Search results

Competitor analysis is a key component in operations management. Most business decisions are rooted in the analysis of rival products inferred from market structure. Relative to more traditional competitor analysis methods, the purpose of this paper is to provide operations managers with an innovative tool to monitor a firm’s market position and competitors in real time at higher resolution and lower cost than more traditional competitor analysis methods.

The authors combine the techniques of Web Crawler, Natural Language Processing and Machine Learning algorithms with data visualization to develop a big data competitor-analysis system that informs operations managers about competitors and meaningful relationships among them. The authors illustrate the approach using the fitness mobile app business.

The study shows that the system supports operational decision making both descriptively and prescriptively. In particular, the innovative probabilistic topic modeling algorithm combined with conventional multidimensional scaling, product feature comparison and market structure analyses reveal an app’s position in relation to its peers. The authors also develop a user segment overlapping index based on user’s social media data. The authors combine this new index with the product functionality similarity index to map indirect and direct competitors with and without user lock-in.

The approach improves on previous approaches by fully automating information extraction from multiple online sources. The authors believe this is the first system of its kind. With limited human intervention, the methodology can easily be adapted to different settings, giving quicker, more reliable real-time results. The approach is also cost effective for market analysis projects covering different data sources.

Segmenting techniques used in the lodging industry typically assume that individuals fall into mutually exclusive groups, that is, they are assigned to one type of lodging concept by the segmenting method used. In reality, however, travelers often use several types of lodging alternatives. This study utilized a canonical correlation approach to segment the senior pleasure traveler market. The analysis resulted in both uniquely defined and overlapping segments based on the relationship between lodging preferences and benefits/attributes sought in a lodging accommodation. The study also revealed implications dealing with overlapping segments as well as loyalty to specific lodging segments.

This study examines internet banking adoption and resistance behaviour in Greece in order to develop profiles of adopters and non‐adopters of the service. The aim is to illustrate customers' resistance behaviour towards internet banking. The existing research does not explain resistance behaviour, since it does not clearly distinguish non‐adoption from resistance. Consequently, it has not recognised the different types of non‐adoption.

A measuring instrument was developed and utilised in a survey of a convenience sample of 1,200 customers. The derived dimensionality of the relevant perceptual variables was used to explore the existence of different customer segments through cluster analysis.

Three segments were identified, where the description of their profiles is based on customer perceptions of the service and general usage data. Across these segments adopters and non‐adopters were found to have different characteristics. With regard to demographics, only income was found to be associated with segment membership.

Perceptual and usage variables are useful in market segmentation. The results also suggest the possible existence of sub‐groups within each segment characterised by different aspects of resistance behaviour. Further research could identify and explore their potential and study non‐adopter behaviour.

Service providers should target users and non‐users across the segments differently. While the users identified require different retention policies, the resistance or non‐resistance observed in non‐users suggest the proper management of delay and rejection behaviours.

The customer segments identified in this study are based on new links found between the factors that drive diffusion and resistance to diffusion and general usage data. Non‐adopters across the segments resist for different reasons, or not resist.

Social networking sites (SNS) have become extensively used communication environments as a result of the advancements in online technologies, and among various SNS platforms, Instagram is currently the most prominent image-based network. Since usage motives for alternative SNS environments with different outstanding benefits are expected to vary, this study has focused mainly on extracting the key context-specific usage motives of Instagram. Another purpose of this study is to figure out personality traits differences among Instagram user segments.

An online survey was designed, and a total of 690 fully completed questionnaires was collected, and 507 of the respondents were Instagram users. After conducting factor analysis, six main usage motive categories have been revealed and named as self-expression, recording, socialization, recreation, creativity, and prying.

Instagram users have been clustered as passionate, distant, and spectator users based on their usage motives. Ultimately, personality differences among these clusters have been explored using the Big Five Inventory (BFI) and two additional traits, social interaction anxiety, and fear of negative evaluation. Openness to experience, social interaction anxiety, and fear of negative evaluation were found to be significantly different among these clusters.

Discovering the motives of SNS usage, segmenting users based on these motives, and then portraying the personality traits of each segment gives important clues about how SNSs can better design their interfaces and generate content for attracting users in different segments.

It is important to remain creative when conducting segmentation research, as many different ways to segment a market can exist. Five main bases are discussed: geographic, demographic, psychographic, behaviouristic and image. This is followed by an overview of the main techniques used to establish and verify segments, including automatic interaction detector, conjoint analysis, multidimensional scaling and canonical analysis.

Collaborative learning with recorded lectures and presentations can be supported by allowing users to anchor notes in the documents and exchange them with other learners. While the traditional modality for annotation and discussion is text, there are a number of reasons in favour of supporting other media and modalities as well. We describe the extension of a lecture‐on‐demand annotation and discussion system that allows learners to use spoken notes. Our main focus is on the development of a suitable user interface that facilitates the retrieval of speech data employing signal‐processing algorithms while at the same time being simple and easy to use.

For a better understanding of the preferences and differences of young consumers in emerging wine markets, this study aims to propose a clustering method to segment the super-new generation wine consumers based on their sensitivity to wine brand, origin and price and then conduct user profiles for segmented consumer groups from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences.

We first proposed a consumer clustering perspective based on their sensitivity to wine brand, origin and price and then conducted an adaptive density peak and label propagation layer-by-layer (ADPLP) clustering algorithm to segment consumers, which improved the issues of wrong centers' selection and inaccurate classification of remaining sample points for traditional DPC (DPeak clustering algorithm). Then, we built a consumer profile system from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences for segmented consumer groups.

In this study, 10 typical public datasets and 6 basic test algorithms are used to evaluate the proposed method, and the results showed that the ADPLP algorithm was optimal or suboptimal on 10 datasets with accuracy above 0.78. The average improvement in accuracy over the base DPC algorithm is 0.184. As an outcome of the wine consumer profiles, sensitive consumers prefer wines with medium prices of 100–400 CNY and more personalized brands and origins, while casual consumers are fond of popular brands, popular origins and low prices within 50 CNY. The wine sensory attributes preferred by super-new generation consumers are red, semi-dry, semi-sweet, still, fresh tasting, fruity, floral and low acid.

Young Chinese consumers are the main driver of wine consumption in the future. This paper provides a tool for decision-makers and marketers to identify the preferences of young consumers quickly which is meaningful and helpful for wine marketing.

In this study, the ADPLP algorithm was introduced for the first time. Subsequently, the user profile label system was constructed for segmented consumers to highlight their characteristics and demand partiality from three aspects: demographic characteristics, consumers' eating habits and consumers' preferences for wine attributes. Moreover, the ADPLP algorithm can be considered for user profiles on other alcoholic products.

The authors develop a logit modeling approach, designed for application to UPC scanner panel data, to assess the effects of coupon promotions on consumer brand choice. The effects of coupon promotions are captured via two measures: the prevailing level of availability and the prevailing face value of coupons for each brand. Both of these measures are derived from coupon redemptions of a separate sample of households. The approach captures both the advertising effect and the price discount incentive of a coupon. It also avoids drawbacks of previous choice models which have incorporated coupon effects by subtracting the value of a redeemed coupon from the price of the brand purchased. The authors illustrate their modeling approach on data for two product categories: catsup (light coupon usage) and liquid laundry detergent (heavy coupon usage). Findings are reported for coupon users and non‐users as well as across latent segments.