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Three-parameter interval grey numbers (TPIGNs) have been extensively studied as an extended form of interval numbers. However, most existing TPIGN multi-attribute decision-making methods only consider the similarity of positions, ignore the similarity of developmental directions and focus primarily on static evaluation. To address these limitations, in this study, the authors propose a dynamic technique for order preference by similarity to an ideal solution (TOPSIS) based on modified Jaccard similarity and angle similarity for TPIGNs.

First, the authors extend Jaccard similarity to a TPIGN environment to represent positional similarity. A simple example is provided to illustrate the limitations of the traditional Jaccard similarity. Then, the authors introduce an angle similarity measure to represent developmental directional similarity. Finally, a dynamic TOPSIS model is constructed by incorporating time-series data into conventional two-dimensional static data. Stage weights are obtained by an objective function designed to maximize the amount and minimize the fluctuation of decision information. A quadratic weighting method is adopted to derive the overall evaluation value of alternatives.

To evaluate the effectiveness of the proposed method, this study takes the pre-assessment of ice disaster and the selection of cooperative enterprises as examples. The authors then provide the results of comparative and sensitivity analyses, which demonstrate the effectiveness and flexibility of the proposed method.

The proposed TOPSIS method in a TPIGN environment can take a more holistic and dynamic perspective for decision-making, which helps mitigate the limitations of single-perspective or static evaluations.

Nowadays, online platforms which provide products or services try to implement their homegrown communities to facilitate users' social interactions. Reviewers' activities in these communities can reflect their interests. Based on the theory of homophily, the authors aim to explore the impacts of the reviewer preference similarity and opinion similarity on the rate of product diffusion.

First, the authors construct reviewer similarity network based on their common interests and propose typical network metrics to measure reviewer preference similarity. Second, the authors measure reviewer opinion similarity with natural language processing. Finally, based on a panel data from an online video platform in China, both the fixed-effect and random-effect panel data models are constructed.

The authors find that reviewer preference similarity has a positive effect on the product diffusion, whereas reviewer opinion similarity has a negative effect on the diffusion. Furthermore, temporal distance moderates the relationship between reviewer similarity and the product diffusion. As a double-edged sword, review preference similarity hinders product diffusion in the initial phase, whereas benefits it in the later phase. Reviewer opinion similarity is always detrimental to product diffusion, especially in the initial phase.

This paper extends the understanding of homophily from the micro peer level to the group level by constructing reviewers' similarity network and highlights the important role of reviewer preference similarity and opinion similarity in product diffusion. The results also provide important insights for managers to design and implement diversity strategies for better product adoption in the community context.

Though social trends are driving consumers toward solo consumption of various services, many are reluctant to do so. There is little guidance for service providers as to how to effectively induce solo consumption. This study aims to examine the joint effect of self-esteem and an incidental similarity cue (e.g. a person’s initials) on anticipated satisfaction with with a solo consumption experience to fill this gap.

This study used a two-factor (incidental similarity cue and self-esteem) quasi-experimental design to test the hypotheses. The respondents read a scenario depicting a solo service consumption experience and completed scales that measured perceived fit with the service context and anticipated satisfaction with the experience.

Results indicate that, in the absence of an incidental similarity cue, self-esteem has a positive effect on solo consumers’ perceived fit. In the presence of such a cue, however, self-esteem has a minimal impact on perceived fit. Furthermore, perceived fit mediates the effect of self-esteem on anticipated satisfaction when the cue is absent.

The authors’ findings suggest that promoting incidental similarities with consumers may not be an efficient strategy to attract solo consumers. Conversely, service providers wishing to induce solo consumption may benefit from situationally increasing self-esteem among potential solo consumers. The current research advances the authors’ understanding of the effect of an incidental similarity cue and self-esteem in the context of a growing social trend of solo consumption.

This article aims to explore how to incorporate similarity joins into XML database management systems (XDBMSs). The authors aim to provide seamless and efficient integration of similarity joins on tree-structured data into an XDBMS architecture.

The authors exploit XDBMS-specific features to efficiently generate XML tree representations for similarity matching. In particular, the authors push down a large part of the structural similarity evaluation close to the storage layer.

Empirical experiments were conducted to measure and compare accuracy, performance and scalability of the tree similarity join using different similarity functions and on the top of different storage models. The results show that the authors’ proposal delivers performance and scalability without hurting the accuracy.

Similarity join is a fundamental operation for data integration. Unfortunately, none of the XDBMS architectures proposed so far provides an efficient support for this operation. Evaluating similarity joins on XML is challenging, because it requires similarity matching on the text and structure. In this work, the authors integrate similarity joins into an XDBMS. To the best of the authors’ knowledge, this work is the first to leverage the storage scheme of an XDBMS to support XML similarity join processing.

The purpose of this paper is to propose and experiment a framework for analysing the tweets to find the basis of popularity of a person and extract the reasons supporting the popularity. Although the problem of analysing tweets to detect popular events and trends has recently attracted extensive research efforts, not much emphasis has been given to find out the reasons behind the popularity of a person based on tweets.

In this paper, the authors introduce a framework to find out the reasons behind the popularity of a person based on the analysis of events and the evaluation of a Web-based semantic set similarity measure applied to tweets. The methodology uses the semantic similarity measure to group similar tweets in events. Although the tweets cannot contain identical hashtags, they can refer to a unique topic with equivalent or related terminology. A special data structure maintains event information, related keywords and statistics to extract the reasons supporting popularity.

