Label Buying Behaviour

TPB VARIABLES

PROD-LED B BI PBC

PROD-LED B 1 BI .137** 1 PBC .136* .168** 1 ATMS .277** .017 .315** ATAW -.036 .058 .033 ATQA -.282** -.146** -.141** ATMT -.008 .115* .199** *P=< .05 (two-tailed); **=< .01 (two-tailed) Table 4 . Stepwise Multiple Regression Relating the TPB Variables and Producer-led QA/FA Label Buying Behaviour

MODEL Std. ß

Coefficient Std. Error

Of ß R2 Adjusted

R2 F SignificanceATQA -.282 .007 .08 .08 30.26 .000 ATMS .234 .004 .13 .13 26.68 .000 BI .100 .015 .14 .13 19.28 .000

Using the stepwise method (Table 4), a significant model emerged (r = .38, F 3, 349 = 19.28, p < 0.0005). The final model includes ATQA, ATMS and BI as the main influencing predictors, explaining 14% of the total variance in producer-led label buying behaviour. The adjusted R2 (i.e. similar to cross validation correlation of R2) reduces this figure to 13%. Table 4 also indicates that PBC, ATAW and ATMT failed to meet the p<.0.05 criterion, and therefore, were not entered into the regression analysis. As multiple regression analysis does not test the hypothesised model, the next step is to test the data using EQS (Figure 1).

The AASR and ADASR are small (i.e. ≤ 0.03), indicating a good fit of the model to the data. The model confirms the multiple regression results by indicating that producer-led label buying behaviour is primarily explained by, ATMS (5%), ATQA (5%) and BI (1%). BI is explained by PBC (2%). PBC is explained by ATMS (8%), ATMT (4%) and ATQA (1%). No modifications were made to Figure 1. The results of this dataset confirm the TPB where attitudes and BI appear to be a strong predictor of producer-led QA/FA label buying behaviour. The model X2 value was 3.77, df = 7, p = .80, compared with the null model (independence) X2 = 257.45, df = 21, p = .005. Since the X2

value is non-significant, the data fits the hypothesised model. Goodness of

100 Morven G. McEachern and Gary Warnaby Fit (GOF) indices (i.e. NFI, NNFI, CFI) are robust (i.e. ≥0.9 and above), showing a very good fit of this data to the TPB variables. Overall, this was a robust fitting model, with attitudes (i.e. ATTS), PBC and BI, explaining 54% of the variation in producer-led buying behaviour. Figure 1. Modelling TPB Variables and Producer-Led QA/FA Label Buying Behaviour

0.11* 0.29* 0.23* 0.16* 0.47* 0.11* 0.13* 0.10* 0.23* 0.11* 0.22* N=353 Average Absolute Standardised Residuals (AASR) = 0.00 Average off diagonal Absolute Standardised Residuals (ADASR) = 0.01 Independent Chi square = 257.45 df = 21 Chi-square = 3.77 df = 7 p = 0.80 Normed Fit Index = 0.985 Non-Normed Fit Index = 1.041 Comparative Fit Index = 1.000 The Wald and the Lagrange Multiplier test did not suggest any further modification to this model The null hypothesis is rejected and a robust fitting model is obtained. In order to assess if prediction capability of producer-led QA/FA label buying behaviour increases as a result of additional variables being added, the additional variables of the consumers’ past purchase experience (HABIT), knowledge (PRODK) and openness (OPEN) are now tested. Tables 5 and 6 show the correlation matrix and stepwise regression between the TPB variables, HABIT, PRODK, OPEN and producer-led QA/FA label buying behaviour (PRODB).

ATQA

ATMT

PRODB

ATMS

ATAW

PBC

BI

Improving Customer Orientation 101 Table 5. Correlation Matrix Between Habit, Knowledge, Openness, TPB Variables and Producer-Led QA/FA Label Buying Behaviour

TPB VARIABLES

PROD-LED B BI PBC

PROD-LED B 1 BI .137** 1 PBC .136* .168** 1 HABIT .262** -.076 .036 ATMS .277** .017 .315** ATAW .450** .058 .033 ATQA -.282** -.146** -.141** ATMT -.008 .115* .199** PRODK .204** .187** .351** OPEN .205** -.078 -.164** *P=< .05 (two-tailed); **=< .01 (two-tailed) These results indicate a significant positive relationship between: BI, HABIT, ATMS, ATAW, PRODK, OPEN and producer-led label buying behaviour (r = .45; p < 0.01); BI, PBC and PRODK (r = .19; p < 0.01); and PBC, ATMS, ATMT and PRODK (r = .35; p < 0.01). Table 5 also indicates a significant negative correlation between: ATQA and producer-led label buying behaviour (r = .28; p < 0.01); BI and ATQA (r = .15; p < 0.01); and PBC, ATQA and OPEN (r = .16; p < 0.01). Table 6. Stepwise Multiple Regression Relating Habit, Knowledge, Openness, TPB Variables and Producer-led QA/FA Label Buying Behaviour

