An Empirical Investigation of High
Relational Orientation Sport Club Members

Stewart Adam [HREF1], Associate Professor in Electronic Marketing, Faculty of Business and Law, Deakin University, 221 Burwood Highway, Burwood, VIC, 3125. Telephone: +61.3.9244.6054. e-mail: stewarta@deakin.edu.au

Heath McDonald, Executive Director, Centre for Business Research [HREF2], Faculty of Business and Law, Deakin University, 221 Burwood Highway, Burwood, VIC, 3125. Telephone: +61.3.9244.6206. e-mail: hmcd@deakin.edu.au

Hossein S. Zadeh, Lecturer, School of BIT, Faculty of Business, RMITU, 229 Bourke Street, Melbourne, VIC, 3000. Telephone: +61.3.9925.5086. e-mail: hossein.zadeh@rmit.edu.au

Abstract

Relationships between businesses, businesses and end customers, as well as between customers are an important area of practical and scientific interest. In the present era, largely due to digital technologies such as the database, public and private networks, and data collection and information distribution via TCP/IP (Transmission Control Protocol/Internet Protocol) interface tools such as the World Wide Web (Web), the interest in relationships and related aspects such as trust as it relates to Web interactivity continues. An important antecedent empirical study established that arts patrons (customers) of a New York theatre company could be segmented according to their relational orientation, and that this orientation mediated between component attitudes and future purchase intentions. The study reported in this paper employs Web-based data collection and postal data collection methods in an investigation of the mediation effects of these data collection methods used with the same population of a premier football club in Australia. While a future aim is to more closely compare the outcomes established in the arts study with those from a similarly constructed study in the sporting arena, the focus of this initial paper is the differences in response exhibited by online respondents relative to postal survey respondents. The paper reports findings which do not support those of the antecedent arts and entertainment study concerning the weakness of overall satisfaction on the purchase intentions of high relational orientation customers. The paper also reports findings which give confidence to users of online surveys that despite differences in demographic profiles of these respondents and postal survey respondents, there is a degree of similarity in the responses of the two groups on the measures used in this study. The paper also suggests the need for further research into these data collection effects as they relate to relationship marketing.

Background

Commercial relationships are of wide practical interest and empirical study, having formed the backbone of commerce and industry since 3,500 BCE (before the Christian era) (Moore and Lewis 2000). Relationships between businesses (Morgan and Hunt 1994), between businesses and end customers (Gronroos 1994), as well as between customers (Martin 1996) are of continuing scientific interest. In the present era of addressability (Blattberg and Deighton 1991), largely due to digital technologies such as the database, public and private networks, and data collection and distribution via TCP/IP interface tools such as the World Wide Web (Web), the practical interest in relationships and management of expectations from these relationships and the relationships themselves continues (Adam 2002). This scientific interest extends across a number of inter-related business disciplines (e.g., business information systems, law, marketing and management) and extends to customer views of trust as it relates to the interactivity this medium permits (Merrilees and Fry 2003).

There have been different views expressed concerning the relevance of customer relationships in the marketing discipline (Gronroos 1994) and also in business information systems when referring to e-Commerce (Turban et al. 2002) – the transaction element of online or electronic business (Adam and Deans. 2000). However, whether referring to such aspects as customer addressability (Blattberg and Deighton 1991), interactivity, ‘share of customer’ (Peppers and Rogers 1995) and supposed differences in the marketing of ‘almost pure services’ over ‘almost pure goods’ (Kotler et al. 2003, p. 254) some aspects are more salient than others. Among the diverse views of marketing are notions that marketing is concerned almost solely with exchange transactions. Another view is also professed by some, notably those advocating a complete ‘paradigm shift’ from a focus on transactional exchanges to relationships (Gummesson 1997). A third position taken is where both exchange and relationship paradigms can co-exist (Dwyer et al. 1987).

