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This study elicits the critical attributes, consequences and values associated with the purchasing process in the context of cross-border e-commerce (CBEC). The purpose is to provide a better understanding of the fundamental factors that determine consumer values in CBEC.

The study applies the means-end-chain theory and soft-laddering techniques to interview 60 CBEC consumers to construct an implication matrix and a hierarchical value map (HVM) of the consumer purchasing process, consisting of attribute-consequence-value (A-C-V) paths.

By analyzing the significant linkages, elements, ladders and chains in the HVM, four dominant A-C-V paths were identified: economic-driven, efficiency-driven, progress-driven and quality-driven paths.

This study included only Chinese CBEC buyers. This limitation might affect the generalizability of the conclusions as culture, purchase habits and economic development differ between China and other countries.

The results of this study provide CBEC practitioners an understanding of the consumer purchasing process and how consumer values are associated with platform characteristics. Thus, the results aid practitioners in allocating resources and developing CBEC platforms in an appropriate manner and direction.

This study sheds lights on the emerging phenomenon of CBEC. By applying the means-end-chain approach, the study provides a comprehensive HVM for interpreting the consumer online purchasing process in this novel context. By illustrating the dominant paths, this research provides deeper theoretical insights into the specific focuses of CBEC consumer purchasing.

Integrating the Chat Generative Pre-Trained Transformer-type (ChatGPT-type) model with government services has great development prospects. Applying this model improves service efficiency but has certain risks, thus having a dual impact on the public. For a responsible and democratic government, it is necessary to fully understand the factors influencing public acceptance and their causal relationships to truly encourage the public to accept and use government ChatGPT-type services.

This study used the Latent Dirichlet allocation (LDA) model to analyze comment texts and summarize 15 factors that affect public acceptance. Multiple-related matrices were established using the grey decision-making trial and evaluation laboratory (grey-DEMATEL) method to reveal causal relationships among factors. From the two opposite extraction rules of result priority and cause priority, the authors obtained an antagonistic topological model with comprehensive influence values using the total adversarial interpretive structure model (TAISM).

Fifteen factors were categorized in terms of cause and effect, and the antagonistic topological model with comprehensive influence values was also analyzed. The analysis showed that perceived risk, trust and meeting demand were the three most critical factors of public acceptance. Meanwhile, perceived risk and trust directly affected public acceptance and were affected by other factors. Supervision and accountability had the highest driving power and acted as the causal factor to influence other factors.

This study identified the factors affecting public acceptance of integrating the ChatGPT-type model with government services. It analyzed the relationship between the factors to provide a reference for decision-makers. This study introduced TAISM to form the LDA-grey-DEMATEL-TAISM method to provide an analytical paradigm for studying similar influencing factors.

Due to the high use of mobile devices, the market share of mobile advertisements (Ads) is significantly growing. Although mobile Ads can contact potential customers at any time and in any location depending on their unique demands, one of the biggest problems for advertisers is how to improve customer repurchases with their Ads. The development and empirical support of customer repurchase through mobile Ads context have not been addressed. Therefore, the purpose of this paper is to define and identify the key attributes of customer repurchase in a mobile Ads context.

In this research, the set of attributes was derived from a systematic literature review and finalized by applying the Fuzzy Delphi method. To develop a hierarchical model and classify the cause/effect groups among identified key attributes, the Fuzzy mixed approach uses a combination of Fuzzy interpretive structural modeling-decision-making trial and evaluation laboratory.

The findings suggest that language, type of website and social media are classified to as essential attributes for improving customer repurchase through mobile Ads.

The focus of the current research is limited to identify and develop the hierarchical interrelationships between customer repurchase attributes that are unique to the mobile Ads business context. Additional research may be conducted for various media contexts and other products/services categories.

This study illustrated how multicriteria decision-making techniques could be used effectively using Fuzzy theory to explore the research area of customer repurchase in mobile Ads concept.

The purpose of this paper is to explore the key performance indicators (PIs) that serve as a decision support tool in case of dairy supply chain practices and to analyze their interactions in the context of Indian dairy industry sector. A total of 11 PIs have been identified through the literature review and the opinions of an expert team consisting of managerial and technical experts from dairy industry and academics.

A solution methodology based on the interpretive structure modeling (ISM) technique is used to analyze the interactions among PIs and to propose a structural model. The developed model not only helps in understanding the contextual relationship among the PIs, but also in determining their interdependence to assess the supply chain performance in dairy industry. Further, the importance of PIs has been determined based on their driving and dependence power by using MICMAC analysis.

