Whilst Engel and Blackwell (1982:501) conceptualise satisfaction as ‘an evaluation that the chosen alternative is consistent with prior beliefs with respect to that alternative’, Andreassen (1995:33) suggests that customer satisfaction represents ‘the accumulated experience of a customer’s purchase and consumption experiences’ and the performance that is perceived is related to ‘the consumer’s perception of quality, marketing mix, brand name and image of the company’. It is therefore of no surprise that, because of an interest amongst firms to increase their rate of customer retention, interest in understanding how service components influence customer satisfaction continues to grow following seminal work by Slater (1997), who sought to develop a customer value–based theory of the firm that ‘maximises the effectiveness of the firm’s customer value creation activities’ (p. 165). Underpinning our understanding of customer satisfaction is the expectancy–disconfirmation theory first introduced by Oliver (1981). According to scholars (e.g. Aurier and Guintcheva 2014; Sengupta, Balaji & Krishnan 2015), the expectancy–disconfirmation theory posits that customers form a perspective of satisfaction against a specific service and that this is carried out mainly subjectively by comparing their idiosyncratic preferences and ideal expectations. In other words, customer satisfaction remains a psychological construct of expectations and perceptions.

Articulation of the satisfaction construct remains complex not only because of the intricate nature of the interrelationships that exist between assessment attributes which represent the general evaluative criteria for assessing the quality of service employed in judging satisfaction but also because of situational differences that may arise from customers’ perceptions of ‘quality’ (Calabrese & Scoglio 2012; Dagger & Sweeney 2007; Parasuraman, Zeithaml & Malhotra 2005; Zayer, Otnes & Fischer 2014). Thus, perceptions of service quality may influence customer satisfaction (Burton, Sheather & Roberts 2003; Dagger & Sweeney 2007; Nyffenegger et al. 2014; Sureshchandar, Rajendran & Anantharaman 2002). In other words, quality – which refers to the totality of the characteristics of a service (Golder, Mitra & Moorman 2012) – remains a key factor for researchers seeking to understand consumer satisfaction judgements. These notions of service, quality and customer satisfaction also apply to transportation (Ettema et al. 2011; Weinstein 2000).

A review of literature (see, e.g. De Ona & De Ona 2015; Eboli & Mazzulla 2011; Fellesson & Friman 2012; Morris & Guerra 2014; Olsson et al. 2012) shows that numerous studies dealing with customer satisfaction in public transportation have been undertaken. Studies specific to bus transport services include that of Hensher et al. (2003), who, following the discovery that key challenges still existed in the identification of how bus customers assessed satisfaction with service levels, suggested the need to quantify service quality levels as a means of comparing service levels within and between bus operators.

Other studies include that of Stradling et al. (2007) that examined the customer experience of the ideal bus journey. As part of the study, they examined the influence of factors such as gender, age and frequency of bus use on the experience of bus users. In another study, Gatersleben and Uzzell (2007) examined affective experiences of daily bus commuters, finding that public transport users generally perceived their experiences as unpleasant. This negative affect was generally seen to be driven by a number of factors such as stress associated with waiting times and delays. In response to similar findings on the negative impact of waiting times on the experience of bus customers, scholars such as Dell’Olio et al. (2010), Politis et al. (2010) and Tang and Thakuriah (2012) have examined the role of real-time bus information systems on improving the overall bus transport experience. Fellesson and Friman (2012), for example, found four satisfaction dimensions – system, comfort, staff, and safety – to be of importance in their assessment of perceived satisfaction with public transport service across nine European cities. At the same time, the findings from the studies by Gatersleben and Uzzell are not surprising in view of much earlier work by Van Vugt, Van Lange and Meertens (1996) into motivational factors underlying the decisions by passengers on whether to commute by car or by public transportation. Van Vugt et al. (1996) had found that commuters preferred journeys that involved not only shorter travel time but also alternative and direct travel routes (Stradling et al. 2007). The question of routing is particularly important, noting the findings of earlier studies. Numerous studies (Broome et al. 2012; Kepaptsoglou & Karlaftis 2009; Van Oudheusden, Ranjithan & Singh 1987; Wirasinghe & Vandebona 2011) all show that optimised bus route designs have the ability to simultaneously enhance customer satisfaction with bus service operations.

