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Since the publication of Van Raaij and Verhallen’s seminal work in European Journal of Marketing in 1994, identifying the domain-specific market segmentation approach as one of the most feasible for segmenting markets, there has been surprisingly limited development in this field, with the food domain as the only exception. This study aims to develop a methodological approach using latent class mixture modelling as contribution in the domain-specific market segmentation field.

This study captures the AIO lifestyle perspective using a domain-specific 80-item algorithm which has the wine (product) domain as its focus. A sample size of 811 consumers is used from data collected by means of the CATI approach.

The authors use four criteria for model selection: comparison of the Bayesian information criterion (BIC) statistic, comparison of classification error, verification of the interpretation of the derived segments and, finally, use of the conditional bootstrap procedure to test whether the selected model provides a significant improvement over the previous model. The five-segment model option yields a minimum BIC, the classification error measure is minimal and is easier to interpret than the other models. Segment descriptions for the five identified lifestyle-based segments are developed.

Segmentation by traditional k-means clustering has proven to be less useful than the more innovative alternative of mixture regression modelling; therefore, the authors identify segments in the market on the basis of individuals’ domain-specific lifestyle characteristics using a latent class mixture modelling approach.

Following the attainment of a clear and robust market segmentation structure, the simultaneous analysis of the lifestyles, demographics and behaviours of consumers as nexus of the domain-specific segmentation approach, provides rich and valid information accurately informing the market segment descriptions.

The authors make a substantive contribution by developing a methodological approach using latent class mixture modelling; the first of its kind in the area of domain-specific segmentation. Next, they use the discriminant and/or predictive validity of the 80-scale items to predict cluster membership using the WRL algorithm. Finally, the authors describe the identified market segments in detail and outline the practical implications.

From an early age we categorise the myriad stimuli we are confronted with. This adaptive process continues throughout our lives, and finds a natural expression in marketers’ desire to segment their consumers into different types. Argues that traditional attitudinal segmentations can prove disappointing when looking for differences between groups on behavioural or brand preference measures. Clusterwise regression, a form of latent class segmentation, offers an alternative approach to establishing meaningful market segments, and we present a case study using this technique within the health and beauty sector. Argues that the method’s emphasis on the importance of different attributes to different groups of people could be usefully incorporated into qualitative group methodology to produce meaningful and robust market segmentation.

Although social networking sites (SNS) are providing marketers a lot of information, it is also providing consumers with the ability to present their virtual identities, limiting the benefit of such information. The purpose of this paper is to understand how marketers can segment virtual consumers.

A review of the literature is first conducted. Followed by a survey method, data from 258 consumers were analyzed using a combination of scales including best-worst scaling. Classes and other demographics, behavioral and psychographic covariates were determined using latent-class analysis.

Findings show there exist three different segments based on values: self-conservers, social entertainers, and achievers. The results show how SNS consumers differ in their motivation to use social media, even when there is similarity in the uses (virtual behaviors) of SNS.

Analyzing behavior of virtual consumers can be limited by the fact that they are presenting their virtual identity. Psychographic metrics should be the focus of future research when dealing with online consumers, values and motivations provide a better way as they are more consistent than the virtual behavior.

Practitioners should look for more ways to integrate SNS segments with traditional segments, values-segmentation can aid in this. Additionally, practitioners should maximize the information access benefits of SNS by focussing also on underlying motives to certain behaviors on SNS.

This research value is derived from the fact that it is the first to perform values-segmentation on SNS. The results show that it is reliable and necessary when segmenting consumers on SNS.

Microfinance has become an important way to alleviate poverty. Though four decades have passed since its introduction, its impact is still not entirely clear. What makes it difficult to ascertain its efficacy is the existence of diverse types of microfinance organizations and client profiles. Microfinance institutions must primarily pay more attention to the client, and to the mechanism through which financial services are delivered. The purpose of this paper is to identify the profiles of microfinance customers and the features of their operations.

In this paper, multilevel latent class models were estimated to reveal clusters of operations and classes of clients.

The results show that there are six clusters of operations and four classes of clients in the market, each with distinct profiles and needs. Different strategies are recommended for each cluster and class.

Numerous studies have focused on the importance of getting to know the clients of microfinance programs, but none as yet have used market segmentation as a way to do so. The goal is to generate better strategies to help clients improve their business results. Applying market segmentation to the microfinance market may point to different products for different groups of clients, taking the real needs of each of them into account.

