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Customer value has recently become a primary focus among many strategy researchers and practitioners as an essential element of a firm's competitive strategy. Many firms are engaged in some form of customer value analysis (CVA), which involves a structural analysis of the antecedent factors of perceived value (i.e. perceived quality and perceived price) to assess their relative importance in the perceptions of their buyers. Previous CVA research has focused upon using aggregate market or market segment level analyses. The purpose of this paper is to expose the limitations of implementing CVA on either an aggregate or market segment level basis, and propose an alternative individual level approach.

The paper develops an extended hierarchical Bayesian approach for cross‐sectional data with one observation per response unit, which allows for estimation at the individual firm level to make CVA more useful. This paper demonstrates the utility of the proposed Bayesian methodology involving a CVA study conducted for a large electric utility company. It also compares the empirical results from aggregate, market segment, and the proposed individual level analyses, and show how traditional approaches mask underlying price and quality importance.

Marketing and management strategy researchers need to exhibit care when conducting such CVA analyses as underlying heterogeneity can be masked when aggregate market or segment level analyses are conducted.

This paper provides a new hierarchical Bayes recursive simultaneous model formulation for CVA analyses to provide individual level insights with cross‐sectional data.

The purpose of this research is to test for price threshold effects in the demand for high-involvement services for small businesses.

The authors use a stated preference choice-based conjoint study of small business telecommunications demand. Using survey data, individual-level parameter estimates for a demand model are achieved via the Hierarchical Bayes method of estimation.

For demand for small business telecommunications services, the authors find very strong positive impacts of nine-ending and zero-ending prices on the demand for a common bundle of telecommunications services (wired telephone service, broadband internet, and cellular telephone service), even at prices so high a shift in the left-most digit does not occur.

The advertising, brand, or product manager or statistician who assumes threshold effects are not extant in high-involvement service demand may find conventional demand estimation methods lead to erroneous conclusions and less effective pricing strategies.

In the statistical literature on price-ending effects on product demand, most products for which demand is modelled are low-involvement consumer products priced at less than ten monetary units per unit of product. There is a lacuna in this price-ending effects literature regarding small businesses and high-involvement services offered at three-digit prices via monthly subscription. This research indicates that testing for threshold effects should be de rigeur in the methodology of demand estimation for telecommunications or other high-involvement services.

This paper aims to investigate to what extent sustainable and nonsustainable attributes can be used to characterize different clusters of consumers in an emerging market, where economic conditions can increase the relevance of price. Consumers seem reluctant to engage frequently in pro-sustainable behavior, mainly for financial reasons. However, purchasing decisions can be understood as a multidimensional process.

The authors conducted quantitative and descriptive research employing a choice-based conjoint/hierarchical Bayes (CBC/HB) experiment in malls in a low-income city in northeast Brazil with 1,287 potential buyers of denim jeans. The conjoint analysis therefore collected data on preferences in the course of actual decision-making. The authors then took the individual part-utility from each respondent and ran a cluster analysis to identify similar groups in the sample. The classification and regression tree (CART) method was used to determine the relationship between the conjoint attributes and the sociodemographic characteristics.

The data demonstrate that buying decisions constitute a complex process of interplay between many different factors, often involving trade-offs between a wide variety of nonsustainable and sustainable attributes. The survey confirmed that price is still of paramount importance when it comes to consumer choices. The authors also found that sustainable attributes played a relatively more significant role than brand or origin of production. The authors identify notable differences between groups of consumers in the “pro-sustainable” and “non-pro-sustainable” clusters and different levels of importance regarding the sociodemographic characteristics.

Although price emerged as the most significant attribute, the research also demonstrates that there is a market in Brazil for products and practices based on a genuine commitment to the natural environment and social issues. The findings suggest that marketing managers and policymakers should consider different combinations of concerns over sustainability with product attributes and include sociodemographic variables rather than considering the textile market as uniform or thinking that there is no space for sustainability in fashion.

Traditional methods of capturing and determining logistics attribute importance have serious research limitations. The purpose of this paper is to introduce maximum difference (MD) scaling as a new research methodology that will improve validity in measuring logistics attribute importance, overcoming many of the limitations associated with traditional methods. In addition, this new research method will allow logistics researchers to identify meaningful need‐based segments, an important goal of logistics research.

This paper provides an overview of MD scaling along with important research advantages, limitations, and practical applications. Additionally, a detailed research process is put forth so that this technique can be implemented by logistics researchers. Finally, an application of this technique is presented to illustrate the research method.

