Search results

Focussing on behaviors and attitudes of casual dining patrons, the purpose of this paper is to use a factor-cluster approach to segment patrons into market groups and attempts to determine if differences exist in motivational factors among segments.

Factor-cluster analysis is an alternative segmentation method to more traditionally used methods based on consumer demographics. Push and pull motivators were analyzed through factor analysis to determine important groupings. Then, to identify homogenous subgroups, k-means cluster analysis was conducted to segment 559 survey respondents based on factor importance.

Three diverse groups were identified: Fraternizing Kitchen Fearfuls, Functional Feasters, and Foodie Fanatics. The various push and pull factors appeared to affect segments differently, with each cluster ascribing various importance levels to each of the factors used in the clustering approach.

Limitations include the use of a convenience sample and on-campus sampling. Future research should use random sampling methods and obtain surveys from sites not associated with a college campus.

Though not often used in hospitality research, factor-cluster analysis can be useful to segment diners based on behavioral intentions and attributes, allowing marketers to more accurately target these diverse consumer segments. Marketing implications for casual dining restaurants are suggested.

Using the involvement construct with push/pull motivators, this study groups respondents though factor-cluster analysis. Though used in tourism studies, factor-cluster analysis has yet to be studied in the context of casual dining restaurant patrons.

The purpose of this paper is to examine the relationship between customer order lead time (COLT) and the price sensitivity of an electrical equipment manufacturer company. In consequence, it examines two research questions in terms of COLT, price and profitability level and to ensure the validity and practical justification of these research questions.

In this research the authors have used a case study approach where three business measures, namely COLT, price and the profitability level of a case company were investigated and analyzed critically. These measures were implemented through four different customer segments with two production lines of the case company. Data were collected from the company's order delivery database from the period 2006 to 2008. In addition, different experimental data were collected through interviewing and reviewing the results of the data analysis with the unit managers.

In this paper the authors have observed the correlation between the price, profit and COLT with all four customer segments in both the production lines of the case company. From the case data, the authors concluded that the customer did not pay more when the COLT is shorter than with the average time. It is also noticeable that the profit margin is higher for the case company to handle COLT with shorter lead time than the average order delivery lead time.

More case examples might be helpful to motivate the managers to accept the research outcomes.

The concept of the company's COLT in relation to the price and profitability level supports organizational managers in their decision‐making process in terms of productivity level and the company's growth. It will motivate the managers to make tradeoffs among various developmental measures.

This paper implemented a unique approach for measuring the significant level of price and profitability level over COLT. From the outcomes of this study, it is observed that the price correlated positively with the COLT and has a direct and significant impact on it. When the price is increased the COLT is also increased. It is also noticed that the products of the case company which offered shorter lead times were on average also more profitable, even though there were no significant differences in average pricing between the customer segments.

Internal capital markets of diversified firms have been associated with inefficient allocation of investment funds across divisions, leading to value losses. Utilizing a sample of diversified firms that adopted or eliminated Residual Income (RI) plans between 1990 and 2009, we show that adoptions of these plans mitigate investment distortions and lead to value gains. Following the adoption of RI plans, diversified firms start allocating investment funds based on growth opportunities of their divisions. RI plan adopters lower their divisional investment levels, especially in segments with below-average growth opportunities. The overall investment allocation efficiency improves, and the diversification discount diminishes after the adoption of RI plans. However, RI plans appear to be used only as temporary tools for assessing corporate performance. The plans are adopted primarily by firms expected to immediately generate plan bonuses for management, and they are frequently eliminated by firms with bad accounting performance and low managerial bonuses. The study contributes to the literature on organizational efficiency, internal capital markets, and on the importance of measures based on economic profits or RI.

Alongside structural equation modeling (SEM), the complementary technique of partial least squares (PLS) path modeling helps researchers understand relations among sets of observed variables. Like SEM, PLS began with an assumption of homogeneity – one population and one model – but has developed techniques for modeling data from heterogeneous populations, consistent with a marketing emphasis on segmentation. Heterogeneity can be expressed through interactions and nonlinear terms. Additionally, researchers can use multiple group analysis and latent class methods. This chapter reviews these techniques for modeling heterogeneous data in PLS, and illustrates key developments in finite mixture modeling in PLS using the SmartPLS 2.0 package.

The purpose of this paper is to apply a very simple but powerful analysis of the variance‐covariance matrix of individual best‐worst scores to detect which attributes are determining utility components and drive distinct consumer segments.

First an analysis of variance and covariance is used to find attributes which are perceived to have different importance by different consumers and which jointly drive consumer segments. Then we model consumer heterogeneity with Latent Clustering and identify utility dimensions of on‐premise wine purchase behaviour with a principal component analysis.

Four consumer segments were found on the UK on‐premise market, which differ in the relative strength of five wine choice utility dimensions: ease of trial, new experience, restaurant advice, low risk food matching and cognitive choice. These segments are characterised by sociodemographics as well as wine and dine behaviour variables.

