Search results

Several inferential procedures are advocated in the literature. The most commonly used techniques are the frequentist and the Bayesian inferential procedures. Bayesian methods afford inferences based on small data sets and are especially useful in studies with limited data availability. Bayesian approaches also help incorporate prior knowledge, especially subjective knowledge, into predictions. Considering the increasing difficulty in data acquisition, the application of Bayesian techniques can be hugely beneficial to managers, especially in analysing limited data situations like a study of expert opinion. Another factor constraining the broader application of Bayesian statistics in business was computational power requirements and the availability of appropriate analytical tools. However, with the increase in computational power, connectivity and the development of appropriate software programmes, Bayesian applications have become more attractive. This chapter attempts to unravel the applications of the Bayesian inferential procedure in marketing management.

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.

This study aims to reveal consumer purchase intentions for Software-as-a-Service (SaaS) lifetime deals and the role of service offerings in shaping these intentions.

Lifetime deals − an aggressive market penetration strategy − have the potential to allow startups to gain market share, user base and the cash necessary for growth. However, startups need to mitigate consumer concerns for which service offering design plays a key role. Drawing on expectancy-value and signaling theories, this study developed a research model and then conducted empirical research on 2,173 consumers via choice-based conjoint analysis to reveal the critical service offering attributes for consumer utility in lifetime deals in the SaaS presentation tool market context. After using the hierarchical Bayes model to derive each respondent’s part-worth utilities for service offering attributes, the hypotheses were tested via the factor score regression method.

The results show that the service offering attributes of low price, refund option, human support and feature updates enhance consumer utility in SaaS lifetime deals. Three of these four attributes, namely, low price, refund option and feature updates, enhance consumers' purchase intentions by reducing their concerns about the service’s performance, seller and lifespan, respectively.

This study elucidates consumer purchase intentions for SaaS services in digital marketplaces. By investigating a widespread market entry strategy − lifetime deals − it shows consumer preferences and behavior for these deals in the fast-growing online tools market. This study also shows how startups can use lifetime deals through a well-designed service offering to mitigate various consumer concerns.

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.