In the context of retaining customers and achieving profitability, views are put forward concerning the need to hold the most profitable customers, since they are argued to provide the highest profitability over time as they are prepared to pay more, and cost less to service. For example, the widespread view is that loyalty programs hold profitable customers (Reichheld 1993; Reichheld and Schefter 2000) and that businesses holding these customers for as long as possible are on the path to profit optimisation. However questions arise in this regard, and the counter view is that loyalty programs do not always provide the intended financial outcomes, for loyal customers do not always equate to high profits (Dowling and Uncles 1997; Reinartz and Kumar 2000). Reinartz and Kumar (2000, p. 24) found a number of discrepancies with long held views on customer lifetime value and profitability such as that “although long-term customers in Segment 1 (long customer tenure and high lifetime revenue) are important to the firm, short-term customers in Segment 3 (short customer tenure and high lifetime revenue) are important as well, because they generate more than a quarter of the total cohort profits”.

In any event, it is necessary to identify then hold the highest spending customers, and this is more easily enabled in the case of the subscriber market (e.g., football club and theatre company members) as datamining techniques can be employed. This is not as easily achieved in the case of repertoire markets (e.g., most fast moving consumer goods), although the use of "summary repeat purchase statistics from panel data" and more sophisticated modelling can be used (Sharp et al. 2002).

Relational Orientation

Businesses differ in the markets they face and the degree to which they do and need to interact with their potential and actual customers. In an extreme example, the motor car manufacturer might know that home users adopt a ten year repeat purchase cycle but will also know that it cannot afford to maintain a close relationship with customers who are not in the market. Thus far, however, we have not considered the fact that individuals exhibit different relational orientations. If they did not exhibit this, then either all football supporters would be members, or all members would merely remain supporters. The marketing task for football clubs is to predict their on-going membership base and thus prepare accurate cashflow, and profit/loss projections.

Turning to a New York off-Broadway repertory theatre company, Garbarino and Johnson (1999) employed two models in their attempt to predict customer intentions that included subscription renewal. On the one hand, they employed survey measurement of trust and commitment and examined the mediating effect of this variable between attitudes and intentions concerning ‘future attendance, subscription, and donation’ (p. 72). On the other hand, they employed survey measurement of customers’ overall evaluation of the marketing organisation and examined the mediating effect of this variable between component attitudes – ‘actor satisfaction, preference for familiar actors, play satisfaction, and theatre facility satisfaction’ (p. 73) – and ‘the constructs of trust, commitment, and future intentions’ (p. 72).

Garbarino and Johnson (1999) define the constructs trust and commitment, as we do in the present study in a subsequent section, and introduce the notion of relational orientation. These researchers show that overall satisfaction, trust and commitment can be separately identified and that they interact differently depending on the relational orientation of the customer in the particular subscriber market they examine. The importance of focusing on component satisfaction as delineated earlier is that management can focus marketing strategy on aspects where weakness is identified. Also importantly, these researchers make the point that for customers with weak relational orientation, the component ‘actor satisfaction is the main driver of their overall satisfaction and therefore their future intentions’ (p. 81). For those customers with strong relational orientation, ‘actor satisfaction is the main driver of trust, and preference for familiar actors is the main determinant of commitment’ (p. 81). Thus, a number of important findings stem from this study in the arts arena that while not doing Garbarino and Johnson due justice may be summed up by stating that the future intentions of low relationally oriented arts patrons are driven by overall satisfaction, whereas those with high relational orientation are driven by trust and commitment.

Sport and Relational Orientation

Given that there are few such important studies into the mediating effects of relational orientation in subscriber markets, we are working towards replication of the Garbarino and Johnson study in the market for sport – viz. premier league football in Australia – and thereby test the generalisability of their findings. In the lead up to this study, we have already undertaken a number of satisfaction surveys for two football codes in Australia and have employed both online (email and Web form) and postal data collection methodologies.

The data analysis and findings reported in this paper are also concerned with the differences in the influences of commitment, trust and overall satisfaction on purchase intention, if any, that might be evidenced by respondents polled by the two data collection methods. Given the much lower costs associated with online data collection reported by (McDonald and Adam (2003) and others (e.g., Weible and Wallace 1998), the final choice of data collection method (online, postal or hybrid) and concomitant costs for the proposed study will be influenced by the findings of the data analysis reported herein.

