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Plantar force is the interface pressure existing between the foot plantar surface and the shoe sole during static or dynamic gait. Plantar force derived from gait and posture plays a critical role for rehabilitation, footwear design, clinical diagnostics and sports activities, and so on. This paper aims to review plantar force measurement technologies based on piezoelectric materials, which can make the reader understand preliminary works systematically and provide convenience for researchers to further study.

The review introduces working principle of piezoelectric sensor, structures and hardware design of plantar force measurement systems based on piezoelectric materials. The structures of sensors in plantar force measurement systems can be divided into four kinds, including monolayered sensor, multilayered sensor, tri-axial sensor and other sensor. The previous studies about plantar force measurement system based on piezoelectric technology are reviewed in detail, and their characteristics and performances are compared.

A good deal of measurement technologies have been studied by researchers to detect and analyze the plantar force. Among these measurement technologies, taking advantage of easy fabrication and high sensitivity, piezoelectric sensor is an ideal candidate sensing element. However, the number and arrangement of the sensors will influence the characteristics and performances of plantar force measurement systems. Therefore, it is necessary to further study plantar force measurement system for better performances.

So far, many plantar force measurement systems have been proposed, and several reviews already introduced plantar force measurement systems in the aspect of types of pressure sensors, experimental setups for foot pressure measurement analysis and the technologies used in plantar shear stress measurements. However, this paper reviews plantar force measurement systems based on piezoelectric materials. The structures of piezoelectric sensors in the measurement systems are discussed. Hardware design applied to measurement system is summarized. Moreover, the main point of further study is presented in this paper.

Cryptocurrency, an important application of blockchain technology, has gradually circulated, and its use has become widespread. While cryptocurrency is growing rapidly, potential risks are simultaneously emerging. Users thus may abandon their usage behavior of cryptocurrency, hindering the future development of cryptocurrency. While prior studies focus more on the intention to use cryptocurrency in the pre-adoption phase, less studies pay attention to discontinuance usage intention in the post-adoption phase. To fill this knowledge gap, this stfudy aims to explore factors that cause discontinuance usage intention regarding cryptocurrency.

Based on the net valence framework theoretically grounded on the theory of reason action, a dilemmatic dual-factor model is proposed to figure out cryptocurrency users' discontinuance usage intention from the perceived risk and perceived benefit. This study identifies four potential risks and three potential benefits that affect perceived risk and benefit. The model with nine hypotheses were developed, and research data were collected by a survey method. A total of 343 valid responses were received, and PLS-SEM with SmartPLS was utilized to test the nine hypotheses, with seven hypotheses supported empirically.

Our findings demonstrate that financial, legal and operational risks are critical to increase users' perceived risk, and perceived usefulness and seamless transactions play important roles in enhancing users' perceived benefit. Moreover, while perceived risk can increase users' discontinuance usage intention to cryptocurrency, perceived benefit can mitigate such intention.

This study contributes nascent knowledge to the literature by examining factors that influence discontinuous usage intention in regard to cryptocurrencies, to firms that have issued or attempted to issue cryptocurrencies and to the potential users of cryptocurrencies by adjusting the mode of operation and investment strategies and reducing user costs, achieving a win-win situation for firms and users.

Customer lifetime value (CLV) scoring is highly effective when applied to marketing databases. Some researchers have extended the traditional association rule problem by associating a weight with each item in a transaction. However, studies of association rule mining have considered the relative benefits or significance of “items” rather than “transactions” belonging to different customers. Because not all customers are financially attractive to firms, it is crucial that their profitability be determined and that transactions be weighted according to CLV. This study aims to discover association rules from the CLV perspective.

This study extended the traditional association rule problem by allowing the association of CLV weight with a transaction to reflect the interest and intensity of customer values. Furthermore, the authors proposed a new algorithm, frequent itemsets of CLV weight (FICLV), to discover frequent itemsets from CLV-weighted transactions.

Experimental results from the survey data indicate that the proposed FICLV algorithm can discover valuable frequent itemsets. Moreover, the frequent itemsets identified using the FICLV algorithm outperform those discovered through conventional approaches for predicting customer purchasing itemsets in the coming period.

This study is the first to introduce the optimum approach for discovering frequent itemsets from transactions through considering CLV.

This study aims to examine the moment-to-moment (MTM) effects of in-consumption dynamic comments on consumers' responses to digital engagement and the underlying mechanisms involved, as well as the interactive role of advertisements embedded in short-form online video.

This study uses data extracted from 2,081 videos posted on the prominent Chinese online live platform, Bilibili. The hypotheses are tested using regression models and natural language processing.

The results indicate that the intensity of live comments at the beginning negatively affects users' digital engagement, while a corresponding increase in live comments at the end elicits a positive effect. A linear trend and peak difference in live comments intensity positively affect digital engagement, while the variability of live comment intensity exerts a negative effect. These MTM effects were driven by sentiments of live comments. Furthermore, in-video advertisements are likely to amplify the negative beginning effect on users' digital engagement and mitigate the negative variability of live comments.

This study is the first to examine the direct effects of MTM comments from the online temporal sequence perspective, differentiating the process- and performance-based engagement. The mechanism and interactive role of in-video advertisements were identified. These findings contribute to literature on interactive marketing and provide valuable guidance for influencer marketing.

Digital transformation (DT) has great potential to improve operations and stimulate business growth. It has attracted widespread attention from businesses around the world. However, firm performance in DT implementation (DTI) cannot be neglected. This study examines the benefits and costs of DT and its effects on operations and financial performance of DT.