The ISM-based model suggests four PIs at first level, three PIs at second level, one PI at third level as well as one PI at fourth level and two PIs at fifth level. Model allocates to the effective information technology, brand management, responsiveness in shipment and accuracy and a control over wastages as the key PIs in the dairy industry sector. The effective traceability systems, cold chain infrastructure, quality management and the support for technological innovations are the next major PIs. There exists no autonomous PI in MICMAC analysis which proves the importance of identified PIs in the case study.

The proposed model is an attempt to capture the dynamics of milk processing sector and to incorporate all relevant constraints related to internal and external environments that would significantly improve the supply chain performance in the dairy industry.

The model developed in this study has been tested in the cooperative milk processing units based in India and also discussed with the experts from academics. This work may help practitioners, regulators and dairy industry professionals to focus their efforts toward achieving high performance by the effective implementation of the identified PIs.

In this study, 11 PIs are considered. Interactions among PIs are evaluated with the help of the ISM matrix. Out of the 11 PIs, six demonstrate both strong driving and dependence power as explained in the MICMAC analysis.

This paper aims to proposes a framework, labeled strategic alignment matrix, to attain organizational alignment by integrating the horizontal dimension of performance (results driven by activities carried out by multiple organizational units) and the vertical one (results of single units) through the use of a sophisticated information structure composed by quantitative measures and management processes.

A science-based design approach was adopted. A review of the literature on strategic performance measurement systems (SPMS) and coordination allowed the identification of a set of design principles (guidelines reflecting the accumulated knowledge in the literature). The design principles guided the design of the proposed framework. The framework was tested in a tiles company on the new product development process.

Five design principles are presented for the design of a working SPMS as follows: to integrate the horizontal and vertical dimensions of performance; to have all the relevant information in one place (package); to understand how actors contribute to the overall performance; to favor the emergence of integrating conditions for coordination; and to enrich the role of quantitative non-financial information to attain inter-functional integration. During the test of the framework, managers highlighted the increased ability to coordinate actions and the existence of double-loop learning.

The model was tested in one organization. The study should be replicated in other contexts connecting the strategic alignment matrix to the budgeting and incentive systems.

Working at the interface between science and design helps to address the theory-practice gap that has been a priority in management studies for long.

The present research identifies a total of nine factors influencing tax avoidance under the international taxation regime of the developing countries and establishes a hierarchical relationship through modeling of the identified factors using modified-total interpretive structural modeling (M-TISM).

Due to “scale without mass” properties of the digital economy, businesses reduce their physical presence in the countries of economic activities. Aided with digital features, multinational enterprises (MNEs) avoid, abolish, or adopt flexible tax burden in the developing nations through by-passing the permanent establishment condition for company taxes or the income characterization prerequisite for royalty taxation. The present research endeavors to identify the drivers of tax avoidance in the developing countries, especially exacerbated due to digital technologies (economy). In addition, the authors also examine the hierarchical relation between the extracted drivers of tax avoidance.

This research presents a considerable driving force of elements like historical foundation of tax-treaties, dominance of the developed countries, influence of trade bodies in policy matters and finally information and communications technologies (ICTs).

Identified elements drive the actors like professional enablers, tax havens, international organizations, and intangible assets in the form of intellectual properties (IPs) which act upon tax arbitrage situations both under the domestic and treaty regulations, finally culminating into profit shifting, tax manipulations or avoidance.

The gradual ageing of global population has necessitated the creation of conducive and supportive food and beverage environments for older adults. This study identifies the key evaluation criteria for senior-friendly restaurants and examines the importance of each criterion.

This study uses the fuzzy analytic hierarchy process (FAHP) to synthesis the key evaluation criteria for senior-friendly restaurants and analyses the weights of these criteria. It identifies and prioritises four main criteria and twenty sub-criteria in the hierarchical framework by employing the sophisticated approach.

The results indicate that the main criteria ranked by importance are “barrier-free environment”, “food quality”, “service quality” and “corporate social responsibility (CSR)”. There are five most important sub-criteria, such as “simple and intuitive use” and “perceptible information” belonging to the main criterion “barrier-free environment”, “hygiene and safety” and “food freshness” belonging to the main criterion “food quality” and “assurance” belonging to “service quality”. Incorporating the analytical findings, this study suggests the key evaluation criteria to facilitate the construction and development of senior-friendly restaurants.