The purpose of this paper is to extend existing research on stakeholder orientation (SO) on higher education institutions (HEIs) by segmenting Spanish public universities based on their perceived levels of SO and describing the profiles of the different segments identified. Given that being stakeholder-oriented, as well as being market-oriented, is a matter of degree and not an absolute condition, the authors expect to find heterogeneity among public universities in terms of their levels of SO.

The authors designed a mail survey that was e-mailed to all managers of Spanish public universities, resulting in 795 usable questionnaires. A latent segmentation methodology was used to define the segmentation and profiling of the individuals.

Through latent class segmentation analysis, two clusters were identified: high stakeholder-oriented universities (HSOs) and low stakeholder-oriented universities (LSOs). Interestingly, HSOs universities perceive themselves to have greater organisational complexity and a lower level of reputation than LSOs, but that they are more active in terms of resource acquisition. LSOs universities are larger than HSOs and have a superior ranking.

This study will help university managers by providing useful information that will enable them to be more sensitive to the wants and needs of their stakeholders. It will also assist policy-makers who wish to support strategic decisions to promote differentiation among public universities, avoiding the mimetic effect.

Latent class segmentation can be a very useful technique to segment public universities based on their SO. Wherever heterogeneity exists, the latent class analysis will facilitate identification of the clusters of public universities and the profile of these different clusters according to their level of SO (either responsive or proactive).

The purpose of this paper is to obtain empirical understanding of organic food buyers in the context of emergent organic food market (i.e. Bosnia and Herzegovina) by using a segmentation approach.

A self-administrated online survey was carried out among organic food buyers (n=202) using the snowball sampling technique. Measurement items were mainly adapted from the prior studies.

The authors analysed the heterogeneity of organic food buyers with latent class model. Four distinct latent classes (i.e. segments) of organic food buyers were identified. Those segments were named as enthusiastic social-seekers, enthusiastic moralists, hostile seldom shoppers, and hostile heavy shoppers.

Though the study was exploratory, the identified segments of organic food buyers can enhance our knowledge about differing characteristics of organic food buyers in the context of the country where the organic food industry is in the early stages of development. The findings of this study will give organic food producers and marketers a much better framework for making product, pricing, distribution and marketing communications decision. Moreover, the identification of organic food consumer profiles will provide an insight into how policymakers should tailor their public policy and strategies to expand the size of the organic food market.

The purpose of this paper is to propose the study of the adoption and use of e-banking by adults over the age of 50, from the theoretical framework based on the unified theory of acceptance and use of technology.

After proving the validity of the model, the authors analyse the heterogeneity of these persons by using a structural model-based latent class segmentation called partial least squares prediction-oriented segmentation.

The results enable the identifying of four segments with different behaviours with respect to this electronic service. As implications of the results obtained, it is observed how sociodemographic characteristics do not explain their differences. However, the psychographic as well as the cognitive age, self-confidence and audacity are discriminating and explanatory criteria for the behaviour of the four segments.

From the academic point of view, this paper offers important contributions to the prior literature. First, although there is an extensive literature on the use of internet banking, the authors have nevertheless found a clear lack of research studies related to the specific group of older adults. There are not many works that exclusively deal with the elderly’s acceptation and use of technologies, and even less so in the case of internet banking services. Also, if the elderly are focussed, then it is discovered that there is a socially shared stereotype that sees them as inexpert and cut off from technology. This is, however, an unfair view that conceals a broad diversity of behaviours as has been proved for the different segments found.

The purpose of this paper is to understand how consumers may be segmented with respect to their reactions to social network marketing.

Consumers are segmented on the basis of attitudes toward social network marketing and the association among psychological, economic, and socio-demographic covariates are explored using data from 883 consumers and latent-class analysis.

A total of five segments are identified – Passive, Talkers, Hesitant, Active, and Averse – along with significant covariates, such as information search, convenience, entertainment, age and gender that predict membership.

Evidence was found of two segments that are highly impacted by social network marketing in terms of brand engagement, purchase intention and WOM referral intention. The most engaged – the Active – representing approximately 10 percent, is most open to interacting with brands in social networks, likely to make a purchase as a result of the campaign, and likely to spread WOM. The second group – Talkers – representing 28 percent, is also high on all outcomes, but not impacted as greatly in terms of purchase intentions.

