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The purpose of this paper is to examine information-seeking behavior (ISB) of strategic planners in enterprise across different work-task types and stages.

A case study was conducted in a pharmaceutical company in China, labeled as T Company. One of the authors worked in the department of strategic planning of this company as an intern. The data were collected via participant observation and unstructured in-depth interviews. Open coding was performed to analyze the data.

Four work-task stages were identified: project preparation, gathering, discovery and presentation, and strategy formulation. The results indicate that work-task types, work-task stages, and strategic planners’ work role or position affect their information needs, source selection, and seeking process. Task complexity, task familiarity, and task goal are of the most important task attributes that directly shape strategic planners’ ISB. Work role determines the extent to which strategic planners can access the information of the company. Internal information has priority, but external information is also important when internal information is not sufficient; both are equally important for strategic planning projects. Social media has been a very important channel to access, disseminate and share information. Workshops are an important approach to producing final project reports. Face-to-face discussion and information exchange play a critical role in the formulation of new strategies.

This is a case study with data collected from only one company in China. Some of the results may not be generalizable. However, it adds new knowledge to ISB research in enterprise, informs people how to provide better information services for strategic planners, and informs MBA education for students’ better information-seeking skills.

Though myriad studies on ISB, little research has been done to examine strategic planners’ ISB from a business context, especially taking into account the effect of work-task types and stages.

The purpose of this paper is to scrutinize the performance effects of supply chain risk management (SCRM). Besides financial performance, two aspects of operational performance are examined: operational efficiency and flexibility. Moreover, the authors explore the moderating role of supplier integration in the relationship between SCRM and operational performance.

A survey-based methodology was adopted. Based on the data from an international survey, this study applied the structural equation modeling and latent moderated structural equations approach to test the hypotheses.

The results indicate that SCRM positively influences both operational efficiency and flexibility, and has an indirect effect on financial performance. In addition, supplier integration enhances the impact of SCRM on operational flexibility, but does not moderate the relationship between SCRM and operational efficiency.

This study extends the existing literature by providing a comprehensive analysis of the performance effects of SCRM. It also provides managerial insights on both risk management and supplier integration.

The risks associated with digital innovation increasingly challenge value co-creation among stakeholders within the innovation ecosystem. Stakeholder collaboration is helpful in preventing risk occurrence. This study intends to explore the effects of different stakeholder collaboration strategies on risk prevention performance in a digital innovation ecosystem context.

A systematic literature analysis was first conducted to identify risk factors of digital innovation based on the technology–organization–environment (TOE) framework. Then, a bidimensional network model was constructed to visualize the collaborative relationships among stakeholders and the identified risks by focusing on a digital innovation case. The social network analysis method was applied to design stakeholder collaboration strategies from the ego and global network perspectives, and a simulation approach was conducted to evaluate the effects of the strategies on risk prevention performance.

The results validate the positive effect of stakeholder collaboration on risk prevention performance and reveal the important role of network reachability in formulating collaboration strategies. The strategy of strong–strong collaboration strategy can best enhance risk prevention performance like a “Matthew effect” in the digital innovation ecosystem.

First, risk identification based on the TOE framework provides a systematic list of risk factors for future digital innovation risk management research. Second, this study designs stakeholder collaboration strategies from a network perspective to enhance the understanding of the network status of each stakeholder and the network structure of the digital innovation ecosystem. Third, the simulation results reveal the effects of different collaboration strategies on risk prevention performance.

This study aims to investigate users’ adoption of bike sharing systems in China.

This research combined perceived risk factors with existing technology diffusion theories (e.g. technology acceptance model and unified theory of acceptance and use of technology) to develop a research model to examine users’ adoption of bike sharing systems in China. As a result, a research model with 11 hypotheses was developed. The developed research model was empirically tested using data collected from a survey of 298 users in China. Structural equation modeling was used to analyze the collected data.

The findings indicated that perceived usefulness, facilitating conditions and perceived risks were important determinants to the adoption of bike sharing systems. However, perceived ease of use and social influence did not have significant positive impacts on users’ behavioral intention to use bike sharing systems.

It is important for service providers to dedicate their time and efforts in maintaining and repairing bikes to ensure that the bikes are in a good condition to be used. System providers need to work on good solutions to better protect users’ personal information and location information.

This study is first of its kinds in investigating the adoption of bike sharing systems by combining technology diffusion theories and perceived risk theory in China.

本论文旨在研究用户在中国使用共享单车系统的情况。

本论文结合感知风险因素和多个技术扩散理论（比如TAM, UTAUT）来开发一个研究模型, 以研究用户在中国使用共享单车系统的情况。因此, 本论文用十一条假设搭建了一个研究模型。这个研究模型使用问卷采样方式, 收取298份中国用户问卷, 来进行测量。样本通过结构方程模型来进行分析测量。

研究结果表明, 感知实用性、辅助条件、感知风险等是重要因素, 决定着共享单车系统的使用。然而, 方便使用和社会影响等因素对用户使用共享单车系统的意向并没有显著积极影响。

研究型论文

服务提供者投入时间和精力来维护维修单车是非常重要的, 这样能保证单车保持在良好的状态以备用户使用。系统供应商需要找到好的解决方式, 来更好地保护个人信息和地点信息。

本论文是首个类似论文, 结合技术扩散理论和感知风险理论, 在中国研究共享单车系统的使用情况。

UTAUT, TAM, 感知风险, 共享单车系统,使用

In community question answering (CQA), people who answer questions assume readers have mastered the content in the answers. Nevertheless, some readers cannot understand all content. Thus, there is a need for further explanation of the concepts that appear in the answers. Moreover, the large number of question and answer (Q&A) documents make manual retrieval difficult. This paper aims to alleviate these issues for CQA websites.

