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The purposes of this paper are (1) to explore the overall development of AI technologies and applications that have been demonstrated to be fundamentally important in the healthcare industry, and their related commercialized products and (2) to identify technologies with promise as the basis of useful applications and profitable products in the AI-healthcare domain.

This study adopts a technology-driven technology roadmap approach, combined with natural language processing (NLP)-based patents analysis, to identify promising and potentially profitable existing AI technologies and products in the domain of AI healthcare.

Robotics technology exhibits huge potential in surgical and diagnostics applications. Intuitive Surgical Inc., manufacturer of the Da Vinci robotic system and Ion robotic lung-biopsy system, dominates the robotics-assisted surgical and diagnostic fields. Diagnostics and medical imaging are particularly active fields for the application of AI, not only for analysis of CT and MRI scans, but also for image archiving and communications.

This study is a pioneering attempt to clarify the interrelationships of particular promising technologies for application and related products in the AI-healthcare domain. Its findings provide critical information about the patent activities of key incumbent actors, and thus offer important insights into recent and current technological and product developments in the emergent AI-healthcare sector.

This paper aims to first apply more advanced anisotropic yield criterions as Yld91 and Yld2004 to spherical indentation simulations, and investigate plastic anisotropy identified from indentation simulations following different yield criterions (Hill48, Yld91, Yld2004) to discover laws. It also aims to compare the difference in plastic anisotropy identified from indentation on three yield criterions and evaluate the applicability of plastic anisotropy.

This paper uses indentation simulations on different yield criterions to identify plastic anisotropy. First, the trust-region techniques based on the nonlinear least-squares method are used to determine anisotropy coefficients of Yld91 and Yld2004. Then, Yld91 and Yld2004 are implemented into ABAQUS software using user-defined material (UMAT) subroutines with the proposed universal structure. Finally, through considering comprehensively the key factors, the locations of the optimal data acquisition points in indentation simulations on different yield criterions are determined. And, the identified stress–strain curves are compared with experimental data.

This paper discovers that indentation on Yld2004 is able to fully identify difference in equivalent plastic strain between 0° and 90° directions when indentation depth h_t is relatively smaller. And, this research demonstrates conclusively that plastic anisotropy identified from indentation on Yld2004 and Yld91 is more applicable at larger strains than that on Hill48, and that on Yld2004 is more applicable than that on Yld91, overall. In addition, the method on the determination of the locations of the optimal data acquisition points is demonstrated to be also valid for anisotropic material.

This paper first investigates plastic anisotropic properties and laws identified from indentation simulations following more advanced anisotropic yield criterions and provides reference for later research.

In the context of global economic downturn and intense competition, firms are increasingly resorting to supply chains to acquire capital support and achieve sustainability. This study aims to investigate the effect of supply chain finance (SCF) on corporate sustainability performance (CSP) and identifies SCF-related recipes for CSP.

Based on a sample of 1,038 firms that disclose CSP – namely, corporate financial performance (CFP) and environmental, social and governance performance (ESGP) – the authors use a quasi-replication method consisting of empirical analysis with fuzzy-set qualitative comparative analysis (fsQCA) to investigate SCF’s effects on CSP.

The authors find that SCF has a “doing well by doing good” effect on CSP. CFP can promote the positive effect of SCF and ESGP while ESGP’s positive effect on SCF and CFP is nonsignificant. In addition, heterogeneity tests show that SCF’s promoting effect on CSP is affected by high-low CFP and ESGP. The fsQCA results verify the empirical findings and reveal five SCF-related recipes for achieving high CSP.

This study has the following two limitations. First, we do not consider how SCF affects CSP in different industries. There is a need to investigate whether industry heterogeneity changes SCF’s effects on CSP, especially in prominent industries, such as the energy industry, with its high susceptibility to ESGP, and the manufacturing industry, with its extensive application of SCF. It will be important to investigate these industries to better understand SCF’s role in sustainability. Second, we study the secondary supply chain – namely, core firm–suppliers and core firm–customers. The authors do not consider financial institutions (e.g. banks and guarantee institutions). SCF modes that include the participation of financial institutions, such as factoring financing and reverse factoring financing, cater more to the capital needs of diversified firms. In the future, studying specific industries that have made significant contributions to the application of SCF along with others that are more sensitive to environmental governance could better highlight the effect of SCF on sustainability and help supply chain managers understand the application value of SCF. Future research could also extend SCF participants into multiple roles to explore separate effects. Tracking financing demanders, fund providers and credit guarantors could capture SCF characteristics more comprehensively. Methodologically, it will be challenging to accurately measure SCF networks in terms of quantification. In future work, this could be performed with the help of artificial intelligence.