MODEL Std. ß

Coefficient Std. Error

Of ß R2 Adjusted

R2 F SignificanceATQA -.282 .007 .08 .08 30.26 .000 HABIT .260 .027 .15 .14 30.11 .000 ATMS .201 .004 .19 .18 26.43 .000 PRODK .161 .011 .21 .20 23.20 .000 OPEN .137 .004 .23 .22 20.54 .000 BI .106 .014 .24 .23 18.09 .000

Using the stepwise method, a significant model emerged (r = .49, F 6, 346 = 18.09, p < 0.0005). The final model includes ATQA, HABIT, ATMS, PRODK, OPEN and BI as the main influencing predictors, explaining 24% of the total variance in producer-led label buying behaviour. The adjusted R2 reduces

102 Morven G. McEachern and Gary Warnaby Figure 2. Modelling Habit, Knowledge, Openness, TPB Variables and Producer-Led QA/FA Label Buying Behaviour 0.14*

0.23*

0.10* 0.17* 0.16* 0.17* 0.28* 0.15* 0.20* 0.21* 0.16* 0.30* 0.10 0.11* 0.42* 0.23* 0.13* 0.13* 0.14* 0.21*

N=353 Average Absolute Standardised Residuals (AASR) = 0.02 Average off diagonal Absolute Standardised Residuals (ADASR) = 0.03 Independent Chi square = 436.63 df = 45 Chi-square = 26.71 df = 20 p = 0.14 Normed Fit Index = 0.939 Non-Normed Fit Index = 0.961 Comparative Fit Index = 0.983 The Wald test suggested the removal of the pathway between PBC and BI The Lagrange Multiplier test did not suggest any further modification to this model The null hypothesis is rejected and a good fitting model is obtained.

PRODK

PBC

BI

PRODB

ATQA

HABIT

ATMS

ATMT

ATAW

OPEN

Improving Customer Orientation 103 this figure to 23%. Table 6 also indicates that PBC, ATAW and ATMT failed to meet the p<.0.05 criterion, and therefore, were not entered into the regression analysis. The next step is to test the data using EQS (Figure 2).

The AASR and ADASR are small (i.e. ≤ 0.03), indicating a good fit of the model to the data. The model confirms the multiple regression results by indicating that producer-led label buying behaviour is primarily explained by ATQA (4%), HABIT (4%), OPEN (2%), and BI (1%). BI is explained by ATQA (2%). PBC is explained by PRODK (9%), ATMS (8%), OPEN (4%), ATQA (2%) and ATMT (1%). The above model was modified once, as the Wald test suggested dropping the pathway between HABIT and BI. The prior model reported overall GOF indices of: NFI 0.946; NNFI 0.972; and CFI 0.988. Despite a non-significant beta weight, the pathway between PBC and BI was retained to improve the overall model fit. The results confirm the TPB, in that BI and attitudes are strong predictors of producer-led QA/FA label buying behaviour. However, PRODK, OPEN and HABIT significantly improve the predictive capability for producer-led QA/FA label buying behaviour (i.e. adding 8%, 8% and 7% respectively). The model X2 value was 26.71, df = 20, p = 0.14, compared with the null model (independence), X2 = 436.63, df = 45, p = .005. Since the X2 value is non-significant, the data fits the hypothesised model. GOF indices (NFI, NNFI, CFI) indicate a good fit (i.e. ≥ 0.9), justifying incorporation of the HABIT, OPEN and PRODK constructs to the TPB variables. Overall, the model explains 77% of the shared variance, with the additional variables collectively explaining 23% of the shared variance compared to the TPB variables on their own (see Figure 1). These results clearly have managerial implications for producer-led assurance schemes, which are discussed below. Discussion and Managerial Implications Producer-led labels were clearly preferred by respondents compared to either QA/FA label types sponsored by independent associations (e.g. organic labels; added welfare labels such as the RSPCA’s ‘Freedom Food’); or retailer assurance labels. However, despite 64% of respondents always reading the label and 33% sometimes checking the label before purchase, 71% of respondents had difficulty reading and understanding QA/FA labels. Indeed, this was confirmed by the fact that respondents generally interpreted QA/FA labels as possessing higher standards than they actually do. This may indicate that the marketing communications of producer-led schemes are misleading consumers over their underpinning standards.