The importance of truck driver satisfaction attributes was analyzed using bivariate correlation analysis as well as MD scaling analysis. The two sets of results are compared and contrasted. The resulting rank order of attributes is very different and MD scaling results are shown to possess important advantages. As a result of this analysis, MD scaling analysis allows for meaningful, need‐based segmentation analysis, resulting in two unique need‐based driver segments.

From a practitioner viewpoint, knowing which attributes are most important will help in investing scarce resources to improve decision making and raise a firm's ROI. Although a number of relevant applications exist, the most important may include examining: the importance of customer service attributes; the importance of logistics service quality attributes; and the importance of customer satisfaction attributes.

MD scaling is a relatively new research technique, a technique that has yet to be utilized or even explored in existing logistics and supply chain literature. Yet, evidence is mounting in other fields that suggest this technique has many important and unique advantages. This paper is the first overview, discussion, and application of this technique for logistics and supply chain management and creates a strong foundation for implementing MD scaling in future logistics and supply chain management research.

Managers seeking to manage customer word-of-mouth (WOM) behavior need to understand how different attitudinal drivers (e.g. satisfaction, positive and negative emotion, commitment, and self-brand connection) relate to a range of WOM behaviors. They also need to know how the effects of these drivers are moderated by customer characteristics (e.g. gender, age, income, country). The paper aims to discuss these issues.

To investigate these issues a built a large-scale multi-national database was created that includes attitudinal drivers, customer characteristics, and a full range of WOM behaviors, involving both the sending and receiving of both positive and negative WOM, with both strong and weak ties. The combination of sending-receiving, positive-negative and strong ties-weak ties results in a typology of eight distinct WOM behaviors. The investigation explores the drivers of those behaviors, and their moderators, using a hierarchical Bayes model in which all WOM behaviors are simultaneously modeled.

Among the many important findings uncovered are: the most effective way to drive all positive WOM behaviors is through maximizing affective commitment and positive emotions; minimizing negative emotions and ensuring that customers are satisfied lowers all negative WOM behaviors; all other attitudinal drivers have lower or even mixed effects on the different WOM behaviors; and customer characteristics can have a surprisingly large impact on how attitudes affect different WOM behaviors.

These findings have important managerial implications for promotion (which attitudes should be stimulated to produce the desired WOM behavior) and segmentation (how should marketing efforts change, based on segments defined by customer characteristics).

This research points to the myriad of factors that enhance positive and reduce negative word-of-mouth, and the importance of accounting for customer heterogeneity in assessing the likely impact of attitudinal drivers on word-of-mouth behaviors.

Fungus-resistant grape varieties (FRGVs) are the key to more environment-friendly wine growing. This paper aims to examine whether German consumers are willing to buy environment-friendly wines. The study focuses on reducing the amount of fungicides applied and the improvement of the carbon footprint, which are both related to the FRGVs . Furthermore, a cluster analysis leads to more insights into the consumer groups that are open to environment-friendly wine.

A choice experiment was conducted among 1,500 German wine drinkers with the following attributes: “reduction of pesticides”, “reduction of carbon emissions”,“familiarity with the grape variety”,“organic certification”, the slogan “better for the environment” and“price”. The individual-level, part-worth utilities were estimated using the Hierarchical Bayes method. The Ward’s method was used to cluster the individual-level, part-worth utilities. The participants’ wine involvement and environmentalism are used to further analyse the sample.

The most important attribute is “price”, followed by the “familiarity with the grape variety” and the “reduction of pesticides” and of “carbon emissions”. The least important attribute is “better for the environment”. The cluster analysis results in three clusters: the green-minded, the traditionalists and the price-minded.

The insights about the consumer acceptance of environment-friendly wines can be used to market these wines more effectively to consumers.

This is the first study that combines a choice experiment with attributes that are derived from the benefits of fungus-resistant grape varieties.

To examine whether the country of origin (COO) effect actually exists in an e-commerce context, the authors intend to contribute to the ongoing debate by measuring the COO effect through a series of connected studies.

Drawing on cue utilization theory, the authors emphasize the urge to investigate the COO effect in multiple cue settings in order to reveal a more realistic picture of its actual effect size. In contrast to most prior research, which often does not analyze COO using methodological plurality and neglects important contextual factors, the authors employed a four-staged research design in an attempt to trigger and measure the COO’s implicit effect size in today’s pervasive context of online shopping. The importance of brands (inhering the COO) is decompositionally calculated relative to other extrinsic cues by applying a Hierarchical Bayes estimation, with the COO impact being extracted subsequently.

The results deepen concerns that the COO effect actually does not exist, particularly in the more contemporary context of online shopping. Specifically, preferences for previously favored German products faded when controlling for brand attitude for both high-involvement (p = 0.003) and low-involvement products (p = 0.024).