First, our findings indicate that SCF has a “doing well by doing good” effect on core firms. SCF can not only overcome the capital shortage of SMEs but also provide significant benefits to core firms. Second, our findings provide SCF-related recipes to help firms fulfil ESGP obligations without sacrificing CFP under the pressure to “do good.” The authors provide valuable insights and diverse recommendations to help supply chain managers, marketing executives and researchers adjust supply chain management strategies. Third, this work can guide executives in various fields to adopt SCF to achieve sustainability as a risk-mitigation strategy by means of marketing.

This study identifies better, more straightforward SCF-related recipes for CSP (consisting of CFP and ESGP) using a quasi-replication analysis that improves upon conventional methods such as regression analysis, which have limited power. The authors provide valuable insights and diverse recommendations to help managers pursue sustainable development. The findings point to practical guidelines and feasible solutions that can support well-founded operational strategic and management decision-making, which can enhance a firm’s competitiveness under uncertainty and a sluggish economy.

This paper aims to examine the consequences for innovative work behavior (IWB) of top-down knowledge hiding – that is, supervisors’ knowledge hiding from supervisees (SKHS). Drawing on social learning theory, the authors test the three-way moderated-mediation model in which the direct effect of SKHS on IWB is first mediated by self-efficacy and then further moderated by supervisor and supervisee nationality (locals versus foreigners).

The authors collected multi-sourced data from 446 matched supervisor-supervisee pairs working in a diverse range of organizations operating in the Kingdom of Saudi Arabia. After initial data screening, confirmatory factor analysis was conducted to test for the factorial validity of the used measures with AMOS. The hypothesized relationships were tested in regression analysis with SPSS.

Results showed that SKHS had both direct and mediation effects, via the self-efficacy mediator, on supervisee IWB. The mediation effect was further moderated by supervisor and supervisee nationality (local versus foreigners), which highlighted that the effect was stronger for supervisor–supervisee pairs that were local-local or foreigner-foreigner than for pairs that were local-foreigner or foreigner-local.

This study contributes to both knowledge hiding and IWB literature and discusses the useful theoretical and practical implications of the findings.

Providing that branded applications (apps) became a new trend in mobile marketing, the purpose of this study, thus, is to explore how to promote app users’ continuance intention and purchase intention (i.e. “app continuance”) toward a specific branded app.

By integrating both goods-dominant logic (GDL) and service-dominant logic (SDL), this study uses a unifying model to examine whether perceived usefulness and task-service fit (TSF) have different effects on the two parts of app continuance. This study identifies task characteristic and four service characteristics (interactivity, presence, localization and ubiquity) as antecedents of TSF. Furthermore, psychological barriers are examined as mediators of TSF and purchase intention within SDL. Data collected from 631 users of the targeted branded apps support all of the proposed hypotheses.

The findings show that besides perceived usefulness, TSF is an essential determinant of both app continuance in the context of branded apps and a partial mediator of psychological barriers between TSF and purchase intention.

Unlike prior studies, which have focused on traditional GDL to examine continuance intention, this study incorporates SDL and the notion of psychological barriers to explore such matters. The evidence concerning the significantly higher explanatory power of the full model suggests that a deeper understanding of the antecedents of app continuance is possible when the alternative view is taken into consideration, thus providing a promising avenue for future research.

As the functions of environmental regulations cannot be quantified while assessing their environmental efficiency, there has been no comprehensive evaluation of environmental efficiency. The purpose of this paper is to evaluate environmental regulations based on triangular and trapezoidal fuzzy numbers.

This paper uses L-R fuzzy numbers to transform the evaluation language into triangular fuzzy numbers, and adopts an α-level flexible slacks-based measurement model to evaluate the performance of these regulations. Trapezoidal fuzzy numbers are combined with a data envelopment analysis model, and an α-slack-based measurement (SBM) model is used to evaluate the environmental efficiency. The α-SBM model is confirmed to be stable and sustainable.

Relevant index data from 16,375 enterprises were collected to test the proposed model, and models corresponding to triangular fuzzy numbers and trapezoidal fuzzy numbers were used to evaluate their environmental efficiency. Comparative results showed that the proposed model is feasible and stable.

The main contributions of this study are twofold. First, this paper provides a valuable evaluation method for environmental regulation. Second, our research improves the practical performance of trapezoidal fuzzy data envelopment analysis and enhances its feasibility and stability.

