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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.

The purpose of this paper is to examine the antecedents of ethical leadership by drawing on the theory of planned behavior (TPB). The authors hypothesized that a leader’s attitude toward ethical behavior, subjective norm about ethical behavior and perceived behavioral control relate to his/her ethical intention and subsequently to the follower’s perceptions of ethical leadership.

The authors found general support for the model using data collected from a two-wave and two-source field study involving 119 supervisor-subordinate dyads.

The results demonstrated that the leader’s favorable attitude toward ethical behavior and perceived behavioral control predicted his/her ethical intention and subsequently to the follower’s perception of ethical leadership, whereas the subjective norm did not.

The findings of the study provide important insights into developing relevant training and intervention programs in organizations to cultivate ethical leadership. These can be done by encouraging leaders’ ethical intentions through changing their attitudinal and control beliefs regarding ethical behavior. Study findings also provide important insight on developing the recruiting device in a way that would help selecting individuals who may have favorable beliefs toward ethical behavior and thus have the potential to be an ethical leader.

This study first demonstrates the applicability of the TPB in examining the antecedents of ethical leadership.

Members’ sustained participation positively influences success of brand community. Although scholars have confirmed the effects of social capital on continuance intention in third-party hosted communities, little work has been done to explore these relationships in context of enterprise-sponsored brand communities, especially, the precursors of active members’ sustained participation in such context is still unclear. Besides, how to recognize active users with high precision and coverage remains an open question. Therefore, this paper aims to propose a novel method to identify active users effectively and investigate the antecedents of their continuance intention from perspective of social capital in enterprise-sponsored brand community.

This work established several social networks based on the information of Xiaomi smartphone forum users’ posts and feedbacks. Node centrality (out-degree) analysis was adopted to identify the users with high degree of active in these networks, and then behaviour analysis was performed to exclude the community managers from the group of active users. Finally, a research model was proposed based on the theory of social capital. It was tested by applying partial least squares technique, and the data were collected from a survey of members (n = 327) of Xiaomi forum.

The empirical results showed that the proposed method can recognize the active users effectively. Additionally, social tie, identification, trust and shared vision were proved to be significant predictors of active users’ continuance intention in the context of enterprise-sponsored brand community.

This paper contributes to the information system usage literature and provides opinions regarding how social capital influence active users’ sustained participation in enterprise-sponsored brand community. Besides, this work proposed a novel method to identify active users, which will be useful to assist enterprises to improve their community management.

Many users build personal projects in co-innovation community to accomplish their innovations. However, very few projects from such communities are successful and understanding of this phenomenon is limited. The purpose of this paper is to identify the factors facilitating user projects success in online co-innovation communities.

Based on the theories of persuasion and diffusion of innovation (DOI), a conceptual model is proposed to explain how project success likelihood is affected by the creator, project and user participation characteristics. Then, the model and hypotheses are tested through binary logistic regression on a secondary data set of 572 projects collected from a typical user co-innovation community, Local Motors.

The results show that creator characteristics (prior success rate), project characteristics (project popularity, length and duration) and user participation characteristics (participation users and degree) have significant and positive impacts on project success likelihood. The number of prior projects, which can hardly represent the creator’s credibility in open and unrestricted situations, has no significant influence on the project success likelihood.

This study offers project creators the keys to increase their projects successful possibility. Besides, this study recommends a new way to attract users and helps to identify creative and effective users for community practitioners.

This study expands the research scope in online co-innovation community by focusing on user personal projects. In addition, it combines persuasion theory and DOI theory to add the holistic understanding of user project success likelihood.

Intent detection and slot filling are two important tasks in question comprehension of a question answering system. This study aims to build a joint task model with some generalization ability and benchmark its performance over other neural network models mentioned in this paper.

This study used a deep-learning-based approach for the joint modeling of question intent detection and slot filling. Meanwhile, the internal cell structure of the long short-term memory (LSTM) network was improved. Furthermore, the dataset Computer Science Literature Question (CSLQ) was constructed based on the Science and Technology Knowledge Graph. The datasets Airline Travel Information Systems, Snips (a natural language processing dataset of the consumer intent engine collected by Snips) and CSLQ were used for the empirical analysis. The accuracy of intent detection and F1 score of slot filling, as well as the semantic accuracy of sentences, were compared for several models.

The results showed that the proposed model outperformed all other benchmark methods, especially for the CSLQ dataset. This proves that the design of this study improved the comprehensive performance and generalization ability of the model to some extent.

This study contributes to the understanding of question sentences in a specific domain. LSTM was improved, and a computer literature domain dataset was constructed herein. This will lay the data and model foundation for the future construction of a computer literature question answering system.

The cleaning of food production equipment using cleaning detergents may contaminate the lubricant of the bearings, thereby reducing the bearing service life. The purpose of this paper is to investigate the cause and mechanism of such damage of bearings lubricated by cleaning detergent/water-in-oil emulsions.