An interesting strand of work that has begun to emerge in transportation studies deals with customer satisfaction as relates to ‘mood and mode’. These studies which include, for example, the works of Choi, Coughlin and D’Ambrosio (2012), De Vos et al. (2013), Morris (2013) and Morris and Guerra (2014) have focused on exploring the relationship between satisfaction, access to transportation and travel behaviour. The findings of these studies, whilst not statistically significant, suggest that although individual preference is a major determinant of mode of transportation, a relationship does exist between customer mood and their chosen or preferred mode of transportation. Generally, bus (and train) passengers have been found to have the most negative emotions whilst bicyclists have the most positive emotions. At the same time, there appears to be a relationship between travel behaviour (in terms of the actual commuting experience) and mood, with some scholars such as Choi et al. (2012) and Morris (2013) finding that passengers with longer distances to commute tend to have low levels of satisfaction with their travel experience.

More pertinent to our study, Wall and McDonald (2007) and Wall et al. (2008) had identified the key determinants of satisfaction with bus travel to include frequency, reliability, bus information, comfort of travel, friendliness of bus driver, competence of bus driver, appearance of bus shelters or stops, cleanliness, cost, route, time of travel and socio-demographic factors. In addition, Eboli and Mazulla (2007) had sought to rate customer satisfaction with bus campus services against 16 service quality attributes (see Table 1). In the process, they found three over-arching attributes – (1) planning and reliability, (2) comfort and other factors and (3) network design – as factors impacting customer satisfaction with campus bus services. Similarly, in an extensive study, which involved international comparisons of satisfaction with public transportation experiences, Fellesson and Friman (2012) found four key attributes – (1) systems, (2) information, (3) design and (4) skills – which all seemed capable of significantly impacting the service experiences of public sector transport users.

The outcome of the SEM analysis is that three different service attributes – Driver, Planning_Reliability and Routes_Service – are seen to significantly impact customer service satisfaction, thus addressing the first research question (What service attributes significantly influence the satisfaction of customers using bus services?). Our findings are in line with extant literature not only on the moderating role of cost on services (see Sharma 2003), but also on the role that frontline employees in campus bus transportation service providers play in the customer’s service satisfaction (Ellinger, Keller & Bas 2010). In addition, also in line with existing literature (Diana 2012; Hensher et al. 2003; Salicru, Fleurent & Armengol 2011), it was expected that variables such as frequency, punctuality and journey times, all of which measure the construct Routes_Service, would have a high impact on customer satisfaction; however, this construct, that is Routes_Service, had a low standardised path coefficient of 0.06. On the other hand, in line with earlier studies (Eboli & Mazzulla 2011; Politis et al. 2010; Wall & McDonald 2007), Planning_Reliability has a high impact on customer satisfaction. In the case of our model, this construct had a path coefficient of 0.49.

It might be the case that the variables that measure Routes_Service, that is punctuality, routes, frequency, location of bus stops and journey times, are hygiene factors, which do not necessarily lead to customer satisfaction. Regarding the underlying case of customer satisfaction, it might be that those variables are important, even though this is not directly implied by our results. The service quality area, Planning_Reliability showed the greatest impact on customer satisfaction (0.49) amongst the three constructs. This emphasises the importance of the underlying variables that contribute most to the construct; namely, appearance of the bus (0.74), cleanliness (0.7), security feeling (0.68) and comfort (0.71). Those variables all aim to ascertain customers’ perceptions on the buses, that is how passengers perceive the overall experience of their bus journey.

In terms of the second research question (Does customer satisfaction vary depending on bus transport route and frequency?), the ANOVA showed that there were statistically significant differences between the service quality areas and bus routes and frequency. Nevertheless, the effect size measured by the Eta squared indicated small effects on the difference in means. Pallant (2010) argues that even though some results of the ANOVA might indicate statistical significance, the interpretation can be impractical when mean scores only differ slightly. With a large sample, small differences in mean scores might become statistically significant, although the difference between the groups has only limited practical meaning (Pallant 2010).

The study examined the determinants of satisfaction with campus bus transportation. The research reflected scholarly interest on the selection of modes of transport service, but specifically that related to buses – a mode of transport characterised by its popularity. Within this context, the study also explored possible implications of satisfaction with such campus bus transportation for service quality. The study is of particular relevance to logistics research for a number of reasons including the fact that universities and other institutions of higher education are increasingly embracing the concept of campus shuttle services in order to reduce costs – for example, those relating to parking operations. More specifically, Farzaneh et al. (2009:vi) suggest that such strategies ‘have positive impact on the liveability of the campus and surrounding neighbourhoods and can provide substantial fiscal benefits for the university…’. Traditionally, worldwide, the transportation systems of most universities had been predominantly car-dependent. However, in light of emerging concerns relating to safety, noise and pollution, and the demand for land required to build and operate parking facilities, mainly for single-occupancy vehicles, the operation of ‘dedicated’ campus bus transportation (shuttles) has become increasingly popular. Taking this into consideration, this study presented two research questions: (1) What service attributes significantly influence the satisfaction of students using campus bus services? and (2) Does customer satisfaction vary depending on bus transport route and frequency?