An implementation of the algorithms has been experimented on a data set of 218,490 tweets from five different countries for popularity detection and reasons extraction. The experimental results are quite encouraging and consistent in determining the reasons behind popularity. The use of Web-based semantic similarity measure is based on statistics extracted from search engines, it allows to dynamically adapt the similarity values to the variation on the correlation of words depending on current social trends.

To the best of the authors’ knowledge, the proposed method for finding the reason of popularity in short messages is original. The semantic set similarity presented in the paper is an original asymmetric variant of a similarity scheme developed in the context of semantic image recognition.

The purpose of this paper is to propose a new approach to retrieve similar questions in online health communities to improve the efficiency of health information retrieval and sharing.

This paper proposes a hybrid approach to combining domain knowledge similarity and topic similarity to retrieve similar questions in online health communities. The domain knowledge similarity can evaluate the domain distance between different questions. And the topic similarity measures questions’ relationship base on the extracted latent topics.

The experiment results show that the proposed method outperforms the baseline methods.

This method conquers the problem of word mismatch and considers the named entities included in questions, which most of existing studies did not.

This paper aims to examine the influences of the number of hotel management responses and especially the textual similarity in hotel management responses to online reviews on hotel online booking.

This study used the data from 437 hotels in New York City on Expedia. The data specifically include online reviews, management responses and real-time number of online hotel bookings, which were merged to create one dataset for this study. To calculate the management response similarity, three widely recognized text mining functions of calculating textual similarity were adopted in this model. Fixed-effect panel data model was then used to examine the influence of management response to consumer online reviews on online hotel booking volume.

The empirical results demonstrate that the number of management responses to consumer online reviews does not significantly affect hotel booking; compared to none or only one management response, or management responses with low similarity, management responses with high similarity can significantly reduce the hotel booking on Expedia.

This study suggests that the similarity of management responses influences customers’ hotel booking, and hotel managers should avoid providing too similar management responses.

First, this study, for the first time, proposes the concept of management response similarity and its measurement methods. Second, this study takes an initial attempt to empirically test the influence of response similarity on hotel booking by using secondary data online.

The purpose of this paper is to show that high similarity between a parent brand and an extension category can have a detrimental effect on how a brand extension is perceived to perform on specific attributes. This happens because similarity influences the perceived positioning of a brand extension: lower similarity extensions can be perceived as “specialized” products, whereas high similarity extensions are perceived as “all-in-one” products not performing exceptionally well on any specific attribute.

The authors test the hypothesized effect through three experimental studies. The authors manipulate similarity both within subjects (Study 1a) and between subjects (Study 1b and Study 2). Further, the authors test the effect for specific attributes that are physical/concrete in nature (Study 1a and Study 1b) as well as attributes that are abstract/imagery-related in nature (Study 2).

High compared to low similarity improves perceptions of overall performance (i.e. performance across all attributes). But as expected, the authors also find that a high similarity brand extension is perceived to perform worse on the attribute on which a low similarity brand extension specializes, even when the parent brands of the extensions possess that attribute to the same extent. This perception of attribute performance carries on to influence brand extension purchase likelihood.

The degree of brand extension similarity has consequences for how brand extensions are perceived to be positioned in the marketplace. Although high similarity extensions receive positive evaluations, they might not be suitable when a company is trying to instil a perception of exceptional performance on a specific attribute.

The authors demonstrate a consequential exception to the marketing wisdom that brands should extend to similar categories. Although the degree of brand extension similarity has been repeatedly shown to have a positive effect on brand extension evaluation, the authors document a case when its effect is actually detrimental. This study’s focus on the dependent variable of perceived performance on specific attributes is novel in the brand extension literature.

In management research, little is known about how ethno-racial minority leaders interact with similar employees in supervisor–subordinate relationships. This study aims to examine and provide a deeper understanding of individuals’ negative reactions to similar others, thus highlighting the double-edged nature of demographic similarity which has historically predicted positive affective reactions.

Using a survey design, the authors collected data from supervisor-subordinate dyads from multiple companies from the Dallas-Fort Worth metroplex in Texas, USA. They used ordinary least squares regression and conditional process analysis to test the hypotheses, including a two-stage moderation and moderated mediation.

Incorporating social context, i.e. minority status, as a moderator, the results show that ethno-racial minority leaders supervising ethno-racially similar subordinates were more vulnerable to relationship conflict than non-minority dyads. This, in turn, is linked to a reduction in the leaders’ feelings of trust toward their ethno-racially similar subordinate.

This study draws on social identity theory and status characteristics theory to explain the contradictory processes and outcomes associated with dyadic ethno-racial similarity and suggests the conditions under which dyad racial similarity is connected with unfavorable outcomes. This framework helps to broaden the boundary conditions of relational demography to provide a more nuanced explanation of when and why minority leaders in demographically similar hierarchical dyads experience more relationship conflict, which ultimately diminishes trust.

The purpose of this study is to achieve accurate matching of new process cases to historical process cases and then complete the reuse of process knowledge and assembly experience.

By integrating case-based reasoning (CBR) and ontology technology, a multilevel assembly ontology is proposed. Under the general framework, the knowledge of the assembly domain is described hierarchically and associatively. On this basis, an assembly process case matching method is developed.

By fully considering the influence of ontology individual, case structure, assembly scenario and introducing the correction factor, the similarity between non-correlated parts is significantly reduced. Compared with the Triple Matching-Distance Model, the degree of distinction and accuracy of parts matching are effectively improved. Finally, the usefulness of the proposed method is also proved by the matching of four practical assembly cases of precision components.

The process knowledge in historical assembly cases is expressed in a specific ontology framework, which makes up for the defects of the traditional CBR model. The proposed matching method takes into account all aspects of ontology construction and can be used well in cross-ontology similarity calculations.