Figure 1 illustrates robust goodness of fit indices, thus strongly supporting this dataset to the Theory of Planned Behaviour variables. However, two major deviations from the Theory of Planned Behaviour were apparent.

104 Morven G. McEachern and Gary Warnaby Firstly, in contrast to expectancy value theory (see Fishbein 1975), the producer-led model showed a direct path from Meat Safety (i.e. ATMS) and Quality Assurances (i.e. ATQA) to producer-led QA/FA label buying behaviour. This perhaps demonstrates that producer-led QA/FA label purchase behaviour may be more driven by consumer beliefs and attitudes towards meat safety and country of origin and quality attributes (i.e. ATMS and ATQA), rather than sell-by-date, nutritional information and price (see conclusions from MAFF 2000; SEERAD 2002; FSA 2004). This presents a significant problem for producer-led QA/FA scheme co-ordinators as few actually focus on the specific quality of the meat. This is due to the fact that, in spite of 80% of UK producers being involved in assurance schemes (McDougal 2000), 50% and 40% of beef and lamb carcasses respectively (MLC 2002; Lloyd 2004) are failing to meet minimum abattoir and/or supermarket quality specifications. A lack of emphasis on product quality is in stark contrast to QA/FA schemes in other parts of the world (e.g. North America, Australasia), where the prime focus of the QA/FA label is to communicate the enhanced meat ‘quality’ to consumers. At present, quality assurance schemes appear to have enabled the UK meat industry to restore consumer confidence in British meat, with red meat sales currently higher than pre-BSE levels (Bedington 2003). However, the fact that few schemes possess any focus on actual meat quality could not only generate further negative press from the British media, but also diminish any goodwill built throughout the meat supply chain, and therefore, potentially reduce meat sales and devalue consumer perceptions of each producer-led QA/FA brand.

The second deviation from Ajzen’s (1991) theory was that Subjective Norms were not included in either model. Within this study, the main normative influence upon QA/FA label purchase behaviour was guests coming to dinner. Therefore, the Subjective Norm construct was deemed inappropriate to predicting causal influences upon everyday QA/FA meat purchases. However, one area of agreement with Ajzen and Fishbein (1980) is that demographics such as age, marital status and social grouping did not prove significant to the model nor had any direct impact on purchase behaviour.

Initially, 54% of the shared variance was explained using only the Theory of Planned Behaviour variables (Figure 1), but the addition of past behaviour, knowledge and openness (i.e. low scorers), raised the percentage of total variance explained to 77% (Figure 2). The proposed producer-led QA/FA label buying behaviour model obtained good fit indices (i.e. ≥ 0.9), illustrating direct purchase influences to be behavioural intention, openness, past behaviour, meat safety and country-of-origin (i.e. ATMS) and quality assurances (i.e. ATQA). Moreover, these results contradict Ajzen’s (1988; 1991) conclusions with regard to the Theory of Planned Behaviour. In

Improving Customer Orientation 105 contrast to Ajzen’s (1991) argument that the effects of past behaviour are mediated by perceived behavioural control, Figure 2 concludes that past behaviour offers a superior prediction of behaviour than behavioural intention, showing a direct influence upon producer-led label purchase behaviour. The direct role of past behaviour and openness (i.e. low scorers) confirms the perception that consumers who possess a lack of curiosity and narrow interests, may be more inclined to purchase producer-led QA/FA meat on a habitual basis more than consumers who purchase other QA/FA label types.

Despite Ajzen’s (1988) claim that individual measures of self-identity are unnecessary (as they are already measured within consumer values and attitudes), the model confirms the openness construct to be a significant predictor upon producer-led label purchase behaviour. Moreover, it offered a superior prediction of behaviour than behavioural intention. However, one disconcerting aspect for QA/FA scheme co-ordinators is that low scorers of openness reveal an un-analytical, reserved and task oriented approach to QA/FA meat purchases. This may be a reaction to the difficulties incurred in obtaining relevant information, as it is clear that respondents’ knowledge of underpinning standards is minimal (see Table 2).