Where Garbarino and Johnson (1999) were able to engage respondents exhibiting both high (members/subscribers) and low relational orientation (individual ticket buyers), only members were invited to respond in the present study i.e., those exhibiting high relational orientation. Garbarino and Johnson define trust “as customer confidence in the quality and reliability of the services offered by the organization” (1999, p. 73) and use seven items employing seven point scales to ascertain this variable. In the sport study analysis we report, a single item is used as opposed to multiple items – regardless of what the club does, I will always be a member – rather than an item such as ‘always meet expectations’ (1999, p. 77). Garbarino and Johnson also define commitment as “customer psychological attachment, loyalty, concern for future welfare, identification, and pride in being associated with the organization” (1999, p. 73), and employ four items for this measure. In the sport study analysis, an 11 point scale item – encourage every supporter I know to join as a member – is employed. Where Garbarino and Johnson examined future intentions, we examine likelihood of rejoining next year. While the sport study gathered information on a number of measures of overall satisfaction, for practical reasons in the data analysis we report, a single measure is used that involves responses to an 11 point scale item – overall satisfaction. We also employ the measure overall satisfaction with on-field performance given the criticality of this aspect in the antecedent study and in the hypotheses that follow.

Aims of the Study

The immediate aim of the analysis we report is to test aspects of the antecedent study in the context of sports club membership, and as a prelude to a further study, we wish to be sure that the choice of data collection method will not unduly influence outcomes.

Hypotheses follow:

H1: For customers with high relationship strength (members), satisfaction with on-field performance influences overall satisfaction.

H2: For customers with high relationship strength (members), satisfaction with on-field performance influences trust.

H3: For customers with high relationship strength (members), satisfaction with on-field performance influences commitment.

H4: For customers with high relationship strength (members), commitment to the club is related positively to trust in the club.

H5: For customers with high relationship strength (members), satisfaction with on-field performance influences intentions to rejoin.

H6: For customers with high relationship strength (members), trust influences intentions to rejoin.

H7: For customers with high relationship strength (members), commitment influences intentions to rejoin.

H8: For customers with high relationship strength (members), overall satisfaction influences intentions to rejoin.

H9: For customers with high relationship strength (members), hypotheses as expressed will be supported or refuted whether data is collected online or via postal survey method.

Football Study Methodology

The study which gathered the dataset analysed in this paper involved a satisfaction survey for an Australian Football League (AFL) club XYZ Club. Data was collected online and via the post employing a seventy-item questionnaire. The postal version ran to seven pages. Each questionnaire was entitled "Club Name (withheld here) Member Satisfaction Survey". As already indicated, each questionnaire employed similarly framed scale items, with an eleven-point (0 – 10) "poor" to "excellent" scale.

Two samples were drawn from the then 21,000 members – 1,026 were sent a postal survey which engendered 471 complete responses (46%), while the club emailed 3,900 members inviting them to visit a secure website and complete an online questionnaire which in turn resulted in 826 complete responses (21%). The two versions of the questionnaire were almost identical. The online form questionnaire involved minimal use of colour and employed radio buttons and a 'scrollable' HTML form that disclosed all questions to respondents. While it is possible to use Javascript to error check missed items, this was not done in this study.

In line with Garbarino and Johnson (1999), members who had joined in the year of the survey were removed. Table 1 indicates that the profile for online respondents differs to postal respondents in that online respondents tended to be younger, had a higher income, higher occupation level and were at an earlier stage in their family lifecycle.

Data analysis involved Correlations and multiple regressions were employed to evaluate the significance of the relationships between the variables, and the predictiveness of the relationships. To determine the strength, direction and significance of the correlations Spearman correlation coefficients were calculated, given that the scale data is being treated as ordinal data. Multiple regression analysis (optimal scaling) was undertaken to analyse variance (ANOVA), and the adjusted R² used to establish significance and predictiveness respectively.