The precisely hierarchical model and key criteria proposed in this study provide clear guidelines for managers of senior-friendly restaurants to develop feasible strategies and also contribute to the theoretical development of food-friendly environments and services for elderly consumers.

This purpose of this paper is to describe a fast and easy method of both clustering samples and identifying active genes in cDNA microarray data.

The method relies on alternation of identification of the active genes using a mixture model and clustering of the samples based on Ward hierarchical clustering. The initial‐point of the procedure is obtained by means of a χ² test. The method attempts to locally minimize the sum of the within cluster sample variances under a suitable Gaussian assumption on the distribution of data.

This paper illustrates the proposed methodology and its success by means of results from both simulated and real cDNA microarray data. The comparison of the results with those from a related known method demonstrates the superiority of the proposed approach.

Only empirical evidence of algorithm convergence is provided. Theoretical proof of algorithm convergence is an open issue.

The proposed methodology can be applied to perform cDNA microarray data analysis.

This paper provides a contribution to the development of successful statistical methods for cDNA microarray data analysis.

This research aimed to visualize and analyze the co-word network and thematic clusters of the intellectual structure in the field of linked data during 1900–2021.

This applied research employed a descriptive and analytical method, scientometric indicators, co-word techniques, and social network analysis. VOSviewer, SPSS, Python programming, and UCINet software were used for data analysis and network structure visualization.

The top ranks of the Web of Science (WOS) subject categorization belonged to various fields of computer science. Besides, the USA was the most prolific country. The keyword ontology had the highest frequency of co-occurrence. Ontology and semantic were the most frequent co-word pairs. In terms of the network structure, nine major topic clusters were identified based on co-occurrence, and 29 thematic clusters were identified based on hierarchical clustering. Comparisons between the two clustering techniques indicated that three clusters, namely semantic bioinformatics, knowledge representation, and semantic tools were in common. The most mature and mainstream thematic clusters were natural language processing techniques to boost modeling and visualization, context-aware knowledge discovery, probabilistic latent semantic analysis (PLSA), semantic tools, latent semantic indexing, web ontology language (OWL) syntax, and ontology-based deep learning.

This study adopted various techniques such as co-word analysis, social network analysis network structure visualization, and hierarchical clustering to represent a suitable, visual, methodical, and comprehensive perspective into linked data.

Document management is growing in importance proportionate to the growth of unstructured data, and its applications are increasing from process benchmarking to customer relationship management and so on. The purpose of this paper is to improve important components of document management that is keyword extraction and document clustering. It is achieved through knowledge extraction by updating the phrase document matrix. The objective is to manage documents by extending the phrase document matrix and achieve refined clusters. The study achieves consistency in cluster quality in spite of the increasing size of data set. Domain independence of the proposed method is tested and compared with other methods.

In this paper, a synset-based phrase document matrix construction method is proposed where semantically similar phrases are grouped to reduce the dimension curse. When a large collection of documents is to be processed, it includes some documents that are very much related to the topic of interest known as model documents and also the documents that deviate from the topic of interest. These non-relevant documents may affect the cluster quality. The first step in knowledge extraction from the unstructured textual data is converting it into structured form either as term frequency-inverse document frequency matrix or as phrase document matrix. Once in structured form, a range of mining algorithms from classification to clustering can be applied.

In the enhanced approach, the model documents are used to extract key phrases with synset groups, whereas the other documents participate in the construction of the feature matrix. It gives a better feature vector representation and improved cluster quality.

Various applications that require managing of unstructured documents can use this approach by specifically incorporating the domain knowledge with a thesaurus.

Experiment pertaining to the academic domain is presented that categorizes research papers according to the context and topic, and this will help academicians to organize and build knowledge in a better way. The grouping and feature extraction for resume data can facilitate the candidate selection process.

Applications like knowledge management, clustering of search engine results, different recommender systems like hotel recommender, task recommender, and so on, will benefit from this study. Hence, the study contributes to improving document management in business domains or areas of interest of its users from various strata’s of society.

The study proposed an improvement to document management approach that can be applied in various domains. The efficacy of the proposed approach and its enhancement is validated on three different data sets of well-articulated documents from data sets such as biography, resume and research papers. These results can be used for benchmarking further work carried out in these areas.