The construct of knowledge also significantly enhanced the prediction of producer-led label buying behaviour. Here, knowledge is an antecedent to perceived behavioural control and mediated by attitudes towards meat safety (i.e. ATMS), and animal welfare (ATAW). The reason for knowledge’s indirect role within the proposed producer-led model is due to the direct role of past behaviour - consumers are more likely to buy a producer-led QA/FA label on a habitual basis as a result of quality, safety and country-of-origin preferences. These results conflict with the majority of government-based labelling studies (see MAFF 2000; SEERAD 2002; FSA 2004) in concluding that country-of-origin information is considered prior to purchase, especially within the context of meat safety.

Attitudes towards the media (ATMT) also act as a control factor. This confirms that consumers of producer-led QA/FA labels do rely on media formats to make purchase decisions rather than point of purchase materials or other direct marketing communications.

The model also shows openness, knowledge and attitudes towards meat safety, country-of-origin and quality (i.e. ATMS and ATQA) as being mediated by perceived behavioural control. However, the non-significant status of perceived behavioural control, along with the direct influence of past behaviour, illustrates that such purchase behaviour may be under more volitional control (see for example Shepherd 1999). The model also implies that rather than consumers’ control over obtaining relevant information (i.e. perceived behavioural control) being mediated by behavioural intention or

106 Morven G. McEachern and Gary Warnaby directly influencing purchase behaviour, perceived behavioural control is explained and predicted by consumers’ lack of curiosity (i.e. low openness scores) and knowledge. Respondent apathy towards obtaining relevant information on producer-led schemes’ underpinning standards may be an indication of the difficulty in accessing such standards.

Attitudes towards farm animal welfare (i.e. ATAW), although present in both models, suggest that welfare influences have no direct impact on producer-led QA/FA label purchase behaviour. The proposed model indicates that consumer attitudes towards animal welfare (i.e. ATAW) are mediated by information gained from media sources (i.e. ATMT). However, low levels of openness mediate animal welfare attitudes, thus confirming previous studies’ conclusions (i.e. MAFF 2000; SEERAD 2002; FSA 2004) that animal welfare is of secondary concern to consumers when purchasing producer-led QA/FA labels. The indirect significance of animal welfare to the proposed model may also indicate that the fresh meat consumer is perhaps disconnected from production processes at the point of purchase. This behaviour raises questions about the necessity for so many QA/FA brands in the UK marketplace that simply meet existing minimum legislative requirements set by DEFRA. Furthermore, their subsequent value to consumers is potentially threatened as additional QA/FA brands offering little additional benefits to product safety, quality or animal welfare, continue to be developed and targeted at the British consumer. Robertson (2003, p. 4) claims that assurance schemes are “here to stay”, but rather than adopting a unilateral process of national rationalisation, a proliferation of QA/FA schemes continue to operate. Many more have been added since the beginning of this study, some of which cover other livestock species (e.g. turkey, venison, wild boar), but the majority mainly cover existing animals (e.g. Linking Education And Farming (LEAF), The National Trust, the Royal Society for the Protection of Birds). Without educating consumers of the benefits surrounding welfare-friendly systems of production (e.g. the link between high animal welfare standards and meat quality) and providing unlimited access to reliable information surrounding the criteria underpinning QA/FA labels, it is expected that many consumers will continue to demonstrate little preference for assured meat purchases.

A preferred operational strategy that would help scheme co-ordinators develop a cohesive market orientation and assist consumers, is for producer-led schemes to co-operate (individually or collectively) with retail QA/FA labels (e.g. via an umbrella branding strategy). This could then be communicated widely through co-operative advertising and point of purchase displays (see Nancarrow et al. 1998; Van Trijp and Steenkamp 1998; Nilsson et al. 2004). In doing this, producer-led schemes may in the short term reduce the scope for differential competitive advantage, but in the

Improving Customer Orientation 107 longer term, a consolidation of the various QA/FA labels into a small number of well-recognised schemes, may benefit such organisations by reducing consumer confusion and establishing a common benchmark that those QA/FA schemes seeking competitive advantage would aim to surpass. The advantage for producer-led scheme co-ordinators would hopefully arise from improvements in consumer education about QA/FA labels and the standards underpinning them as well as enhancing consumer loyalty.

