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The purpose of this paper is to investigate value‐relevant information provided by intellectual capital (IC) beyond financial performance under different life‐cycle stages.

The life‐cycle classification method and the residual income model are used to examine the information technology industry.

The empirical results show that the value‐relevant information provided by IC under the growth, maturing, and stagnant stages can be ranked in order (from high to low) of custom, innovation, process, and human capital. Specifically, the empirical results indicate that overall IC provided the most value‐relevant information in the stagnant stage and the lowest value‐relevant information in the growth stage.

This paper reveals that evaluating the company market value merely by financial performance involves a number of limitations, thereby requiring IC to supplement the process. The internet downturn in the mid‐2000s might have likewise affected the categorization of life‐cycle stages and the value‐relevant information provided by intellectual capital during the “bubble” period.

Managers and investors should not merely focus on financial performance as the main value‐relevant information, but a thorough review of IC in different life‐cycle stages should be made in order to avoid making incorrect decisions.

This paper contributes to the existing literature by exploring customer, process, innovation, and human capital which, significantly, have different value‐relevant information in different life‐cycle stages.

The purpose of this paper is to investigate whether value‐creating activities and intellectual capital (IC) accumulation are affected by different business models.

Field visitations and interview‐based questionnaires are used for data collection. This study uses the structural equation model to examine Taiwanese original equipment manufacturers (OEMs) and original brand manufacturers (OBMs) in China.

Empirical results show that Taiwanese OEMs and OBMs adopt different combinations of value‐creating activities, which results in differences in IC accumulation. Taiwanese OEMs have engaged in manufacturing and innovation activities, and have created process and innovation capitals. By contrast, Taiwanese OBMs have developed their marketing channels, human resources, innovation centres, and social networks, and have accumulated their human, customer, process and innovation capitals.

Taiwanese OEMs have cultural advantages and have built productive infrastructure in China. Therefore, these enterprises should transform their business models into OBMs to enhance their market performance. Foreign investors could leverage the experiences and IC of Taiwanese enterprises to make their investments run more smoothly.

This paper contributes to the existing literature by investigating relationships among business models, value‐creating activities, and IC. This study also provides useful guidance for enterprises considering investing in China and for academics researching in this area.

As more women are now being appointed to senior and top management positions and invited to sit on boards of directors, they are now directly participating in strategic company decision-making. As female directors have been found to provide new ideas, increase company competitiveness, efficiency and performance and bring a greater number of external resources to a company than male directors, this paper aims to put female directors as a variable into the data envelopment analysis (DEA) and statistical models to explore the effect of female directors on operating performances. The DEA first quantified and measured the company efficiencies, after which the statistical model analyzed the correlations between the variables to specifically identify the impact of female decision makers on the operating efficiencies in state-owned and private enterprises.

A novel two-stage, meta-hybrid dynamic DEA was developed to explore Chinese cultural media company efficiencies under optimal input and output resource allocations, after which Tobit Regression was applied to determine the effect of female executives on these efficiencies.

From 2012 to 2016, the overall efficiencies in Chinese state-owned cultural media enterprises were better than in the private cultural media enterprises. The overall technology gaps (TGs) in the state-owned cultural media enterprises were better than in the private cultural media enterprises.

Previous research has tended to focus on the causal relationships between female senior executives and business performances; however, there have been few studies on the relationships between female executives and company performance from an efficiency perspective (optimal resource allocation). This paper, therefore, is the first to develop a novel two-stage, meta-hybrid dynamic DEA to examine Chinese cultural media enterprise efficiencies, and the first to apply Tobit Regression to assess the effect of female executives on those efficiencies.

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

Based on theories of organizational learning and strategic considerations, this study aims to develop a strategic contingency model for technology alliance and identify how alliance‐specific factors, strategic factors, and organizational capability factors influence firms to acquire competencies and competitive advantages through technology alliance.

A six‐page, 94‐item survey questionnaire was developed and mailed to top‐level managers of the semiconductor firms in Taiwan. A total of 63 valid responses were received.

The study results indicate that firms with higher absorption orientation, higher risk reduction orientation, higher R&D scale economy orientation, and higher top management team experiences tend to perform better in acquiring competitive advantages. In addition, the strategic fit between strategic factors, organizational capability factors and technology alliance choice could lead firms to better competitive advantage.