The purpose of this paper is to explore flow as an underlying mechanism linking psychological ownership (PO) and subjective happiness and identify how flow affects employees’ subjective happiness.

Two separate samples were used. Sample 1 examined the responses of 120 female spa workers. Sample 2 examined the responses of 334 male logistics technicians. Data were collected through paper-based questionnaires across two time points.

PO was positively related to employees’ subjective happiness across both samples. Furthermore, PO has distinct impacts on employees’ subjective happiness through two distinct measures of flow: immersion and mastery. The authors found that immersion fully mediates the relationship between PO and employee subjective happiness in Sample 1, and mastery in Sample 2.

This is one of the first empirical studies to examine whether and how PO increases employees’ subjective happiness. The results contribute to the literature by providing a theoretical rationale for and an empirical analysis of a model wherein flow mediates the linkage between employees’ feelings of PO and their subjective happiness.

Numerous companies have launched brand pages (BPs) on social networking sites to enhance customer-brand communication, cultivate the customer-brand relationship and promote brand loyalty. This study aims to investigate how BP affordances support social commerce.

The study devises a theoretical model linking the proposed BP affordances (visibility, selectivity, persistence and interactivity) to three customer values (relationship quality, brand experience and smart shopping feeling [SSF]) to encourage brand loyalty and BP endorsement on the part of the customer.

Data collected from 591 respondents support all proposed hypotheses. The model explains high variances in brand loyalty and BP endorsement, indicating that relationship quality plays a more salient role in producing brand loyalty, while SSF plays a more important role in eliciting BP endorsement.

The study is unique in four ways. First, drawing on the lens of affordance, it proposes specific affordances for BPs and offers empirical results for their applicability. Second, by incorporating CDL into the research model, it illuminates the high explanatory power of these proposed BP affordances on the three customer values. Integrating the S-O-R model with the affordance perspective and CDL provides a more complete picture of the BP phenomenon. Third, it extends the reach of existing work by examining BP endorsement in social media as a dependent variable beyond brand loyalty, with SSF included as another source of values to shed more light on the relationships depicted in the model. Fourth, by taking trait competitiveness into account, it sheds further light on relationships between customer values and BP endorsement.

The aim of this study is to extend DeLone and McLean's IS success model by introducing justice – fair treatments received from the exchanging party – and trust into a theoretical model for studying customers' repurchase intentions in the context of online shopping.

The research model was tested with data from 219 of PCHome's online shopping customers using a web survey. PLS (partial least squares) was used to analyze the measurement and structural models.

Data collected from 219 valid respondents provided support for all but one hypotheses (with a p‐value of less than 0.05). The unsupported hypothesis regards the relationship between service quality and satisfaction (H4). The study shows that trust, net benefits, and satisfaction are significant positive predictors of customers' repurchase intentions toward online shopping. Information quality, system quality, trust, and net benefits, are significant determinants of customer satisfaction. Besides, online trust is built through distributive, procedural, and interactional justice. Overall, the research model accounted for 79 percent of the variance of repurchase intention.

An endeavor to extend the updated IS success model in terms of the peculiar nature of e‐commerce is needed. The study complements the updated IS success model with justice trust perspectives, considering them a more comprehensive measure of online shopping satisfaction and repurchase intention in an e‐commerce context.

Available information for evaluating the possibility of hospitality firm failure in emerging countries is often deficient. Oversampling can compensate for this but can also yield mixed samples, which limit prediction models’ effectiveness. This research aims to provide a feasible approach to handle possible mixed information caused by oversampling.

This paper uses mixed sample modelling (MSM) when evaluating the possibility of firm failure on enlarged hospitality firms. The mixed sample is filtered out with a mixed sample index through control of the noisy parameter and outliner parameter and meta-models are used to build MSM models for hospitality firm failure prediction, with performances compared to traditional models.

The proposed models are helpful in predicting hospitality firm failure in the mixed information situation caused by oversampling, whereas MSM significantly improves the performance of traditional models. Meanwhile, only partial mixed hospitality samples matter in predicting firm failure in both rich- and poor-information situations.

This research is helpful for managers, investors, employees and customers to reduce their hospitality-related risk in the emerging Chinese market. The two-dimensional sample collection strategies, three-step prediction process and five MSM modelling principles are helpful for practice of hospitality firm failure prediction.

This research provides a means of processing mixed hospitality firm samples through the early definition and proposal of MSM, which addresses the ranking information within samples in deficient information environments and improves forecasting accuracy of traditional models. Moreover, it provides empirical evidence for the validation of sample selection and sample pairing strategy in evaluating the possibility of hospitality firm failure.