Table 1. Demographic profiles of online and postal respondents

Findings and Discussion

An issue arises in the use of the two survey methods in such a study involving high relationship customers in that there are statistically significant differences in the demographic profiles of the postal and online respondents as Table 1 illustrates. This is the case for income, occupation, age, lifecycle stage reached and extends to the length of membership.

Turning firstly to a comparison of the online and postal responses in this study, as summarised in Table 2, we find that there are statistically significant differences between the online and postal respondents in terms of their responses to Satisfaction with on-field performance, and Intentions to rejoin. Their responses are statistically similar with respect to Overall satisfaction, Trust and Commitment.

Table 2. A Comparison of Online and Postal Responses

Note: Low = 0 – 3; Medium = 4 – 7; and High = 8 – 10 on the 11-point scales used.

Examining online responses alone, there is a positive correlation between Satisfaction with on-field performance and Overall satisfaction in the case of online respondents (Spearman r = .41, p = .00) and in the case of postal respondents (Spearman r = .37, p = .00). In the case of online respondents, there is support for H1 shown by multiple regression (F = 16.72, p = .00), and 17 per cent of the variance in overall satisfaction is explained by satisfaction with on-field performance (R²= .17). In the case of postal respondents, there is similar influence (F = 20.42, p = .00), however, a quarter of the variance in overall satisfaction is explained by on-field performance (R² = .25). This hypothesis is therefore supported in the case of both groups of respondents.

Turning to H2 and online respondents, we find a weak positive correlation between Satisfaction with on-field performance and Trust (Spearman r = .18, p = .00). A similarly weak positive relationship is found in the case of postal respondents (Spearman r = .14, p < .05). There is support for H2 in that Satisfaction with on-field performance influences Trust (F = 5.68, p = .00) as it does in the case of postal respondents (F = 2.47, p < .05), however, in both cases only a small proportion of the change in trust is accounted for by change in satisfaction with on-field performance in the case of online respondents (R² = .04) and postal respondents (R² = .02).

In the case of H3, there is a weak positive correlation between Satisfaction with on-field performance and Commitment in the case of online respondents (Spearman r = .08, p < .05). In the case of postal respondents there is not a statistically significant correlation evident (Spearman r = –.06, p = .28). For online respondents, H3 is supported in that Satisfaction with on-field performance is a predictor of Commitment (F = 6.88, p = .00), however, only a small proportion of the variance in commitment is explained by satisfaction with on-field performance (R² = .03). The situation is not supported in the case of postal respondents where there is not a statistically significant correlation between Satisfaction with on-field performance and Commitment. For postal respondents Satisfaction with on-field performance is not a statistically proven predictor of Commitment (F = 2.76, p <.05), and a negligible proportion of the variance in commitment is explained by views of on-field performance (R² = .02).

The next issue to be addressed with both groups of respondents is the nature of the relationship between TrustandCommitment, if they are related at all. For both online and postal respondents the correlation between the two variables is positive (Online: Spearman r = .38, p =.00; Postal: Spearman r = .35, p =.00). Thus, H4 is supported for both groups.

A perennial issue for sporting organisations is how satisfaction with on-field performance influences intentions to rejoin the club. In the case of online respondents, there is a very weak, but statistically significant, positive relationship between Satisfaction with on-field performance and Intentions to rejoin (Spearman r = .14, p =.00). The situation is similar for postal respondents (Spearman r = .11, p < .05). Satisfaction with on-field performance is a not a predictor of Intentions to rejoin for the online respondents (F = 1.67, p =.17) and satisfaction with on-field performance does not explain the variance in intentions to rejoin (R² = .00). In the case of postal respondents, on-field performance is a weak predictor of intentions to rejoin (F = 2.96, p =.00). However, on-field performance perceptions explain little of the respondents intentions to rejoin (R² = .02). We are inclined to conclude that H5 is not supported for either group of respondents.