Attributes with high variance signal respondents’ disagreement on their importance and indicate the existence of distinctive consumer segments. Attributes jointly driving those segments can be identified by a high covariance. Principal component analysis condenses a small number of behavioural drivers which allow an effective interpretation and targeting of different consumer segments.

This paper's analysis opens new doors for marketing research to a more insightful interpretation of best‐worst data and attitude scales. This information gives marketing managers powerful advice on which attributes they have to focus in order to target different consumer segments.

This is the first study considering individual differences in BW scores to find post hoc segments based on revealed differences in attribute importance.

Many insurance companies vigorously pursue top‐line growth, even though it has the potential to develop unprofitably over time. The time lag (or tail) between when insurance is sold and when claims are paid generates risks unique to insurance companies. Furthermore, the insurance market is both mature and efficient (i.e. its level of competitive risk is very high), which means that profitable opportunities are both rare and untenable unless protected by competitive advantage. There is currently no practical measure available (of which the authors are aware) at the business unit level to evaluate insurance premium growth in the face of the industry's risks, impairing executives' ability to assess segment opportunities (and hazards), thus hampering strategic decision making. The purpose of this paper is to introduce a practical measure developed by the authors called Underwriting Return (UWR) which aims at helping to alleviate this situation.

The paper introduces UWR which was developed during the course and scope of the authors' work in the insurance industry, and their research into applying value‐based management to that industry.

The paper finds that UWR is a practical measure that property and casualty executives can use at the business unit level to help quantify market segments to grow, hold, harvest and abandon.

A variety of strategic analysis tools, such as the popular Boston Consulting Group matrix, are utilized today. In general, the application of such tools is hampered by an imprecision of measurement but each can add a level of insight to executives' resource allocation options. UWR can further aid insurance executives in strategic analysis by helping to quantify in which segments to compete, and which ones to abandon. The paper demonstrates the utility of the measure in an example based on an actual analysis.

The purpose of this research paper is to apply the ABC model and examine the attitudinal segmentation of online consumer in India in terms of internet usage, perceived risks, website attributes, intent to do online purchase in future and preference of website attributes.

A structured questionnaire was administered to 600 online consumers using field and online survey mediums. The logit analysis was applied to arrive at segmentation of online users.

Findings show how attitudes are different of online users when they were segmented based on internet usage. This segmentation showed attitudes that were paradoxical in nature. To explain this paradoxical behaviour of online buyers, this study applied the logit analysis. The online users were further examined applying the ABC model of attitude. Three distinct segments of online users emerged. They are: CAB, CBA and BCA.

The findings of this paper will be useful for online retailers who want to start e-commerce business in India. The findings are also useful for designing appropriate promotion and marketing strategies to entice online users to become online buyers.

The key contributions of this paper are the new insights from using the ABC model. Based on usage of internet in number of hours, online buyers could be segmented into four groups. On further analysis using ABC model, this studied showed that a better segmentation of online buyers is possible and that is called attitudinal segmentation. The BCA attitudinal segment is a finding of this study and is unique to this research. This has not been done using Indian online buyers and this adds to the originality of the study.

The aim of this paper is to propose an approach to international market segmentation that identifies meaningful cross‐national consumer segments, which focuses on airline passengers in the NAFTA market.

A conjoint analysis is used to evaluate consumers' preferences for six flight attributes: price, in‐flight service, number of stops before destination, on‐time performance, frequent flyer programme, and country of airline. A cluster analysis based on the relative importance scores of each of the six flights attributes then identifies five segments that prioritize similar product attributes within each country.

A representative sample of 4,787 airline passengers from the three countries reveal that price is the most important attribute for consumers from the USA and Canada, while on‐time performance is the most important attribute for Mexican consumers. A cluster analysis identifies five segments that prioritize similar product attributes within each country. It is also found that there are five cross‐national consumer segments in the NAFTA market that are homogeneous in terms of consumer preferences but heterogeneous in terms of relative group size and demographic variables.

The study is based on a purposive sample, which limits the ability to generalize to the whole population with any known degree of precision.

The research produces practical operational information on each segment that is translatable into strategy, specifically in terms of positioning, promotion, and targeting of the airline service.

The paper sheds light on the nature of cross‐national segmentation in the NAFTA air passengers market and the resulting cross‐national segmentation will be highly relevant for international marketing management.

The conceptual problem associated with marketing productivity analysis is examined followed by an examination of currrent practice in marketing productivity in the following areas — on the product line, in advertising and promotional mix, in the salesforce, in distribution and in customer activity tracking. It provides UK companies with some guidance on how they can improve their performance measurement using marketing information systems and reorganising existing information for more effective marketing action. The research concentrates on 50 well‐known British companies in oil, chemicals, various engineering disciplines, food, pharmaceuticals, insurance, construction and chain‐store retailing. The findings are based on 28 viable responses, and a further 21 (different) responses from companies which were personally visited. Although the research techniques need to be refined they conclude that the management of resources invested in marketing activities can never be refined to the point where an incremental investment in any specific marketing application can be measured with great accuracy. Yet a great deal of measurement is possible and marketing managers can be well enough informed about the behaviour of marketing inputs so that allocation decisions in future periods will benefit.?

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.