In terms of the first research question (What service attributes significantly influence the satisfaction of students using campus bus services?), based on the outcome of the SEM analysis, we found that three different service attributes – Driver, Planning_Reliability and Routes_Service – significantly impacted upon customer service satisfaction with campus bus transportation. In terms of the second research question (Does customer satisfaction vary depending on bus transport route and frequency?), overall, results from our study suggested that customer satisfaction varied depending on bus transport route and frequency.

The study also has considerable implications – in particular, noting that a fundamental objective of services theory focuses on developing an appreciation of not only the service experience of customers but also the way that those experiences are evaluated (Buffa & Ross 2011).

As earlier highlighted, scholarship emphasises that positively evaluated service experiences are primary drivers of customer loyalty and trust in high-contact service scenarios, such as bus transportation. But for what purposes can customer service experience data be used? Consider that, because of contractual guarantees, as a service established to predominantly cater for university students (and staff), arguably, the Uni-link service may be perceived to operate a near monopoly over campus bus services in Southampton. The key implication for managers is that the operators of the Uni-link service in this case lack obvious competitive pressures to improve service satisfaction and performance. The danger here is that one of the managers (or more than one) may succumb to complacency with respect to service quality, which may damage trade-off decisions where cost reduction takes priority over maintaining and improving the quality of service provided to its customers. Hence, the firm might usefully manufacture their own clear incentives. A first step is to conceive of service improvement as something more than reactive fire-fighting in response to dips in performance on explicitly stated and identified customer experience attributes. One way forward for the operators of the Uni-link service is to focus more on one, one additional very specific measure of service satisfaction, ‘customer accountability’. Through the provision of transparent customer service indicators, derived from and improved through consultation with customers themselves, the service operator might use customer discipline as a substitute to clearly manifest market discipline in driving forward enhanced service quality improvements. This, though, would hinge upon the determination of Uni-link management to report to its newly constituted ‘passenger panels’ with a clear mandate to demonstrate quality improvement to them (dependent on how the panels interpret ‘quality’) instead of simply using the panels to determine isolated ‘local’ incidents and issues requiring isolated managerial responses. It thus appears that an opportunity for further study may involve examining bus services under different market conditions. One such condition may involve full competition along Uni-link’s preferred routes. Such studies will also be beneficial to scholars interested in understanding which service attributes play critical roles under different service conditions.

Whilst transport services operating in a fully competitive environment may seek to utilise service evaluation data to attract and retain customers by improving the efficiency of internal processes and operation, one could argue that, for firms offering dedicated services such as the operators of Uni-link, the main focus on service provision may need to switch from service evaluation to customer evaluation under conditions of improved transparency and accountability to customers. However, crucially, service evaluation data and processes are only useful to the extent that they provide an evidence base for managerial action to enhance or at least meet customer satisfaction expectations. Fundamentally, this entails focusing on weaknesses rather than the strengths that managers may prefer to focus on for purposes of performance evaluation. Of course, in a service environment established to predominantly service university students (and staff), such as the Uni-link services, the reality is that it may prove challenging to measure the hotspots of negative affect that influence customer satisfaction for a number of reasons including, for example problems where highly vocal individuals influence group discussions on customer panels. Another issue is that managers may come under pressure to also accede to different expectations of wider stakeholder groups (which in this case comprises students, the University and the Local Council). Conceivably, a range of stakeholder opinions might vary by individual bus route, which makes it important to privilege customer data based on direct service experiences rather than on general impressions of overall service levels.

