Search results

The advancements of mobile technologies and devices have greatly facilitated the extension of online services from web to mobile environments. Drawing on the categorization theory, the purpose of this paper is to explore the impact of perceived entitativity on users’ web-mobile service extension behavior. The research model considers how perceived entitativity serves as a category cue to link the category- and piecemeal-based processing and shape users’ adoption of extended mobile services.

An online survey (n=552) was conducted to empirically test the model. The data were analyzed by structural equation modeling approach.

The results offer two major findings. First, performance expectancy, perceived controllability and subjective norm are important antecedents of users’ usage intention. Second, perceived entitativity has three types of effects on usage intention: it exerts a direct and positive influence on usage intention; it indirectly facilitates usage intention through increasing PE and perceived controllability; and it moderates the relationship between subjective norm and usage intention.

This study contributes to the literature by taking into account the interplay of category- and piecemeal-based processing to understand consumers’ web-mobile service extension behavior.

Projects have high stakes in how they are categorized. The final place of a project within a classification scheme depends on the inclusion or exclusion of certain classification criteria. So far, many researchers and organizations have used a variety classification criteria to construct different project classification schemes. However, most of these classification criteria have been taken for granted and the process of selecting them to categorize projects still remains a black box. The purpose of this paper is to open the black box of classification process and explain how it is reflected in picking the classification criteria.

Drawing on insights from cognitive psychology’s literature, the authors examine the main views of classification process to provide insight into the unknown or implicit reasons that one might have to pick particular attributes as project classification criteria.

The authors argue that classification occurs in the eye of the beholder; it is not only the project’s features per se but also the classifier’s “goals, ideal and preference” or “knowledge of causal relations” that are reflected in the classification criteria.

By elaborating the classification process, the authors brought the project context into the big picture of classification and provide a more rational, and coherent picture of how project classification works. This contributes to a theoretical blind spot, raised by prior researchers, related to the selection of project classification criteria.

Understanding classification processes will reduce the ambiguities, inconsistencies and multiple interpretations of project categories and help practitioners increase their projects’ visibility and legitimacy within an already established classification scheme. These implications help organizations in addressing some of the main obstacles to using categorization in project management practice.

The review of prior work in the category research literature and the insights from this paper will provide project management scholars with a useful toolbox for future research on project classification, which has long been understudied.

This paper aims to discuss and test the claim that utilization of the personalization techniques can be valuable to improve the efficiency of collaborative information retrieval (CIR) systems.

A new personalized CIR system, called PERCIRS, is presented based on the user profile similarity calculation (UPSC) formulas. To this aim, the paper proposes several UPSC formulas as well as two techniques to evaluate them. As the proposed CIR system is personalized, it could not be evaluated by Cranfield, like evaluation techniques (e.g. TREC). Hence, this paper proposes a new user‐centric mechanism, which enables PERCIRS to be evaluated. This mechanism is generic and can be used to evaluate any other personalized IR system.

The results show that among the proposed UPSC formulas in this paper, the (query‐document)‐graph based formula is the most effective. After integrating this formula into PERCIRS and comparing it with nine other IR systems, it is concluded that the results of the system are better than the other IR systems. In addition, the paper shows that the complexity of the system is less that the complexity of the other CIR systems.

This system asks the users to explicitly rank the returned documents, while explicit ranking is still not widespread enough. However it believes that the users should actively participate in the IR process in order to aptly satisfy their needs to information.

The value of this paper lies in combining collaborative and personalized IR, as well as introducing a mechanism which enables the personalized IR system to be evaluated. The proposed evaluation mechanism is very valuable for developers of personalized IR systems. The paper also introduces some significant user profile similarity calculation formulas, and two techniques to evaluate them. These formulas can also be used to find the user's community in the social networks.

The purpose of this paper is to propose a knowledge extraction framework to extract knowledge, including entities and relationships between them, from unstructured texts in digital humanities (DH).

The proposed cooperative crowdsourcing framework (CCF) uses both human–computer cooperation and crowdsourcing to achieve high-quality and scalable knowledge extraction. CCF integrates active learning with a novel category-based crowdsourcing mechanism to facilitate domain experts labeling and verifying extracted knowledge.

The case study shows that CCF can effectively and efficiently extract knowledge from multi-sourced heterogeneous data in the field of Tang poetry. Specifically, CCF achieves higher accuracy of knowledge extraction than the state-of-the-art methods, the contribution of feedbacks to the training model can be maximized by the active learning mechanism and the proposed category-based crowdsourcing mechanism can scale up the effective human–computer collaboration by considering the specialization of workers in different categories of tasks.

This research proposes CCF to enable high-quality and scalable knowledge extraction in the field of Tang poetry. CCF can be generalized to other fields of DH by introducing domain knowledge and experts.

The extracted knowledge is machine-understandable and can support the research of Tang poetry and knowledge-driven intelligent applications in DH.

CCF is the first human-in-the-loop knowledge extraction framework that integrates active learning and crowdsourcing mechanisms; he human–computer cooperation method uses the feedback of domain experts through the active learning mechanism; the category-based crowdsourcing mechanism considers the matching of categories of DH data and especially of domain experts.

