SERVICE-PROCESS CONFIGURATIONS IN ELECTRONIC RETAILING: A TAXONOMIC ANALYSIS OF ELECTRONIC FOOD RETAILERS

Service-processes of electronic retailers are founded on electronic technologies that provide flexibility to sense and respond online to the dynamic and complex needs of customers. In this paper, we develop a taxonomy of service-processes in electronic retailing and demonstrate their linkage to customer satisfaction and customer loyalty. The taxonomy is grounded in a conceptual classification scheme that differentiates service-process stages on a continuum of flexibility. Using data on electronic service-processes collected from 255 electronic food retailers, we identified eight configurations for the taxonomy. We also collected and analyzed publicly reported customer satisfaction survey data that were available for 52 electronic food retailers in the study sample. The results of this analysis indicate positive and significant correlation of the ordering of the taxonomy configurations with (i) customer satisfaction with product information, product selection, web site aesthetics, web site navigation, customer support, and ease of return, and (ii) customer loyalty. Taken together, the results of our empirical analyses demonstrate that the taxonomy captures information and variety within and across the electronic service-process configurations in ways that can be related to customer satisfaction and customer loyalty.Marketing, Research and Development/Tech Change/Emerging Technologies,

Information Technology Applications in Hospitality and Tourism: A Review of Publications from 2005 to 2007

The tourism and hospitality industries have widely adopted information technology (IT) to reduce costs, enhance operational efficiency, and most importantly to improve service quality and customer experience. This article offers a comprehensive review of articles that were published in 57 tourism and hospitality research journals from 2005 to 2007. Grouping the findings into the categories of consumers, technologies, and suppliers, the article sheds light on the evolution of IT applications in the tourism and hospitality industries. The article demonstrates that IT is increasingly becoming critical for the competitive operations of the tourism and hospitality organizations as well as for managing the distribution and marketing of organizations on a global scale

The Price of Privacy - An Evaluation of the Economic Value of Collecting Clickstream Data

The analysis of clickstream data facilitates the understanding and prediction of customer behavior in e-commerce. Companies can leverage such data to increase revenue. For customers and website users, on the other hand, the collection of behavioral data entails privacy invasion. The objective of the paper is to shed light on the trade-off between privacy and the business value of cus- tomer information. To that end, the authors review approaches to convert clickstream data into behavioral traits, which we call clickstream features, and propose a categorization of these features according to the potential threat they pose to user privacy. The authors then examine the extent to which different categories of clickstream features facilitate predictions of online user shopping pat- terns and approximate the marginal utility of using more privacy adverse information in behavioral prediction models. Thus, the paper links the literature on user privacy to that on e-commerce analytics and takes a step toward an economic analysis of privacy costs and benefits. In par- ticular, the results of empirical experimentation with large real-world e-commerce data suggest that the inclusion of short-term customer behavior based on session-related information leads to large gains in predictive accuracy and business performance, while storing and aggregating usage behavior over longer horizons has comparably less value

STATE OF SHOPPING AND THE VALUE OF INFORMATION: INSIGHTS FROM THE CLICKSTREAM

A critical challenge for online retailers is to determine what types of product and price information are best suited to influence online conversion. While it has long been known that customers differ in their state of shopping, it is cumbersome to learn about such latent differences offline. The availability of clickstream data however helps us in identifying meaningful segments of sessions on the basis of customers’ online behaviors. We examine whether product and price information had different impacts on customers belonging to three states of shopping, and also assess the effect on outcomes within a session and across sessions. Our results question the practice of offering price promotions to all customers of a store, and highlight the value of product information in increasing loyalty for some customers. Depending on the retailer’s goal– short term conversion versus longer-term customer relationship–a different information provision strategy is likely to be optimal

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

Can Visible Cues of Search Results Tell Vendors\u27 Reliability?

A search engine provides two distinct types of results, organic and paid, each of which uses different mechanisms for selecting and ranking relevant Web pages for a query. For an e-commerce query, vendors represented by websites in these organic and paid results are expected to have varying reliability ratings, such as a satisfactory or unsatisfactory score from the Business Bureau (BBB) based on overall customer experiences. In this paper we empirically examine how vendors’ reliability ratings in BBB are associated with cues (such as type of result, relative price, number of sites selling the product) that can be observed or derived from search results, and further we attempt to predict vendors’ BBB reliability ratings using those cues

Clicks to conversion: the value of product information and price incentives

Journal ArticleThis study uses clickstream data obtained from a large online durable goods retailer to examine how different types of information - product-related and price-related information provided by retailers - impact purchase-related outcomes for consumers. Using mixture-modeling techniques to analyze latent differences among customers, we find that consumers fall under three distinct categories - directed shoppers, deliberating researchers and browsers. In examining the impacts of information on purchase outcomes, we find that product and price-related information impacts consumers in these three shopping states differently. While product information highlighting features of product alternatives in a category has the strongest impact on deliberating researchers, specific price incentives related to category-level discounts increases the likelihood of purchase for both directed shoppers as well as browsers. Price incentives relating to site-wide free shipping have a positive impact on purchase for all consumers. Surprisingly, category-level discounts have a negative impact on deliberating researchers, while rich product information hampers the purchase process of directed shoppers. We discuss the managerial implications of our findings and the role of clickstream analytics in designing dynamic targeting and information provisioning strategies for online retailers

Big Data and the Transformation of Operations Models: A Framework and A New Research Agenda

Big Data has been hailed as the ‘next big thing’ to drive business value, transform organisations and industries, and “reveal secrets to those with the humility, willingness and tools to listen” (Mayer-Schönberger and Cukier, 2013: 5). However, despite growing interest from organisations across industry sectors, Big Data applications appear to have concentrated on delivering incremental change and operational efficiency improvements, with little evidence on using Big Data to facilitate strategic, transformational change. In this paper, we explore how Big Data is actually being can be used across different sectors in leading organisations and examine the ways in which it is fostering change in the core operations models of organisations. A definition of ‘operations model’ is developed and the core components dimensions of an operations model are then examined, namely capacity, supply network, process and technology, and people development and organisation. Through a series of case studies, we examine the role of Big Data in affecting some, or all, of these components dimensions in order to generate value for the organisation by optimising, adapting or radically transforming the operations model. Following our analysis, we develop a tentative framework which can be used both for understanding how Big Data affects operations models, and for planning changes in operations models through Big Data. We set out a new research agenda to systematically understand the full potential of Big Data in transforming operations models