The study focused on consumers of Generation Y, as they represent one of the most important segments in online shopping. Findings might be replicated for other consumer generations. The study focused on Chinese consumers, as the Chinese e-commerce market represents the world’s largest one. Future studies might investigate other markets.

As brands, rather than a COO effect, impacted consumer preferences, companies selling their products to Chinese consumers online need to establish a reputation for quality early on. Chinese companies should emphasize their COO to make use of the ethnocentrism detected. Companies profit from the Best-Worst Scaling investigation revealing which product categories Chinese consumers most preferably buy online from German companies.

To the best of the authors’ knowledge, this study is the first to capture the importance of COO in the contemporary context of ubiquitous online shopping. Moreover, a more realistic and less biased way of measuring the importance of COO is enabled by building upon three pre-connected studies. The findings allow to develop a generalization for both high- and low-involvement products.

Applying the institutional logics perspective to applicant attraction, this study investigates the level of uniformity among preferences for consulting job attributes associated with the institutional logics of the corporation, the profession and the family, and tests for the influence of anticipatory socialization differences.

The study uses a discrete choice experiment with 232 business students. A hierarchical Bayes approach to conjoint analysis uncovers part-worth heterogeneity and allows for subsequent cluster and regression analysis of the choice data.

The findings identify a dominant job-oriented preference type and a minor career-oriented preference type. Anticipatory socialization through personal prior work experience and the occupation of friends decreases adherence to the logic of profession and increases the relevance of the family logic. The parents' occupation has only a minimal influence on preferences.

The study provides attribute-based recommendations on how professional service firms can effectively address the complex expectations of potential applicants in their job ads for an entry position and underlines the role of intra-generational reference groups as important anticipatory socializers.

By testing individual socialization effects at the pre-hire stage and beyond the organizational level, the study fills a void in both the recruitment and the institutional literature.

The purpose of this paper is to explore the differences in preferred supplier choice criteria between food purchasing agents who focus on supplier security and those that do not. Specifically, this research determines the relationship between purchasing agents’ supplier security preferences and their preferences for product quality, delivery reliability, price, and supplier location. The influence of international sourcing on demand for increased supplier security is also explored.

Choice-based conjoint analysis with hierarchical Bayes (HB) estimation and t-tests are used to assess and compare the utility food purchasing managers derive from different supplier attributes.

Purchasing managers that place a higher priority on security when choosing suppliers were willing to pay suppliers a higher price and receive lower levels of delivery reliability in return for higher security but placed less emphasis on suppliers’ product quality. Firms that source internationally do not have a significantly greater preference for advanced supplier security. However, purchasing managers that value supplier security were more likely to source internationally, potentially indicating that security allows for global sourcing by mitigating the increased vulnerability inherent to sourcing abroad.

This research was limited by its focus on the food industry and a relatively small sample size.

This work illustrated that food purchasing managers can be segmented by the emphasis they place on security. Food industry managers will find results useful in formulating their future service offerings with respect to security and other supplier choice criteria.

This is one of few works investigating security as a supplier choice criterion and utilizing HB estimation of choice-based conjoint data.

The purpose of the study was to examine the relative importance of corporate social and environmental responsibility (CSER) in comparison to standard, price, duration, destination, brand and disruption using choice-based conjoint analysis (CBC).

CBC was used as the data collection survey technique, and counts analysis for preference and hierarchical Bayes estimation (HB) for importance levels data analysis methods, from Sawtooth Software Inc.

Results show that 2:1 Royal Caribbean Cruise Line cruise consumers prefer companies with CSER policies and practices. However, their actual product choice selection of cruise package attributes revealed that consumers overall placed less importance on CSER when choosing cruises. Experienced consumers were more brand image-conscious than those new to cruising, and consumers who were less price-sensitive were most willing to choose companies with CSER policies and practices.

The information provided is specifically on “what” cruise consumer preferences and importance attributes are but does not explicitly explain “why” the respondents made the choices they did. This was at the time a limitation of the software used to conduct the study.

The Conjoint Analysis CBC Sawtooth Software pre-2014 version choice simulators do not facilitate questions that provide answers as to “why” respondents make the choices they do in the market simulations.

The knowledge contribution is of value to both academia and industry, as the quantitative statistical data on the cruise consumers’ choice preferences are of value in understanding and identifying solutions/approaches towards “opening the bottleneck” that exists between private sector sustainable development practices and consumer lifestyle changes.

This was the first time that CBC/HB was applied within academia to examine the cruise consumers’ choice preferences in a UK context and also the first time that CSER was applied as a direct variable in a cruise package to determine the preference and important values of a brand in a consumer behaviour decision-making context.