The purpose of this paper is to develop a group-based food diversity index, which represents diversity in household expenditures across food subgroups. The index is compared to a product code-based index and applied to reassess determinants of food diversity demand.

A group-based food diversity index is developed by adapting the US Healthy Food Diversity Index. Using Food Acquisition and Purchase Survey data on 4,341 US households, correlation coefficients, descriptive statistics and linear regressions are estimated to compare and reassess the determinants of group and product code-based food diversity demand.

Results show that the group and product code indices capture different forms of food diversity. The indices are only moderately correlated and have varying means and skewness. Education, gender, age, household size, race, SNAP and food expenditures are found to significantly affect food diversity. However, the magnitude and direction of the effects vary between group and product code indices. Given these differences, it is essential that studies select a diversity index that corresponds to their objective. Results suggest that group-based indices are appropriate for informing food and nutrition policy, while product code-based indices are ideal for guiding food industry management’s decision making.

A group-based food diversity index representative of household expenditures across food subgroups is developed.

The purpose of this paper is to explore how brand origin (BO) cues affect the consumer’s association of a new brand with BO learning and the subsequent effects on brand image (BO semiotics). An integrative theoretical framework is proposed that includes both processes.

The proposed model is based on analogical learning theory and triadic semiotic theory.

Two types of BO knowledge form BO meanings in consumer minds: country-related categories and exemplar brands, which have a classification and/or inferential role. The brand cues (indexes or icons) used by consumers to identify BO generate one or the other type of BO knowledge. Indexes trigger the classification function of country-related categories while icons trigger the inferential role of country-related categories and exemplar brands. BO knowledge informs the meaning transfer when consumers interpret the meaning of a new brand, leading to either a transfer of relations or a transfer of attributes to the new brand.

Marketers should monitor BO exemplar brands that consumers use as meaning sources and carefully select the signs used in their communications to evoke BO.

The proposed framework contrasts with dominant categorisation perspectives, re-establishing the dual role of categories and emphasising the relevance of brand cues in BO identification and BO exemplar brands in the BO meaning transfer process. A meaning-centred perspective is adopted to integrate BO identification and the related transfer mechanisms.

This study aims to examine the relationship between family business identity disclosure by firms and consumer product evaluations and the moderating impact, if any, of firm size on this relationship. Toward this end, the study seeks to develop a theoretical explanation for how consumers process family business identity information.

A qualitative pre-study was conducted to obtain preliminary evidence that consumers’ perceptions of family businesses originate from both family- and business-based category beliefs. A product evaluation experiment, involving young adult subjects, was used to test the research hypotheses, and the experiment data were analyzed using MANOVA.

The key finding was that the effect of family business identity disclosure on consumer product evaluations is moderated by firm size.

This research has implications for businesses seeking to promote their family business identity in branding communications.

This research provides a theoretical account of why consumers might hold different perceptions of family business brands. The interactive effect of firm size and family business identity information disclosure on consumer product evaluations contributes new insight to family business branding.

Content‐based image retrieval suffers from the semantic gap problem: that images are represented by low‐level visual features, which are difficult to directly match to high‐level concepts in the user's mind during retrieval. To date, visual feature representation is still limited in its ability to represent semantic image content accurately. This paper seeks to address these issues.

In this paper the authors propose a novel meta‐feature feature representation method for scenery image retrieval. In particular some class‐specific distances (namely meta‐features) between low‐level image features are measured. For example the distance between an image and its class centre, and the distances between the image and its nearest and farthest images in the same class, etc.

Three experiments based on 190 concrete, 130 abstract, and 610 categories in the Corel dataset show that the meta‐features extracted from both global and local visual features significantly outperform the original visual features in terms of mean average precision.

Compared with traditional local and global low‐level features, the proposed meta‐features have higher discriminative power for distinguishing a large number of conceptual categories for scenery image retrieval. In addition the meta‐features can be directly applied to other image descriptors, such as bag‐of‐words and contextual features.

This study aims to uncover relationships between content communities post language, such as parts of speech, and user engagement.

Analyses of almost 12,000 posts from the content community Reddit are undertaken. First, posts’ titles are subjected to electronic classification and subsequent counting of main parts of speech and other language elements. Then, statistical models are built to examine the relationships between these elements and user engagement, controlling for variables identified in previous research.

The number of adjectives and nouns, adverbs, pronouns, punctuation (exclamation marks, quotation marks and ellipses), question marks, advisory words (should, shall, must and have to) and complexity indicators that appear in content community posts’ titles relate to post popularity (scores: number of favourable minus unfavourable votes) and number of comments. However, these relationships vary according to the category, for example, text-based categories (e.g. Politics and World News) vs image-based ones (e.g. Pictures).

While the relationships uncovered are appealing, this research is correlational, so causality cannot be implied.

Among other implications, companies may tailor their own content community post titles to match the types of language related to higher user engagement in a particular category. Companies may also provide advice to brand ambassadors on how to make better use of language to increase user engagement.

This paper shows that language features add explained variance to models of online engagement variables, providing significant contribution to both language and social media researchers and practitioners.