Search results

This chapter reviews research on the question–behavior effect, the phenomenon that asking questions influences respondents’ behavior. Two distinct research streams, the self-prophecy effect, concerned with socially normative behaviors, and the mere measurement effect, dealing with purchase behaviors without socially normative significance, are identified. Despite the recent attempt at integration, it is argued that there are fundamental differences between the two effects. Distinctions are also drawn between lab-based and field-based mere measurement effects, and between normatively consistent and implicit attitude-driven, normatively inconsistent self-prophecy effects. Key studies, theoretical explanations, and moderators of each effect are discussed, potential unanswered questions and research opportunities are identified, and significant managerial and policy implications are highlighted.

The dominant worldview among marketers is one of technology optimism, which holds that technological advances influence consumers and businesses in positive ways. In direct contrast to this perspective, I advance the thesis that at the organizational frontlines where marketers interact with consumers by observing, informing, persuading, negotiating and co-creating with, and entertaining them, technology commonly produces unforeseen and unexpected effects on consumers with significant negative implications for marketers. The result is Adverse Technology-Consumer Interactions (ATCIs). Marketing practitioners play an instrumental role in producing and exacerbating ATCIs. Yet, I argue they have few incentives to fully investigate the underlying reasons, understand their scope, or find solutions to these potentially troublesome phenomena. Academic researchers, however, are uniquely poised to identify ATCIs, investigate them in depth by considering their industry-wide and society-wide import, develop appropriate theoretical frameworks, and design and test solutions to alleviate their effects. I develop these ideas by considering two ATCIs, falling response rates to customer surveys and customer reactance to frequent price changes. I also point out promising research opportunities for both these phenomena.

The concept and practice of e-services has become essential in business transactions. Yet there are still many organizations that have not developed e-services optimally. This is especially relevant in the context of Indonesian Airline companies. Therefore, many airline customers in Indonesia are still in doubt about it, or even do not use it. To fill this gap, this study attempts to develop a model for e-services adoption and empirically examines the factors influencing the airlines customers in Indonesia in using e-services offered by the Indonesian airline companies. Taking six Indonesian airline companies as a case example, the study investigated the antecedents of e-services usage of Indonesian airlines. This study further examined the impacts of motivation on customers in using e-services in the Indonesian context. Another important aim of this study was to investigate how ages, experiences and geographical areas moderate effects of e-services usage.

The study adopts a positivist research paradigm with a two-phase sequential mixed method design involving qualitative and quantitative approaches. An initial research model was first developed based on an extensive literature review, by combining acceptance and use of information technology theories, expectancy theory and the inter-organizational system motivation models. A qualitative field study via semi-structured interviews was then conducted to explore the present state among 15 respondents. The results of the interviews were analysed using content analysis yielding the final model of e-services usage. Eighteen antecedent factors hypotheses and three moderating factors hypotheses and 52-item questionnaire were developed. A focus group discussion of five respondents and a pilot study of 59 respondents resulted in final version of the questionnaire.

In the second phase, the main survey was conducted nationally to collect the research data among Indonesian airline customers who had already used Indonesian airline e-services. A total of 819 valid questionnaires were obtained. The data was then analysed using a partial least square (PLS) based structural equation modelling (SEM) technique to produce the contributions of links in the e-services model (22% of all the variances in e-services usage, 37.8% in intention to use, 46.6% in motivation, 39.2% in outcome expectancy, and 37.7% in effort expectancy). Meanwhile, path coefficients and t-values demonstrated various different influences of antecedent factors towards e-services usage. Additionally, a multi-group analysis based on PLS is employed with mixed results. In the final findings, 14 hypotheses were supported and 7 hypotheses were not supported.

The major findings of this study have confirmed that motivation has the strongest contribution in e-services usage. In addition, motivation affects e-services usage both directly and indirectly through intention-to-use. This study provides contributions to the existing knowledge of e-services models, and practical applications of IT usage. Most importantly, an understanding of antecedents of e-services adoption will provide guidelines for stakeholders in developing better e-services and strategies in order to promote and encourage more customers to use e-services. Finally, the accomplishment of this study can be expanded through possible adaptations in other industries and other geographical contexts.

More than ever before, public transit must compete in the transport market. This competition is, on the one hand, against steadily increasing car traffic; and on the other hand, between public transit operators. This, in turn, leads to new demands regarding the type, content and quality of data needed for planning and management. Frequently, traditional travel behaviour surveys do not provide sufficiently accurate and detailed information about public transit demand. To plan public transit, frequently a precise description of all trip stages, including the first and the last mile, is necessary. To achieve this, an adaptation of the traditional survey methods is necessary. In many countries, public transit associations have been established to integrate services offered by individual public transit operators with the help of through-ticketing and a coordination of lines and timetables into what looks, to the user, like a single system. To distribute revenue among the operators involved, detailed surveys of passengers are needed. Measuring the quality of public transit service and surveying customer satisfaction are new tasks. Such data are the basis for quality assurance and are essential for gaining and keeping customers of the public transit system. New technologies such as the Global Positioning System, automated passenger counts and Smart Card Payment Systems offer new possibilities to collect data more efficiently and cost-effectively. This article covers essential aspects of surveys and the collection of data that are crucial for the planning and management of public transit; it points to state-of-the-art methods and offers potential solutions.

