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Location-based services (LBS) have become an effective commercial marketing tool. However, regarding retail store location selection, it is challenging to collect analytical data. In this study, location-based social network data are employed to develop a retail store recommendation method by analyzing the relationship between user footprint and point-of-interest (POI). According to the correlation analysis of the target area and the extraction of crowd mobility patterns, the features of retail store recommendation are constructed.

The industrial density, area category, clustering and area saturation calculations between POIs are designed. Methods such as Kernel Density Estimation and K-means are used to calculate the influence of the area relevance on the retail store selection.

The coffee retail industry is used as an example to analyze the retail location recommendation method and assess the accuracy of the method.

This study is mainly limited by the size and density of the datasets. Owing to the limitations imposed by the location-based privacy policy, it is challenging to perform experimental verification using the latest data.

An industrial relevance questionnaire is designed, and the responses are arranged using a simple checklist to conveniently establish a method for filtering the industrial nature of the adjacent areas. The New York and Tokyo datasets from Foursquare and the Tainan city dataset from Facebook are employed for feature extraction and validation. A higher evaluation score is obtained compared with relevant studies with regard to the normalized discounted cumulative gain index.

The purpose of this paper is to explore the applicability of blockchain technology in international trade process from a perspective of letter of credit payment.

A blockchain-based re-engineering process is designed by employing the blockchain and its affiliated smart contract technology to harvest the benefits of distributed ledger and distributed business workflow automation.

Comparative analysis and feasibility study were conducted to identify and validate the prospects, in terms of facilitating process flow and enhancing overall trade performance, of the proposed blockchain-based international trade process model.

Traditional trade processes suffer from a great number of issues about intermediaries, information latency and trust, which, in turn, hinder overall process efficiency. The emerging blockchain technology may have potentials to mitigate those issues by revolutionizing business processes across enterprise borders in various industries.

This study contributes to the conceptual design of a blockchain- and smart-contract-based process along with a provision of practical case in business process re-engineering. Further endeavors devoted to blockchain research and application across different sectors are suggested to reach better performance of business process operations.

The basic assumption is that there is a symmetric relationship between review valence and rating, but what if review valence and rating were linked asymmetrically? There are few studies which have investigated the situations in which positive and negative online reviews exert different influences on ratings. This study considers brand strength as having an important moderating role because the average rating of existing reviews for a particular product is a heuristic cue for decision makers. Thus, the purpose of this paper is to argue that an asymmetric relationship between review content valence and numerical rating will depend on brand strength.

The authors have conducted a sentiment analysis via text mining, using self-developed computer programs to retrieve a data set from the TripAdvisor website.

This study finds there is an asymmetric relationship between review valence (verbal) and numerical rating. The authors further find brand strength to have an important moderating role. For a stronger brand, negative review content will have a greater impact on numerical ratings than positive review content, while for a weaker brand, positive review content will have a greater impact on numerical ratings than negative review content.

Marketers could adopt sentiment analysis via text mining of online reviews as a valid measure or predictor of consumer satisfaction or numerical ratings. Strong brands should direct more attention to negative reviews, because in such reviews the negative impact transcends the positive. In contrast, weak brands should aim to exploit as many positive reviews as possible to minimize the impact of any negative reviews.

This study finds there is an asymmetric relationship between review valence (verbal) and numerical rating and considers brand strength to play an important moderating role. The authors have used real data from the TripAdvisor website, which allow people to express themselves in an unsolicited manner, and linked these with the results from the sentiment analysis.

The purpose of this paper is to apply social network analysis (SNA) to study faculty research productivity, to identify key leaders, to study publication keywords and research areas and to visualize international collaboration patterns and analyze collaboration research fields from all Management Information System (MIS) departments in Taiwan from 1982 to 2015.

The authors first retrieved results encompassing about 1,766 MIS professors and their publication records between 1982 and 2015 from the Ministry of Science and Technology of Taiwan (MOST) website. Next, the authors merged these publication records with the records obtained from the Web of Science, Google Scholar, IEEE Xplore, ScienceDirect, Airiti Library and Springer Link databases. The authors further applied six network centrality equations, leadership index, exponential weighted moving average (EWMA), contribution value and k-means clustering algorithms to analyze the collaboration patterns, research productivity and publication patterns. Finally, the authors applied D3.js to visualize the faculty members' international collaborations from all MIS departments in Taiwan.