Although the results of this study are fruitful, several suggestions could be made for academicians and business practitioners. First, the respondent rate of this study is low and could be improved. Second, in addition to the strategic contingency model as developed in this study, more research factors could be further investigated. Third, more case studies could be conducted to reconfirm the results of this study.

The major contribution of this study is to investigate what critical factors would influence the choice of a technology alliance model, and what effects the influencing factors have on the relationship between a technology alliance model and the intended competency development. The results of this study provide very important references for academicians and practitioners to investigate the effectiveness of technology alliance.

The combination of improved PSO (IPSO) algorithm and artificial neural network (ANN) model for intelligent monitoring of the bearing performance of the hydrostatic turntable.

This paper proposes an artificial neural network model based on IPSO algorithm for intelligent monitoring of hydrostatic turntables.

The theoretical model proposed in this paper improves the accuracy of the working performance of the static pressure turntable and provides a new direction for intelligent monitoring of the static pressure turntable. Therefore, the theoretical research in this paper is novel.

Theoretical novelties: an ANN model based on the IPSO algorithm is designed to monitor the load-bearing performance of a static pressure turntable intelligently; this study show that the convergence accuracy and convergence speed of the IPSO-NN model have been improved by 52.55% and 10%, respectively, compared to traditional training models; and the proposed model could be used to solve the multidimensional nonlinear problem in the intelligent monitoring of hydrostatic turntables.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0081/

Drawing upon the Elaboration Likelihood Model, the purpose of this paper is to examine how the characteristics of social media moderate the effect of a firm’s apology on the attitude of its customers.

An online experiment including 360 active users of internet was employed to test the research model.

Results revealed that an after-crisis apology and firm reputation both have a positive effect on after-crisis user attitude toward the firm. Furthermore, the positive effect of apology becomes stronger as online media interactivity increases, whereas the positive effect of reputation becomes weaker.

This study included only one important characteristic of social media, and experimental scenarios were limited to car recall crisis. Considering that social media has so many platforms that may have different kinds of interactivity, further studies can be conducted to figure out the most suitable social media for firms to deal with an online crisis.

The results inform managers of the importance of after-crisis apology and firm reputation. It is worthwhile for managers to find out the levels of online media interactivity at which users focus on apology and reputation and accordingly conduct an effective online crisis management response strategy.

This study extends the literature on online crisis management and the literature on ELM by highlighting the role of online media interactivity in influencing the persuasive effectiveness of firm’s crisis response in the context of social media.

The purpose of this paper is to examine the antecedents of supply chain integration (SCI) at the product level. More specifically, it aims to show the relationship between product-level characteristics (i.e. product complexity and product variety) and different dimensions of SCI (i.e. internal, supplier and customer integration).

A survey-based research design is developed to measure different dimensions of SCI, product complexity and product variety. The authors use structural equation modelling to test the related hypotheses.

This research shows that internal integration is an enabler to supplier and customer integration. The results also show that under high product complexity, firms tend to implement internal and supplier integration, while product complexity does not have a direct impact on customer integration. Product variety is confirmed to be positively related to all dimensions of SCI.

This paper contributes to the SCI literature by first, providing empirical evidence which supports the study of the product design-supply chain interface; and second, exploring the relationships between product complexity, variety and internal, supplier and customer integration based on a governance view.

By expanding on the work of White and Yanamandram (2007), the purpose of this paper is to examine the direct and indirect influences of switching barriers on the relationship between recovery satisfaction and repurchase intentions in an online auction environment.

Data were collected from 272 customers who had experienced online service recovery in the past six months. Partial-least squares and mediated moderation analysis are employed to test the research model.

The interrelationships among recovery satisfaction, relationship quality, and repurchase intentions are confirmed. Both lost benefit switching costs and inertia moderate the relationship between recovery satisfaction and repurchase intentions. Attractiveness of alternatives mediates the moderating effect of inertia on the relationship between recovery satisfaction and repurchase intentions.

Unlike previous studies, which have treated switching cost as a switching barrier, or used various components to represent switching barriers, this study incorporates switching cost, relationship quality, inertia, and attractiveness of alternatives as four switching barrier factors. This study further examines the direct and indirect effects of switching barriers on the relationship between recovery satisfaction and repurchase intentions.

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.