Our research does show some general trends; yet the insight these trends yield of services at the individual route level ought to be demonstrated rather than assumed. Earlier studies that focused on service delivery challenges in the public sector (see Kelly 2005) suggest that, instead of engaging with these issues, service delivery managers are likely to ‘focus their attention on internal measures of service delivery and not on external measures of value creation from consumers’ (Kelly 2005:77). However, Kelly (2005) further suggests that service delivery decisions are best made where managers are able to harness inputs from customers who have been reasonably acquainted with information, which then enables them to conduct their own service quality assessments. Taking stock of Kelly’s observations, we suggest that, if they are to rise to their service delivery challenges and so maintain or improve their market positions, bus transportation service providers that predominantly service university students (and staff) such as Uni-link need to (re)consider the interrelationship between risk of financial loss and service quality. Clearly, competitiveness is a function of service quality, and so the mitigation of risk of financial loss (particularly in near-service environments where specific competitive threats are not readily manifest and therefore not readily amenable to individually tailored risk-control activities) entails the general pursuit of quality improvement. Quality improvement, of course, entails exploiting opportunities for service innovation. However, as with all innovations the effect of quality improvement in this case is not automatically to reduce risk, but rather to modify the campus bus transportation service provider’s exposure to risk. After all, innovations can be unsuccessful – they can be cost-ineffective and may carry reputational penalties that are difficult to quantify in financial terms. Amongst innovating dedicated campus bus transportation service providers, innovations often have both their promoters (who emphasise risks associated with failing to innovate) and their detractors (who emphasise risks brought by innovation) (see Marshall & Ojiako 2010). Given the uncertain and trial-and-error nature of service innovation, then, it makes sense to widen the service providers’ ‘innovation debate’ by maybe having customer panels ‘plugged in’ as consultees to service innovation processes. This could be undertaken on both an ex ante basis and an ex post basis to service innovations, which may include actually changing the entire competitive landscape that presents students with alternative options when they need to shuttle on and off campus. One such approach may include service providers such as Uni-link collaborating with the University to invest more heavily in embracing blended learning. For example, increased use of Virtual Learning Environments may reduce the need for students to have to be physically present on campus at specific times. Its effect would be to attune the service provider on an ongoing basis to the effects of service innovations upon customer experiences of service quality, in whatever terms customers choose to frame their experiences. In effect, customer-dominant logic can then exert a positive influence on business logic.

In conclusion, the paper provides useful insight into the range of customer experience factors that may be relevant with respect to any given service innovation, but we stress that a viable understanding of customer experience, useful for a healthy customer–manager debate that can help steer service innovation over time, can only emerge where there is effective and ongoing communicativity and dialogue between customers and managers at the interface between customer logic and business logic. In sum, the contributions of the paper not only lie in undertaking the examination of service attributes and their influences on customer satisfaction in campus bus transportation but also in emphasising that managerial attention to service user experiences in predominantly dedicated transportation services alone will fail to deliver competitive success. Instead, management should invest additional effort to significantly enrich service by paying greater attention to the interface between risk of financial loss and service quality. We posit that our findings are valuable as promptuaries or ‘touchstones’ which can be used to encourage customers, for example on the Bluestar (another bus company) customer panels, to offer better informed and more nuanced views of their customer experiences. For example, if presented with our findings, customers would be able to comment on whether existing metrics seem adequate, and they may simultaneously be disciplined by findings based on these metrics, thus being more cautious and realistic when purporting to represent the general views of customers. The effect of this is likely to be benign, because it will help localise discussions at the level of actual customer experiences and the (customer) emotions they evoke.

As expected, this study was not without limitations. Four main limitations are identified. Firstly, examining the data shows that the actual number of bus passengers going to the university was 133 out of 847, representing approximately 15% of the Uni-link service. A possible reason for this lower-than-desired sample could be related to the fact that the data were gathered towards the end of term (which ended on 18 June), at a time when the majority of students were taking their examinations. Another possible reason could be that a lower-than-anticipated number of students utilise the service. Thus, to improve both response rates, future studies could focus on not only undertaking the collection of data perhaps in mid-term (when most students are likely to be on campus) but by conceptualising the study in a much broader way – for example not limiting interest to students. For example, it will be of interest to gain an understanding of why members of the wider public will choose to utilise such services (in the absence of routing considerations). Secondly, by assuming homogeneity in service provision of bus services, although taking specific determinants of satisfaction such as routing into consideration (in the design of the questionnaire), the future possible impact of future direct competition from other bus operators was not taken into consideration in the current study. Such consideration will be imperative in future studies. The third limitation relates to the grouping of the variables (particularly in the service quality areas of the final SEM model). The attribute categorisation was driven by shared interpretation of meaning or definition by the authors. For future studies, a more ‘scientific’ approach to grouping is recommended to ensure greater model robustness. The final limitation relates to an acknowledgement that the study did not undertake any assessment of the respondents’ cognitive mode (process of thought), although the literature (see Amit & Sagiv 2013; Church 1997) does infer that perceptions are influenced by such mental modes. Thus, in reality, the study only captured the views of respondents that were explicitly expressed. Fisk and Haase (2011) highlight the importance of implicitly held perceptions in decision making. Again, this limitation presents considerable opportunities for future research directions.