Authors on market-orientation emphasise the importance of acquiring information relating to customer needs and wants, and acting accordingly. However, in this specific context customer knowledge is partial and often confused. Thus, there exists significant scope for producer-led assurance groups to become more proactive and take the initiative (perhaps via collective action along the lines described above), thereby enabling them to become market drivers instead of being market driven. QA/FA schemes that are meaningful and understood by consumers (via effective communications activities) may become an important strategic resource for those schemes that seek to surpass those minimum legislative standards. This would enable the consumer to at least make an informed choice regarding their meat purchase behaviour. Moreover, an emphasis by these schemes on quality may enable more effective competition on an international basis, thereby capitalising on previous country-of-origin effects that existed prior to recent meat safety crises. Future Research Agenda Whilst Scottish consumers’ consumption and expenditure of fresh meat trends mirror that found across the rest of the UK (Keynote 2003; 2003a), the sampling location for this study is obviously somewhat limited. As many of the producer-led QA/FA labels promoted ‘Scotch Beef’, ‘Scotch Lamb’ and ‘Scottish Pork’, a sample originating from Scotland may have inadvertently biased consumer preferences towards producer-led schemes, rather than the selection of other assurance scheme types, which are more prevalent in other parts of the UK. The research could be expanded to consumers in England, Wales and Northern Ireland. This would help to identify the presence and scale of bias in consumer preferences towards Scottish producer-led schemes and thereby help validate the replicability of the proposed model for use in other areas of the UK

However, the contribution of this research is not just the specific focus on Quality and Farm Assurance schemes for fresh meat (see Fearne et al. 2001; Hawkins 2002; Nilsson et al. 2004), but also the development of a purchase behaviour model that substantially outperforms the predictive capability of the Theory of Planned Behaviour. The proposed final model and its inclusion

108 Morven G. McEachern and Gary Warnaby of added constructs (e.g. habit, knowledge, personal identity) offer producer-led QA/FA organisations vital information regarding future brand management strategies and labelling formats (see Nancarrow et al. 1998; Breakwell 2000). Additionally, the proposed model facilitates a more comprehensive understanding of consumer purchase behaviour of QA/FA meat, which could subsequently, inform a more market-oriented approach. Finally, given that government-based studies (see MAFF 2000; SEERAD 2002; FSA 2004) and non-government organisations’ reports (see Consumer Association 2003) have not comprehensively measured consumer purchase behaviour (i.e. incorporate measurement of habit, personal identity etc), this research should stimulate much interest from QA/FA scheme co-ordinators.

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Appendix 1. Verbal Descriptions of the Main Producer-led Assurance Labels British Meat Red, white and blue background, a square label with

‘British Meat’ in white writing. Scotch Beef Blue background, oval label with ‘Specially Selected

Scotch Beef’ in white writing, underneath a white Scotch thistle.

Scotch Lamb Green background, oval label with ‘Specially Selected Scotch Lamb’ in white writing, underneath a white Scotch thistle.

Little Red Tractor White background, square label, red tractor with blue wheels, and ‘British Farm Standard’ in blue writing.

Scottish Quality Pork Blue background, rosette type label, with ‘Farm Assured Scottish Pork’ in white writing and red writing around the rosette saying ‘quality standard’. The ‘o’ in Scottish is depicted by the flower of the Scotch thistle.

Assured British Pigs Similar to SPII label but rosette depicts the British Meat label with ‘Pork’ in white writing beneath it.

Appendix 2 Quantitative Measurement Tools Behavioural Intention

Respondent intentions to use QA/FA labels were assessed using two items: “I intend to read QA/FA labels before purchase”; “I intend to buy QA/FA meat”. Both items were assessed using 5-point unipolar scales ranging from 1 (extremely unlikely) to 5 (extremely likely) with higher scores indicating a stronger intention to use QA/FA labels during purchase. An aggregate score was obtained by summing both items. Inter-item reliability (Cronbach’s α) for the two items was .72. Alpha values above the threshold of 0.7 are acceptable (Hair et al. 1998).

Behaviour Respondents actual behaviour was assessed as a single item, based on what

QA/FA label they currently purchase. The use of single item scales meant that no inter-item reliability could be calculated. Thus, the dependent variable for this study is the intended purchase behaviour of producer-led assurance labels. Attitudes

114 Morven G. McEachern and Gary Warnaby An individual’s attitudes towards performing a specific behaviour are a summed product of an individual’s beliefs and the evaluation of those beliefs (Ajzen and Fishbein 1980). By scaling the attitude statements, each respondent was given a score on each of the factors, with higher scores indicating more positive attitudes. This score represents an individual’s “evaluation of an object implied by a set of beliefs, intentions or actions” (Ajzen and Fishbein 1980, p. 15). Subjective Norm