Based on the financial report data from S&P 500 firms, we measure the financial performance, productivity and market value of DTI with a baseline in 2020. In addition, we collect secondary data to construct linear regression models for three dummy variables, including DT adopters, first movers and better performers.

Empirical results demonstrate that digitally transformed firms are positively correlated with business performance, while the performance among first movers has a partially positive impact. DT has also had a significant impact on long-term performance as shown by our long-term observations. Empirical findings indicate that DT has varying impacts on organizational performance in different dimensions and provides DT target-setting guidance to enterprises.

There are no studies so far that could proffer certain evidence to reveal the influence of DTI on firm performance. Such evidence is necessary for managers to convince their investors and stakeholders to believe that DTI can create new revenue for enterprises.

As researchers are writing a draft paper with incomplete structure or text, one of burdensome tasks is to deliberate about which references should be cited for one sentence or paragraph of this draft. In view of the rapid increase in the number of research papers, researchers desire to figure out a better way to do citation recommendations in developing their draft papers. The purpose of this paper is to propose citation recommendation algorithms that enable the acquisition of relevant citations for research papers that are still at the drafting stage. This study attempts to help researchers to select appropriate references among the vast amount of available papers and make draft papers complete in reference citation.

This study adopts a model for recommending citations for incomplete drafts. Four algorithms are proposed in this study. The first and second algorithms are unsupervised models, applying term frequency-inverse document frequency and WordNet technologies, respectively. The third and fourth algorithms are based on the second algorithm to integrate different weight adjustment strategies to improve performance.

The proposed recommendation method adopts three techniques, including using WordNet to transform vector and setting adjustment weights according to structural factors and the information completeness degree, to generate citation recommendation for incomplete drafts. The experiments show that all these three techniques can significantly improve the recommendation accuracy.

None of the methods employed in previous studies can recommend articles as references for incomplete drafts. This paper addresses the situation that a draft paper can be incomplete either in structure or text or both. Recommended references, however, can be still generated and inserted into any desired sentence of the draft paper.

Educational data mining (EDM) discovers significant patterns from educational data and thus can help understand the relations between learners and their educational settings. However, most previous data mining techniques focus on prediction of learning performance of learners without integrating learning patterns identification techniques.

This study proposes a new framework for identifying learning patterns and predicting learning performance. Two modules, the learning patterns identification module and the deep learning prediction models (DNN), are integrated into this framework to identify the difference of learning performance and predicting learning performance from profiles of students.

Experimental results from survey data indicate that the proposed identifying learning patterns module could facilitate identifying valuable difference (change) patterns from student’s profiles. The proposed learning performance prediction module which adapts DNN also performs better than traditional machine techniques in prediction performance metrics.

To our best knowledge, the framework is the only educational system in the literature for identifying learning patterns and predicting learning performance.

Since business competition has become more intense throughout the world, most enterprises are seeking to engage in business cooperation with other partners in order to enhance their competitive strengths. However, they do not necessarily develop mature information technologies’ (ITs) capabilities and skills internally but rather outsource them to IT providers. Therefore, the benefits received by firms which adopt the approach of business cooperation with IT providers have become an interesting issue for managers and shareholders.

This study adopted an event study methodology for apprising the short-term business value from the stock market. The authors predicted that investors will react as they receive news coverage about the strategy of business cooperation between outsourcing firms and an IT provider, International Business Machines (IBM) Corporation. The authors then collected all news coverage regarding the firms which had announced business cooperation with IBM and observed different types of abnormal returns.

On analyzing 53 announcements of cooperation with IBM from 2008 to 2016, the authors found that the announcement of business cooperation had a significantly positive influence on companies' market value.

To the best of the authors’ knowledge, this is the first study to investigate the issue for market reaction to the announcement of business cooperation with IBM.

Negative media coverage has important impacts on firm financial performance, but existing studies have inconsistent views of this relationship and lack a unified theoretical framework to explain how such impacts arise. This study aims to bridge this gap in the literature.

This study uses two sets of data encompassing publicly listed companies in Shanghai and Shenzhen stock exchanges from 2013 to 2019, which are covered by the China Stock Market and Accounting Research Database.

This study finds that the number of negative news coverages has an inverted U-shaped relationship with firm financial performance; this relationship is weakened by the proportion of shares held by institutional investors and strengthened by advertising intensity.

This study suggests that corporate executives should be aware of the potential value of a limited amount of negative news coverage and react with tolerance and caution when their companies encounter it.

This study uses two different routes provided in the elaboration likelihood model theory to fully explain the processes underlying changes in investors’ attitudes toward firms experiencing negative media coverage.

The purpose of this paper is to investigate the influence of the potential of hydrogen (pH) and dissolved oxygen in artificial seawater on the passivation behavior of 316L stainless steel.

The corrosion behavior was studied by using electrochemical measurements such as electrochemical impedance spectroscopy and polarization curve. The passive films were characterized with X-ray photoelectron spectroscopy.

The polarization resistance of the passive film decreases as the pH value drops ascribed to the formation of much more point defects. The donor carrier concentration (ND) in the passive film formed in the deaerated condition is lower than that in aerated conditions. Nevertheless, this phenomenon is the opposite when the pH value is 1 due to the significant decrease of Fe oxides/hydroxides coupled with the stable content of Cr oxides/hydroxides species. In addition, the compositional variation of the passive film also leads to the changes of its semiconductor properties from N-type to bipolar type.

This paper shows the variation of polarization resistance, corrosion potential, passive film composition and semiconductor properties with the pH value and dissolved oxygen. The results can serve as references to the further study on crevice corrosion of 316L in seawater.