The authors have first identified important scholars or leaders in the network. The authors also see that most MIS scholars in Taiwan tend to publish their papers in the journals such as Decision Support Systems and Information and Management. The authors have further figured out the significant scholars who have actively collaborated with academics in other countries. Furthermore, the authors have recognized the universities that have frequent collaboration with other international universities. The United States, China, Canada and the United Kingdom are the countries that have the highest numbers of collaborations with Taiwanese academics. Lastly, the keywords model, system and algorithm were the most common terms used in recent years.

This study applied SNA to visualize international research collaboration patterns and has revealed some salient characteristics of international cooperation trends and patterns, leadership networks and influences and research productivity for faculty in Information Management departments in Taiwan from 1982 to 2015. In addition, the authors have discovered the most common keywords used in recent years.

This paper explains the formation of government resistance to open data based on the cognition of stakeholders in Chinese context.

Adopting grounded theory, this study interviewed 22 stakeholders to collect data. Through continuous coding, this study identified the main categories and then constructed theoretical models.

This study identified and explained five main categories that relate to resistance: practice, perception, organizational culture, motivation and resistance strategies. Then, a framework was developed that conceptualizes the government' resistance to open data. Based on the division of the forms of motivation, there are three paths through which government departments resist open data, namely, avoiding disadvantages to the organization, being unwilling to increase the workload and being unwilling to take the initiative.

This paper reveals the internal logic of government departments' resistance to open data and provides a process explanation beyond causality. It also provides a reference for other countries in the same preliminary stage as China with respect to open data implementation.

Rapid economic growth has enriched the lives of individuals and yielded rising material and living standards. However, various types of public pollution problems have successively emerged, and environmental problems have worsened in recent years. Furthermore, the prevalence of leisure and the rapid development of regional tourism industries and bed and breakfast lodging have exacerbated environmental destruction and pollution in various locales. The inclusion of ecotourism into ecological education and the provision of ecolodge are beneficial for the design of student learning outcomes; moreover, community residents and tourists can grasp the importance of environmental protection and education through ecolodging experiences. This measure would improve public awareness of environmental protection, facilitate the cultivation of social responsibility, and achieve the objective of environmental protection advocacy. Therefore, this study explored the relationship between ecological education and the environment by using Kenting National Park, a tourist attraction in Taiwan, as the case study. Tourists visiting the location were the research subjects, and convenience sampling was conducted by distributing 505 questionnaires, with 372 valid responses recovered—a return rate of 74%. The research revealed the following results: (a) Environmental education is positively correlated with environmental attitude. (b) Environmental attitude is positively correlated with environmental behavior. (c) Environmental education is positively correlated with environmental behavior. Finally, this paper proposes recommendations based on the research results with the aim of facilitating environmental literacy, correct environmental attitudes, concern for ecosystems, and the realization of environmental behavior.

Online reviews are increasingly available for a wide range of products and services in e-commerce. Most consumers rely heavily on online reviews when making purchase decisions, so an important topic is that of understanding what makes some online reviews helpful in the eyes of consumers. Researchers have demonstrated the benefits of the presence of customer reviews to an online retailer, however, few studies have investigated how images in review content and the facial expressions of reviewers’ avatars influence the judgment of online review helpfulness. This study draws on self-construal theory, attribution theory and affect-as-information theory to empirically test a model of the interaction effects of images in review content and the facial expressions of reviewers’ avatars on online review helpfulness. Furthermore, the purpose of this paper is to identify an underlying mechanism of causal attribution toward store performance on the above effects.

This study conducted two online experiments. Study 1 is a 2 (images in review content: one person with a product vs a group of people with a product) ×2 (facial expression of the reviewer’s avatar: happy vs angry) between-subjects design. Study 2 is a 3 (image: product alone vs one person with a product vs a group of people with a product) ×2 (facial expression of the reviewer’s avatar: happy vs angry) ×3 (valence of the review: positive vs negative vs neutral) between-subjects design.