There is a sizable share of the market (38 percent) that can be positively impacted through social network marketing. This paper reinforces that it is problematic to collapse all users of social networks.

The paper offers a more nuanced understanding of how consumers engage with social media by focusing on how consumers engage with social network marketing and by employing three segmentation bases: brand engagement, purchase intention, and WOM.

Cyber-attacks on hotel information systems could threaten the privacy of consumers and the integrity of the data they exchange upon connecting their mobile devices to hotel networks. As the perceived cyber-security risk may be reflected heterogeneously within the US consumer population traveling internationally, the purpose of this study is to examine such heterogeneity to uncover classes of US consumers based on their perceptions of risk of using tablets for various tasks when staying in hotels abroad.

Using data collected from 1,016 US consumers who stayed in hotels abroad, this study used latent profile analysis (LPA) to classify the consumers based on their perceptions of risk associated with several tablet use behaviors in hotels.

The analysis uncovered four latent classes and produced a characterization of these classes according to several common behavioral (frequency of travel, the continent of the destination, duration of stay and purpose of travel) and demographic (gender, age, income and education) consumer characteristics.

Being the first study that classifies consumers based on the risk of using tablets in hotels while traveling internationally, this study brings the following contributions: offers a methodology of classifying (segmenting) consumer markets based on their cyber-security risk perceptions, uses LPA, which provides opportunities for an accurate and generalizable characterization of multivariate data that comprehensively illustrate consumer behavior and broadens the perspective offered by the current literature by focusing on consumers who travel from their US residence location to international destinations.

酒店信息系统的网络攻击可能会泄露消费者隐私以及其通过酒店网络连接移动网络而进行的数据交换。由于美国消费者在出国旅游中对于网络安全危险的感知因人而异，因此，本论文旨在研究这种个人差异性，对美国消费者在出国旅游居住酒店中使用平面电脑的危险感知，进行特点分类。

本论文样本为居住在国外酒店的1,016名美国消费者。本论文采用潜在剖面分析（LPA），对其使用酒店平板电脑的危险感知进行分类。

本论文研究结果归纳四种潜在类别，以及根据集中常见行为变量（旅游频率、旅游目的地所在的洲、酒店居住时长、旅游目的地）和统计人口信息（性别、年龄、收入、和教育），进行消费者特点归纳。

本论文是首篇基于消费者出国旅游时使用酒店平板电脑的危险感知而进消费者分类的文章。因此，本论文结果有以下贡献：（1）贡献一种分类研究方法（市场细分），基于网络安全危险感知而划分;（2）使用潜在剖面分析方法，为多变量数据分析消费者行为，产出精确而概括特性的研究，提供机会;（3）对现有文献对国际旅游的美国消费者的研究拓宽了视野。

网络安全危险、平板电脑、潜在剖面分析、酒店、国际旅游

The purpose of this paper is to derive monetary benchmarks and managerial implications for omni-channel retailers’ B2C e-fulfillment strategies by investigating the trade-offs between lead time, delivery convenience and total price including shipment in the context of online electronics retailing.

Based on a choice-based conjoint analysis among 550 US online shoppers, the monetary values of lead time and convenience were calculated in a log-log regression model. In addition, latent class segmentation was applied to identify consumer segments according to their differing e-fulfillment preferences.

From a consumer perspective, the analysis suggests that price is the most important criteria in omni-channel retailer selection, followed by lead time and convenience. The value of time is, on average, $3.61 per day. Regarding convenience, the results indicate that delivery to the home is highly preferred over pick-up options. The value of the consumer’s travel time was estimated at $10.62 per hour. The latent class segmentation identified four segment groups with different preferences.

To validate the findings, future research could analyze real data from omni-channel retailers’ customers’ buying behavior. It should also be interesting to extend the research to other price ranges, market segments and e-fulfillment factors, such as return options, shop ratings and membership programs aiming for further generalization.

The findings guide omni-channel retailers to focus on efficient B2C e-fulfillment strategies. Considerable competitive advantages may be gained by reducing lead times and offering convenient delivery in line with the lead time valuation of the identified customer segment.

This study fills gaps in the academic research of consumer behavior in retailer selection, which has primarily concentrated on the choice between “brick-and-mortar” and online sales channels. It paves the way for a more service-oriented perspective in omni-channel retailing research.