The emulsion was prepared by adding a mixture of cleaning detergent and water in one base oil. A self-designed ball-on-disc optical interference test rig was applied to examine the effect of emulsion on lubrication and wear of bearing contacts under pure sliding conditions.

The emulsion reduced lubricating film thickness at a relatively low-sliding speed but only when the water concentration (20%) in emulsion was high. Water droplets were trapped around the ball-on-disc contact area under static conditions because of a high capillary force. The emulsion can induce damages on the soft surface in the startup mainly due to the presence of water around the contact.

The basic lubrication behaviour of water/oil emulsions containing cleaning detergent under pure sliding was experimental studied and the mechanism of bearing damage in food production equipment was investigated. Based on the study, the solution to avoid such damage was proposed.

This paper aims to measure Chinese regional thermal industries’ evolution.

This paper uses data envelopment analysis (DEA) and global Malmquist–Luenberger productivity (GMLP) index.

The results reveal that the development of Chinese thermal power industry varies significantly in different regions, and it is highly correlated with the level of local economic development. Although the change of technical efficiency and scale efficiency had different impacts on different regions from year to year, the overall GMLP index change shows a close relationship with the contemporaneous frontier shift.

The results indicate that the Chinese Government should make efforts to promote its policy implementations and regulations in thermal industries so that the contemporaneous frontier will shift toward the global technology frontier with more desirable outputs and less undesirable outputs.

As an application, this study uses DEA and GMLP index to measure the productivity of Chinese thermal industries in 30 Chinese provinces from 2006 to 2013. The results have the meaningful policy implications for decision makers in charge of Chinese thermal industries.

The high-quality development of the real estate industry is crucial to the transformation of China's economy. However, few studies apply the productivity to explore the development path of the real estate industry in China. To fill this gap, this study mainly investigates the total factor productivity (TFP) of the real estate industry of 30 sample provinces in mainland China from 2007 to 2016.

The Malmquist index is applied to estimate the productivity (i.e. TFP) of the real estate industry, based on the data envelopment analysis (DEA). Then, the truncated tobit regression analysis explores the external influencing factors on the TFP of the real estate industry.

Through empirical analysis, it is found that the high-quality development of the real estate industry depends on the technological innovation by the real estate enterprises and the targeted policies by the provincial government. Moreover, the development of the real estate industry has a positive correlation with the growth of China's economy but a negative correlation with the development of other industries.

TFP mainly reveals the development status of the provincial real estate industry and identifies the driving force for exploring the high-quality development mode of the real estate sector. Furthermore, the fluctuation rule of TFP can be applied to predict the development trend of the real estate industry in the future.

As an application, this study measures the TFP of the Chinese real estate industry in different provinces and periods. The results have meaningful policy implications for policymakers regulating the real estate industry.

This study aims to analyze the tradeoff between two potential marketing strategies for service providers, namely, market segmentation on the basis of reservation lead time and cooperation with third parties.

This paper proposes an optimization model to describe market segmentation strategy after cooperating with third parties by taking hotels for example.

The results show that the profitability of adopting two strategies simultaneously is lower than that with market segmentation alone under some cases, which is relevant with attributes of travel agencies, such as switch rate and market share.

This study indicates that cooperation with third parties has a negative impact on profitability of hotels using market segmentation in some cases. However, randomness of demand, customer loyalty and existence of cancellation should be considered in further research.

In an e-commerce era, hotels with market segmentation based on reservation lead time, are not required to cooperate with third parties under a number of situations (e.g. high switch rates and small market sizes of travel agencies). In addition, hotels should revise the segmentation strategy based on the change rate of potential demand of individual customers. Furthermore, hotels should enhance customer loyalty, strengthen cooperation with travel agencies that possess large market shares or small switch rates.

The study preliminarily formulates the optimal market segmentation strategy on the basis of reservation lead time after cooperating with third parties, which contributes to the revenue management.

The purpose of this paper is to adopt a behavioural approach to explain how the internet use influences stock market participation (SMP) decisions.

Drawing on the literature on sociability and SMP, this paper analyses whether virtual sociability affects SMP decision in a sample of 34,715 individuals in 14 European countries.

The results show that internet users are more likely to be stockowners. However, the obtained evidence does not support either an informational effect or a social multiplier effect of the virtual sociability. After controlling by the country’s SMP rates, a positive effect of internet usage on SMP decision remains, suggesting that contextual factors matter rather than internet usage per se. Thus, in countries where individuals are “used” to being stockholders, the habit of using internet increases SMP, but the “breeding ground” is a necessary condition.

The massive use of the internet provides a valuable opportunity to find evidence of the frictional costs which would act as inhibitors of the SMP, as economic theory hypothesised. After some promising results, the differences in the evolution of both the SMP and internet usage rates have not confirmed the initial enthusiasm. In addition, the question of why the SMP rates systematically differ across countries still remains open.