The results indicate that when consumers were exposed to a happy-looking avatar, they were likely to express higher perceptions of online review helpfulness in response to an image showing a group of people in a restaurant than they would for an image of one person in the same situation. In contrast, when consumers were exposed to an angry-looking avatar, their perceptions of online review helpfulness did not differ in response to images of either a group of people or of one person. Furthermore, cause attribution toward store performance mediated the interaction between images in content of reviews and the facial expression of a reviewer’s avatar on the perceptions of online review helpfulness.

The authors provide insights into how to develop guidelines on how online reviews should be written so that readers perceive them to be helpful, and how to design effective reward mechanisms for customer feedback.

Compared with previous studies, this study provides further contributions in three ways. First, it contributes to the literature on review content by showing which images in reviews are deemed to be helpful. Second, it extends previous findings from the literature relating to online peer reviews by demonstrating the importance of facial expressions in reviewers’ avatars (i.e. happy vs angry) when explaining helpfulness, rather than the strength of purchase intent. Third, this study contributes by further highlighting a novel mechanism which shows that a causal attribution toward store performance motivates the perceptions of online review helpfulness.

Using social networks to identify users with traits similar to those of the target user has proven highly effective in the development of personalized recommendation systems. Existing methods treat all dimensions of user data as a whole, despite the fact that most of the information related to different dimensions is discrete. This has prompted researchers to adopt the skyline query for such search functions. Unfortunately, researchers have run into problems of instability in the number of users identified using this approach.

We thus propose the m-representative skyline queries to provide control over the number of similar users that are returned. We also developed an R-tree-based algorithm to implement the m-representative skyline queries.

By using the R-tree based algorithm, the processing speed of the m-representative skyline queries can now be accelerated. Experiment results demonstrate the efficacy of the proposed approach.

Note that with this new way of finding similar users in the social network, the performance of the personalized recommendation systems is expected to be enhanced.

The aim of this study is to examine the tracking performance and tracking error (TE) of New Zealand exchange traded fsunds (ETFs).

The authors use regression methods and cointegration analysis to examine tracking performance. Multivariate regressions are used to examine the determinants of TE.

At the daily frequency, the authors observe that the ETFs have substantially different exposures to their underlying indexes from what they should be, which is confirmed by cointegration analysis. At the monthly frequency, tracking performance improves but still shows significant differences between the ETF and its underlying index. When the authors examine the TEs of the ETFs, the authors observe that these are substantial and that there is considerable variation in TE. Regression analysis shows that both characteristics of the ETF and the constituents of the index the ETF tracks, as well as the volatility of the underlying benchmark are determinants of the TE of the ETFs.

This is the first study to examine New Zealand-based ETFs. The findings contribute to understanding the performance of these ETFs and are of relevance to academics, investors and the ETF provider.

This paper aims to present simulation results of a harmonic drive (HD) with involute flexspline (FS) profiles based on two-dimensional (2-D) finite element analysis (FEA).

First, the mathematical model of the FS with involute tooth profile was developed using a straight-edge rack cutter based on the theory of gearing. Then the engaging circular spline (CS) with conjugate tooth profile of FS was derived based on the enveloping theory and theory of gearing. Additionally, a mesh generation program was developed to discretize the FS based on the mathematical model. An elliptical wave generator (WG) was inserted into the FS, and a torque was applied to drive the FS meshing with the CS. The WG and the CS were both assumed to be rigid in the finite element model.

Finally, a 2-D FEA was conducted to explore the stress distribution on the FS, the engagement movement of the FS, the torsional stiffness and the engaged area of teeth of the HD under various conditions. Moreover, this research also studied the effect of changing pressure angle of the involute FS on the performance of the HD.

The simulation model and methodology presented in this paper paved the way for further investigation and optimization of the HD with involute tooth profile FS and conjugate CS.

The simulation model of HD is established on conjugate shape based on the theory of gearing and an automatic mesh generation program is developed to generate the finite element model. The characteristics of the HD can thus be simulated according to the developed model.