Similar to previous studies (see Ajzen 1991; Armitage and Conner 1999) that identify the Subjective Norm as the weakest component of the TPB, normative influences upon respondents were weak and only apparent in the event of assured meat being purchased for special occasions or guests coming to dinner. Consequently, the construct of Subjective Norm was not included in this study as it was deemed irrelevant to everyday fresh meat purchase behaviour. Perceived Behavioural Control

The five items used to measure PBC were similar to those used by Armitage and Conner (1999), but were adapted for this study (i.e. the purchase of QA/FA labels). The items used to assess control were: “It is completely up to me whether or not I read QA/FA label information”; “If I wanted to, I could easily find out about QA/FA labels”; and “For me, reading and understanding QA/FA labels is easy/difficult”. All items were assessed using 5-point unipolar scales ranging from 1 (strongly disagree) to 5 (strongly agree) for the first two statements and 1 (Extremely difficult) to 5 (Extremely easy) for the last statement. Inter-item reliability (Cronbach’s α), for the three items was .79. The items used to assess self-efficacy were: “I am confident in my ability to recognise this QA/FA label”; and “ I am confident of my knowledge about the underpinning standards of each label”. Both items were assessed using 5-point unipolar scales ranging from 1 (strongly disagree) to 5 (strongly agree). Self-efficacy scores were summed on the basis of responses to each QA/FA label measured. Inter-item reliability (Cronbach’s α) for the two items was .86. Despite many studies reporting improved prediction levels by distinguishing between separate measures of self-efficacy and controllability (Terry and O’Leary 1995; Armitage and Conner 1999; Ajzen 2002), little variance was identified when using separate measures. Therefore, both self-efficacy and controllability are aggregated to produce a measure of perceived behavioural control (PBC). Knowledge

Respondent’s recognition, claimed knowledge and actual knowledge were gauged using sixteen national QA/FA labels and one dummy label. Each respondent was assessed on the accuracy of their responses to each label, scoring 1 for each correct answer and 0 for each incorrect answer. Total scores were then summed according to knowledge of producer-led, independent and retail QA/FA labels. Inter-item reliability (Cronbach’s α) for the three items was .81. Past Behaviour (Habit)

Past behaviour is commonly noted as an important factor in predicting frequently performed behaviours such as food purchases (Bentler and Speckart 1979; Ronis et

Improving Customer Orientation 115 al. 1989; Grankvist et al. 2004). Moreover, Conner et al., (2000) argue that measures of past behaviour offers superior predictions of behaviour than behavioural intention, therefore, to identify the role of habitual purchase behaviour, a single item measure was used to assess the frequency of respondents’ purchases of QA/FA meat. Respondents were asked to state the frequency with which they purchased the same QA/FA label when shopping. This was recorded on a 5-point unipolar scale ranging from 1 (never) to 5 (always). Note also that inter-item reliability could not be calculated due to measurement on a single item scale. Personal Identity

Personality information was measured using Goldberg’s (1998; 1999) International Personality Item Pool (IPIP) scale, consisting of 1412 items and 45 preliminary bi-polar dimensions. Cost and availability factors are the primary justifications for the selection of Goldberg’s (1998; 1999) IPIP scales. Goldberg’s (1998; 1999) IPIP items, measure five NEO domains (i.e. Neuroticism, Extraversion, Openness to new experience, Agreeableness, Conscientiousness). Alpha values (Cronbach 1951) showed clear coherence amongst each NEO-domain, with neuroticism possessing an alpha value of 0.87 (13 items), extraversion - a value of 0.83 (13 items), openness - a value of 0.74 (13 items), agreeableness - a value of 0.75 (13 items) and conscientiousness possessing a value of 0.86 (13 items). Of all five personal identity variables, Openness was the only construct to show major correlations and improve predictive capability within the SEM model. About the Authors Dr Morven G. McEachern is a lecturer in Marketing and Consumer Behaviour at the School of Management, University of Salford. Before academia, she was previously a project manager for an agribusiness research and consultancy company. Morven’s research interests are in consumer purchase behaviour and their perceived value of labelling formats, consumer ethics and agri-food marketing. Her research work has appeared in such publications as the International Review of Retail, Distribution and Consumer Research, Journal of Agricultural and Environmental Ethics and the Journal of Consumer Marketing. Dr Gary Warnaby is a senior lecturer in marketing in the School of Management at the University of Salford. His specialist research interests are retail strategy, town centre management and the marketing of place. He has presented papers at a number of international conferences and has published articles in a wide range of academic and professional journals, as well as contributing chapters to various books in both the retailing and public relations fields.