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Multiple classifier systems have been used widely in computing, communications, and informatics. Combining multiple classifier systems (MCS) has been shown to outperform a single classifier system. It has been demonstrated that improvement in ensemble performance depends on either the diversity among or the performance of individual systems. A variety of diversity measures and ensemble methods have been proposed and studied. However, it remains a challenging problem to estimate the ensemble performance in terms of the performance of and the diversity among individual systems. The purpose of this paper is to study the general problem of estimating ensemble performance for various combination methods using the concept of a performance distribution pattern (PDP).

In particular, the paper establishes upper and lower bounds for majority voting ensemble performance with disagreement diversity measure Dis, weighted majority voting performance in terms of weighted average performance and weighted disagreement diversity, and plurality voting ensemble performance with entropy diversity measure D.

Bounds for these three cases are shown to be tight using the PDP for the input set.

As a consequence of the authors' previous results on diversity equivalence, the results of majority voting ensemble performance can be extended to several other diversity measures. Moreover, the paper showed in the case of majority voting ensemble performance that when the average of individual systems performance P is big enough, the ensemble performance P^m resulting from a maximum (information‐theoretic) entropy PDP is an increasing function with respect to the disagreement diversity Dis. Eight experiments using data sets from various application domains are conducted to demonstrate the complexity, richness, and diverseness of the problem in estimating the ensemble performance.

Integrating economic and green initiatives into firm strategies is a challenge for firms in various industries. The study aims to incorporate multiple views, i.e. green innovation theory (GIT), the green institutional perspective (GIP) and the natural-resource-based view (NRBV), to develop a comprehensive model to explore why and how firms implement green product innovation (GPI).

The study explores the relationships among institutional pressure, the firm's green resources and GPI. The research also distinguishes two different types of GPI: exploratory GPI and exploitative GPI. A total of 270 valid questionnaires were collected from electrical and electronics manufacturers in Taiwan. The authors employed structural equation modeling (SEM) using analysis of moment structures (AMOS) 23.0 to test the hypotheses.

The results show that institutional pressure has a significant positive correlation with the firm's green resources. Furthermore, institutional pressure has a significantly positive influence on exploratory GPI and exploitative GPI, respectively. The firm's green resources also have a significantly positive effect on both exploratory GPI and exploitative GPI. In addition, institutional pressures have significantly positive indirect effect on both exploratory GPI and exploitative GPI.

Economic benefits and environmental sustainability are the most pressing issues faced by the electrical and electronics industry today. The study's investigation covers Taiwanese electrical and electronics manufacturers only, so the test of the research model has limited generalizability. The authors suggest that to expand the generalizability of the findings, future research should examine this model in the context of other regions such as Southeast Asia, Africa, South America, etc.

The study has many interesting implications for both practitioners and policymakers. The authors' findings suggest that while Taiwanese electrical and electronics manufacturers face significant pressure from customers, competitors and regulation requirements (e.g. waste electrical and electronic equipment [WEEE], restriction of hazardous substances [RoHS] and energy using product [EuP] directives), firms in that sector should efficiently and effectively deploy their green resources and then perform proper GPI (e.g. exploratory GPI or exploitative GPI). These results also serve as a reminder to policymakers that balancing coercive (command-and-control) mechanisms with incentives and voluntary mechanisms is the best means by which to develop motivational and effective GPI policies.

First and foremost, the paper divides GPI into exploratory GPI and exploitative GPI. Furthermore, the research incorporates two important schools of thought, i.e. the GIP and NRBV, thus providing a more holistic view by which to explore why and how companies adopt GPI.

Telecommunication (telecom) fraud is one of the most common crimes and causes the greatest financial losses. To effectively eradicate fraud groups, the key fraudsters must be identified and captured. One strategy is to analyze the fraud interaction network using social network analysis. However, the underlying structures of fraud networks are different from those of common social networks, which makes traditional indicators such as centrality not directly applicable. Recently, a new line of research called deep random walk has emerged. These methods utilize random walks to explore local information and then apply deep learning algorithms to learn the representative feature vectors. Although effective for many types of networks, random walk is used for discovering local structural equivalence and does not consider the global properties of nodes.

