Searching customer patterns of mobile service using clustering and quantitative association rule

Sohn, So Young; Kim, Yoonseong

doi:10.1016/j.eswa.2006.12.001

Cited by 43 publications

(28 citation statements)

References 10 publications

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“…The typical approach to strategic group identification consists of collecting detailed industry data, and then identifying groups through clustering or other grouping algorithms (Shanley & Peteraf, 2005;Sohn & Kim, 2008). The variables used to group will have much influence on the identified groups.…”

Section: Introductionmentioning

confidence: 99%

Strategic group identification using evolutionary computation

Torres¹,

Toral²

2010

Expert Systems with Applications

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a b s t r a c tThis paper proposes to identify strategic groups among franchisors from a big set of franchisor variables. Genetic evolutionary computation was used to reduce a set of variables efficiently, and factor analysis was used to make up the strategic groups. Franchise 500 was used as database. The results suggest both that the general map of franchisor has changed since Carney and Gedajlovic's study, and that genetic evolutionary computation is a valid way to extract knowledge from a huge set of data. This paper proposes useful information for those retail firms considering internationalization via franchising. The originality of this paper is in the use of Genetic Algorithm to discriminate the final set of variables to be used for the identification of strategic groups instead of evaluating one by one the adequacy of each variable theoretically. The ability of evolutionary computation to create new knowledge is good to produce new insights into this topic.

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Section: Introductionmentioning

confidence: 99%

Strategic group identification using evolutionary computation

Torres¹,

Toral²

2010

Expert Systems with Applications

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“…Understanding the mechanism leading to ubiquitous computing user behaviors is critical to its diffusion [102]. The findings of this study help managers to understand the different criteria between inexperience and experience users in urban environments, resulting in facilitating more efficiently targeted marketing for ubiquitous computing services in each group.…”

Section: Discussionmentioning

confidence: 99%

The moderating role of prior experience in technological acceptance models for ubiquitous computing services in urban environments

Torres

Díaz‐Fernández

Toral

et al. 2015

Technological Forecasting and Social Change

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Ubiquitous computing services constitute a new information technology that can be used in thousands of potential applications and environments. Ubiquitous computing is also changing the classic paradigm of information technology as it is forcing social and cultural changes. Determining factors affecting the use of ubiquitous services is essential to correctly define the characteristics of new value added services. However, this study investigates not only these factors, but also the moderating effect of previous experience. Due to the technological nature of ubiquitous services, previous experience alters the way in which potential users face these services. Findings suggest that previous experience changes the way in which antecedent relates to basic TAM constructs. The derived research models and empirical results also provide valuable indicators for future research and managerial guidelines for the successful adoption of ubiquitous computing services.

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“…In Korea, Sohn and Kim (2008) used patterns of mobile service usage, such as "Caller ID", and identified three clusters: (1) a segment characterised by their utilisation of paid for services; (2) a segment based on their frequent use of low-cost or free services; and (3) a segment based on an indistinct usage pattern. In Canada, Head and Ziolkowski (2010) segmented mobile phone users into two groups according to their preferences to use certain mobile phone functions: (1) a segment distinguished by its high use of text messages, and (2) a segment that considered texting to be important, but rated access to Internet and email as being important functions too.…”

Section: Segmentationmentioning

confidence: 99%

“…At a count of 34.5 million, this segment is a sizeable proportion of consumers. Generation Y, apart from its size, represents a significant population group from a marketing perspective and has demonstrated a particular affinity for new media, through their tech savvy aptitudes (Sohn & Kim, 2008;Stald, 2008;Yarrow & O"Donnell, 2009). …”

Section: Samplingmentioning

confidence: 99%

“…Therefore, a useful segmentation approach towards an improved understanding of consumer behaviour, coupled with the features available and those that mobile owners access on their mobile phones could be productive for both marketers and consumers. Although numerous mobile phone segmentation studies have been initiated elsewhere, (Head & Ziolkowski, 2010;Kimiloğlu, Nasir & Nasir, 2010;Okazaki, 2006;Sohn & Kim, 2008;Zhu et al, 2009), none of these studies considered the availability of different features of mobile devices and the actual use of these features as an approach to segmentation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Conventionalists, Connectors, Technoisseurs and Mobilarti: Differential profiles of mobile marketing segments based on phone features and postmodern characteristics of consumers

Goneos-Malka¹,

Strasheim²,

Grobler³

2014

Journal of Retailing and Consumer Services

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The high penetration of mobile phones amongst the South African population presents mobile phones as an attractive interactive marketing communication medium. This paper argues that the access and actual use of different phone device features can be productively used as a segmentation approach, which may enable marketers to be more effective in planning interactive marketing communication plans. This study, based on 330 students, developed segments derived from mobile phone usage patterns using cluster analysis. The outcome revealed four clusters that were named: Connectors, Conventionalists, Technoisseurs and Mobilarti. Connectors made daily use of a full range of communication functions.Conventionalists were inclined to limit their use of mobile phone features to talking and texting. Technoisseurs were found to use a whole range of sophisticated mobile phone facilities. Mobilarti were identified as a group of expert users; using the full range of functions available to them on their phones, despite recording the lowest percentage of 2 smartphone ownership when compared to the other groups. These groups were further profiled by analysing attitudinal and behavioural variables pertaining to two newly developed postmodern dimensions, which were introduced in the study as mobile importance as an attitudinal aspect, and social transformation as a behavioural outcome. For marketers, an understanding of the proposed segments, as well as the differences in attitudes towards mobile importance and social transformation behaviours, coupled with typical financial and social realities of these segments, allow targeting strategies that are more clearly actionable.

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Searching customer patterns of mobile service using clustering and quantitative association rule

Cited by 43 publications

References 10 publications

Strategic group identification using evolutionary computation

Strategic group identification using evolutionary computation

The moderating role of prior experience in technological acceptance models for ubiquitous computing services in urban environments

Conventionalists, Connectors, Technoisseurs and Mobilarti: Differential profiles of mobile marketing segments based on phone features and postmodern characteristics of consumers

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