In the paper, an algorithm for recommending explanatory Q&A documents is proposed. Q&A documents are modeled with the biterm topic model (BTM) (Yan et al., 2013). Then, the growing neural gas (GNG) algorithm (Fritzke, 1995) is used to cluster Q&A documents. To train multiple classifiers, three features are extracted from the Q&A categories. Thereafter, an ensemble classification model is constructed to identify the explanatory relationships. Finally, the explanatory Q&A documents are recommended.

The GNG algorithm shows good clustering performance. The ensemble classification model performs better than other classifiers. The both effect and quality scores of explanatory Q&A recommendations are high. These scores indicate the practicality and good performance of the proposed recommendation algorithm.

The proposed algorithm alleviates information overload in CQA from the new perspective of recommending explanatory knowledge. It provides new insight into research on recommendations in CQA. Moreover, in practice, CQA websites can use it to help retrieve Q&A documents and facilitate understanding of their contents. However, the algorithm is for the general recommendation of Q&A documents which does not consider individual personalized characteristics. In future work, personalized recommendations will be evaluated.

A novel explanatory Q&A recommendation algorithm is proposed for CQA to alleviate the burden of manual retrieval and Q&A overload. The novel GNG clustering algorithm and ensemble classification model provide a more accurate way to identify explanatory Q&A documents. The method of ranking the explanatory Q&A documents improves the effectiveness and quality of the recommendation. The proposed algorithm improves the accuracy and efficiency of retrieving explanatory Q&A documents. It assists users in grasping answers easily.

The purpose of this paper is to investigate the hydrodynamic performance of liquid film seals with oblique grooves (OGs) and spiral grooves (SGs), considering cavitation, compare and analyze the differences between them.

Considering cavitation effect, the incompressible steady-state Reynolds equation was solved to obtain the sealing performance parameters of the liquid film seal with oblique groove and spiral groove.

The hydrodynamic performance of oblique groove seal (OGS) and spiral groove seal (SGS) shows a similar trend with the change of operating parameters. When the groove angle is less than 20°, the load-carrying capacity of SGS is better than that of OGS, while when the groove angle continues to increase, the hydrodynamic performance of OGS is slightly better than that of SGS, and more suitable for use under small differential pressure and high speed.

The hydrodynamic characteristics of liquid film seals with oblique grooves and spiral grooves considering cavitation effect were studied, which provides a theoretical reference for the application of oblique groove seal.

While the bring-your-own-device (BYOD) trend provides benefits for employees, it also poses security risks to organizations. This study explores whether and how employees decide to adopt BYOD practices when they encounter information security–related conflict.

Using survey data from 235 employees of Chinese enterprises and applying partial least squares based structural equation modeling (PLS-SEM), we test a series of hypotheses.

The results suggest that information security–related conflict elicits information security fatigue among employees. As their information security fatigue increases, employees become less likely to adopt BYOD practices. In addition, information security–related conflict has an indirect effect on employee's BYOD adoption through the full mediation of information security fatigue.

This study provides practical implications to adopt BYOD in the workplace through conflict management measures and emotion management strategies. Conflict management measures focused on the reducing of four facets of information security–related conflict, such as improve organization's privacy policies and help employees to build security habits. Emotion management strategies highlighted the solutions to reduce fatigue through easing conflict, such as involving employees in the development or update of information security policies to voice their demands of privacy and other rights.

Our study extends knowledge by focusing on the barriers to employees' BYOD adoption when considering information security in the workplace. Specifically, this study takes a conflict perspective and builds a multi-faceted construct of information security–related conflict. Our study also extends information security behavior research by revealing an emotion-based mediation effect, that of information security fatigue, to explore the mechanism underlying the influence of information security–related conflict on employee behavior.

This paper aims to demonstrate a cloud-based version of the improved Monte Carlo localization algorithm with robust orientation estimation (IMCLROE). The purpose of this system is to increase the accuracy and efficiency of indoor robot localization.

The cloud-based IMCLROE is constructed with a cloud–client architecture that distributes computation between servers and a client robot. The system operates in two phases: in the offline phase, two maps are built under the MapReduce framework. This framework allows parallel and even distribution of map information to a cloud database in pre-described formats. In the online phase, an Apache HBase is adopted to calculate a pose in-memory and promptly send the result to the client robot. To demonstrate the efficiency of the cloud-based IMCLROE, a two-step experiment is conducted: first, a mobile robot implemented with a non-cloud IMCLROE and a UDOO single-board computer is tested for its efficiency on pose-estimation accuracy. Then, a cloud-based IMCLROE is implemented on a cloud–client architecture to demonstrate its efficiency on both pose-estimation accuracy and computation ability.

For indoor localization, the cloud-based IMCLROE is much more effective in acquiring pose-estimation accuracy and relieving computation burden than the non-cloud system.

The cloud-based IMCLROE achieves efficiency of indoor localization by using three innovative strategies: firstly, with the help of orientation estimation and weight calculation (OEWC), the system can sort out the best orientation. Secondly, the system reduces computation burden with map pre-caching. Thirdly, the cloud–client architecture distributes computation between the servers and client robot. Finally, the similar energy region (SER) technique provides a high-possibility region to the system, allowing the client robot to locate itself in a short time.