A further point of interest is how trust influences football club members' intentions to rejoin. We find a positive relationship for both groups of respondents between Trust and Intentions to rejoin. For online respondents (Spearman r = .44, p =.00) the correlation between the two constructs is slightly weaker than for postal respondents (Spearman r = .50, p = .00). In the case of online respondents, Trust influences Intentions to rejoin (F = 20.24, p =.00), and we see that 17 percent of the variance in intentions to rejoin is explained by trust (R² = .17). Thus, for this group of respondents H6 is supported. The situation is similar in the case of postal respondents with regard to H6 (F = 23.48, p = .00) (R² = .29).

Next, we examine the influence of commitment on members' intentions to rejoin. For online respondents the correlation between Commitment and Intentions to rejoin is weak but statistically significant (Spearmen r = .23, p =.00). The correlation between the two variables is similar for postal respondents (Spearman r = .20, p =.00). In the case of online respondents, Commitment is a weak predictor of Intentions to rejoin (F = 7.60, p=.00)( R² = .04). The situation regarding postal respondents is similar in that H7 is again weakly supported (F = 6.88, p = .00)(R² = .06).

Here, we turn our attention to the nature of the influence overall satisfaction exerts on intentions to rejoin. The correlation between Overall satisfaction and Intentions to rejoin is weaker for online respondents (Spearman r = .32, p =.00) than for postal respondents (Spearman r = .38, p = .00). H8 is supported for both online (F = 13.45, p = .00)(R² = .12) and postal (F = 10.85, p =.00)(R² = .16).

Lastly, it is of note that H9 is not entirely supported for although there are mostly similarities between the two groups of respondents in the strength of the relationships uncovered, there are also differences. Many hypotheses are similarly supported (H1, H2, H4, H6, H7 and H8) and similarly refuted (H5) in the case of both online and postal respondents. However, there is the exception that on-field performance influences commitment (H3) differently for the two groups.

Implications for Future Study

It is reiterated that this study focuses on high relationship customers, i.e., football club members. It is also reiterated that the constructs used only approximate those used by Garbarino and Johnson (1999) in that single measures have been used for each construct (overall satisfaction, commitment, trust, on-field performance and intentions to rejoin) where the antecedent study employed a number of measures for each. We therefore accept that there may be some content validity issues concerning the measures used in this analysis.

It has been found that overall satisfaction, trust and commitment each influence members' intentions to rejoin. Postal respondents' commitment to the club is less likely to be influenced by on-field performance. However, in the case of both groups of respondents on-field performance has little or no predictive power.

While the profiles of the two groups of respondents differ significantly, the findings tend to give confidence that an online study would bring forth results that almost match those of a postal survey. The findings also give rise to some concerns. If multiple measures of each construct had been used, and the weak predictiveness of the constructs continued, there would be a temptation to conclude that there are unmeasured influences on members' future intentions. We might conjecture that attitudes toward the core product are not being measured (e.g., experiential and emotional aspects), or that unmeasured personal characteristics have an effect (Homburg and Giering 2001), and that the measures of the actual product (e.g., on-field performance) have a minor influence on commitment, trust, overall satisfaction and ultimately on customer intentions.

The findings from this analysis do not support Garbarino and Johnson (1999, p. 82) who found that "For high relational customers, overall satisfaction has no significant influence on future intentions". From their conclusions, Garbarino and Johnson argue that relationship marketing programs to such high relational customers as football members "should focus on maintaining and building trust and commitment, not satisfaction" (p. 82). From this initial analysis, we are unable to draw such a firm conclusion, rather finding that across all respondents to the survey, overall satisfaction (F = 22.36, p = .00)(R² = .13) has greater influence than commitment (F = 9.58, p =.00)(R² = .04) and only slightly less influence than trust (F = 35.80, p = .00)(R² = .19). A future use of measures that replicate those employed by Garbarino and Johnson may, however, bring forth similar findings.

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Hypertext References

HREF1

http://www.stewartadam.com/

HREF2

http://www.deakin.edu.au/cbr/

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