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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.

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.

Massive amounts of data, manifesting in various forms, are being produced on the Web every minute and becoming the new standard. Exploring these information sources distributed in different Web segments in a unified way is becoming a core task for a variety of users’ and companies’ scenarios. However, knowledge creation and exploration from distributed Web data sources is a challenging task. Several data integration conflicts need to be resolved and the knowledge needs to be visualized in an intuitive manner. The purpose of this paper is to extend the authors’ previous integration works to address semantic knowledge exploration of enterprise data combined with heterogeneous social and linked Web data sources.

The authors synthesize information in the form of a knowledge graph to resolve interoperability conflicts at integration time. They begin by describing KGMap, a mapping model for leveraging knowledge graphs to bridge heterogeneous relational, social and linked web data sources. The mapping model relies on semantic similarity measures to connect the knowledge graph schema with the sources' metadata elements. Then, based on KGMap, this paper proposes KeyFSI, a keyword-based semantic search engine. KeyFSI provides a responsive faceted navigating Web user interface designed to facilitate the exploration and visualization of embedded data behind the knowledge graph. The authors implemented their approach for a business enterprise data exploration scenario where inputs are retrieved on the fly from a local customer relationship management database combined with the DBpedia endpoint and the Facebook Web application programming interface (API).

The authors conducted an empirical study to test the effectiveness of their approach using different similarity measures. The observed results showed better efficiency when using a semantic similarity measure. In addition, a usability evaluation was conducted to compare KeyFSI features with recent knowledge exploration systems. The obtained results demonstrate the added value and usability of the contributed approach.

Most state-of-the-art interfaces allow users to browse one Web segment at a time. The originality of this paper lies in proposing a cost-effective virtual on-demand knowledge creation approach, a method that enables organizations to explore valuable knowledge across multiple Web segments simultaneously. In addition, the responsive components implemented in KeyFSI allow the interface to adequately handle the uncertainty imposed by the nature of Web information, thereby providing a better user experience.

Producing meeting documents requires an instantaneous recorder during meetings, which costs extra human resources and takes time to amend the file. However, a high-quality meeting document can enable users to recall the meeting content efficiently. The paper aims to discuss these issues.

An application based on this framework is developed to help the users find topics and obtain summarizations of meeting contents without extra effort. This app uses the Bluemix speech recognizer to obtain speech transcripts. It then combines latent Dirichlet allocation and a TextTiling algorithm with the speech script of meetings to detect boundaries between different topics and evaluate the topics in each segment. TextTeaser, an open API based on a feature-based approach, is then used to summarize the speech transcripts.

The results indicate that the summaries generated by the machine are 85 percent similar to the records written by humankind.

To reduce the human effort in generating meeting reports, this paper presents a framework to record and analyze meeting contents automatically by voice recognition, topic detection, and extractive summarization.