Customer experience management is the managing of customer interactions, feelings and emotions at every touchpoint. These interactions and feelings define the customer's future behaviour and perceptions about the offering. Companies can use these touchpoint interactions as a source of competitive advantage. Companies can embark on a journey of experience management by understanding needs and insights about customer's behaviour. These are gathered through interactions, observations and structured surveys. Such feedback from customers is called Voice of Customer (VoC). Another source of understanding customers is employees who are involved in these interactions and also product and service delivery. Understanding employees is equally important. Companies need to even collate their feedback regarding problems in delivery and servicing, customers' expectations versus perceptions. Organisations committed to creating superior customer experience invest in tools like surveys, employee interactions both in formal and informal context to gather voice of the employee (VoE). Finally, both VoC and VoE have to be evaluated in a business context to define processes and measure the effectiveness of these processes. The voice of the process or VoP helps to understand the difference between customer perceptions and process performance. It gives an estimate of error and suggests methods for improving process efficiency.

This chapter explains the three essential ingredients that go into experience management, the tools used to collect information and how can these be used to design a superior and fulfilling experience for customers.

This study used confirmatory factor analysis to analyze the secondary data resulting from a service quality survey conducted by a large public library. The library outsourced the development of this survey, which was founded on the well-recognized SERVQUAL and LibQUAL+ service quality models. Applying structural equation modeling and recognized fit indexes to the secondary data, this study determined that the library model did not fit the data and that the data itself were neither reliable nor valid. This study developed a nine-step process for implementing the SERVQUAL model that enables the data derived from SERVQUAL-type implementations to provide superior information for decision making.

Strategy Question: How do we effectively communicate our brand to our customers and our market?

Summary: Many firms have a sense of the organization’s “brand.” We define brand as “the promise of an experience” to the customers. Firms need to identify the elements of the customer experience that are valued by the customer and determine the brand experience the organization wishes to convey is consistent with the experience of the customer. This tool uses a simple survey process methodology to determine whether the brand experience the organization wishes to convey is consistent with the experience of the customer. A simple X/Y chart is used to array the firm’s offerings based upon the customer’s perception of the brand experience. Comparisons with competitive firms can be included in the analysis. The results provide insight into the effectiveness of promotional and communication efforts intended to develop brand awareness among customers.

Attitudes, perceptions, and intentions of a firm's customers, which can be captured via customer feedback metrics (CFMs), provide valuable information about the state of a firm's customer base. CFMs can help capture the impact of marketing actions on future customer behavior and future firm performance, and thus can help make marketing become more accountable. CFMs have received much attention in marketing research and business practice since the 1970s. In this chapter, we provide a short historical overview of the development of, and research about, CFMs, we classify the different types of CFMs, we highlight the empirical findings of the drivers and consequences of CFMs, and we explore how CFMs can be integrated in a firm's customer dashboard in order to make marketing more accountable. We furthermore explore some of the challenges in accurately measuring CFMs, and in the end of this chapter, we provide information on how to capture CFMs in the age of social listening via modern tools involving text-, voice-, and video-mining.

Industry publications abound with tips on how to create and nurture customer evangelism. Scholarly publications note the effects of evangelism to firms. Consultants promote evangelism creation as part of their skill set. Yet the existence customer evangelism and its effects remain unsupported by empirical evidence. The purpose of this paper is to quantitatively explore customer evangelism.

This paper takes one of the first steps towards empirical analysis of customer evangelism by using a formative composite latent variable model to identify customer evangelists from a survey population. The authors then compare customer evangelists against non-customer evangelists on key characteristics, as per the claims in the qualitative literature, to verify the accuracy of the selection model.

The analysis demonstrates that key claims in the qualitative literature in regard to customer evangelists are supported by quantitative data in this study, namely that customer evangelists are focused on authenticity, cultishness and sharing knowledge, and have a deep emotional and spiritual connection to the brand. They also have higher intentions to purchase the product in future than do non-customer evangelists. However, other claims in the qualitative literature – such as that customer evangelists are more socially oriented, knowledge-seeking, experientially oriented or idealistic than are non-customer evangelists – are not supported by the data in this study, or are inconclusive.

This study is one of the first to attempt to empirically identify customer evangelists, and is part of a movement to study consumer religiosity in an empirical context. This study paves the way for further empirical research into customer evangelism, consumer religiosity and consumer collectivism.