The authors proposed a new method to combine the merits of deep random walk and social network analysis, which is called centrality-guided deep random walk. By using the centrality of nodes as edge weights, the authors’ biased random walks implicitly consider the global importance of nodes and can thus find key fraudster roles more accurately. To evaluate the authors’ algorithm, a real telecom fraud data set with around 562 fraudsters was built, which is the largest telecom fraud network to date.

The authors’ proposed method achieved better results than traditional centrality indices and various deep random walk algorithms and successfully identified key roles in a fraud network.

The study used co-offending and flight record to construct a criminal network, more interpersonal relationships of fraudsters, such as friendships and relatives, can be included in the future.

This paper proposed a novel algorithm, centrality-guided deep random walk, and applied it to a new telecom fraud data set. Experimental results show that the authors’ method can successfully identify the key roles in a fraud group and outperform other baseline methods. To the best of the authors’ knowledge, it is the largest analysis of telecom fraud network to date.

This study aims to propose an integrated view and emphasize the importance of bank intangible value binding in customers' robo-advisory adoption. It explores the relationship between robo-advisors and traditional banking and defines the role of bank intangible assets value. It also attempts to understand if trust in the banking institution and the financial consultant determines the effect of these relationships.

The target sample of the study was investors who currently use wealth management services. This study collected 228 valid questionnaires and then executed structural equation model analysis to test the hypotheses.

Results showed that intangible value bindings play a mediating role, which positively affects consumers' willingness to adopt robo-advisors. Consumers' trust in banks and financial consultants are antecedent variables, which positively affect the intangible value bindings between consumers and banks. In addition, when the consumers' investment amount is higher, it will weaken the positive relationship between the intangible value binding and robo-advisor adoption intention.

Most of the past studies have focused on whether robo-advisors would replace personal financial consultants. This study proposes a hybrid model that contains both robo-advisors and traditional banking services, which encourage the acceptance of robo-advisors.

Environmental sustainability is one of the most pressing issues faced by the electric and electronics industry today. Industries are being challenged to incorporate environmental initiatives in their corporate strategies. Thus, this study aims to investigate the impact of stakeholder pressures (regulatory, internal and market) on green supply chain management and green corporate resources as well as their effects on the economic and environmental performance of Taiwan's electric and electronic industry.

A total of 194 valid questionnaires were collected out of the 1,000 questionnaires distributed to Taiwan's electric and electronic product manufacturers. A structural equation modeling, using Amos 22.0, was used to test the hypotheses.

The results of the analyses show that stakeholder pressure has a significant positive impact on corporate green resources and green supply chain management practices while green supply chain management practices have a significant and positive impact on organizational performance. Moreover, corporate green resources provide a mediation between organizational stakeholder pressure and green supply chain management.

The results may be of value and interest to supply chain managers and policymakers on the push factors for implementing green supply chain management practices and their consequences.

This paper shows the complementarity of stakeholder and resource-based theories in influencing organizational performance in the electric and electronic industry in the context of sustainable development. This also enhances the understanding of the antecedents and consequences of green supply chain management and provides robust findings on the relationship between environmental and economic performance.

Green sustainability has become a critical challenge for businesses to execute green supply chain management (GSCM). Most of the literature on GSCM emphasizes regulations as drivers and few pieces of research are conducted from an internal organizational culture view. This study aims to use the cultural perspective of market orientation (MO) and draws on both the natural resource-based view (NRBV) and dynamic capability view (DCV) to explore the relationships among green market orientation (GMO), GSCM capability and organizational performance.

A total of 1,000 survey questionnaires were distributed to the Taiwanese electronics industry and 207 valid questionnaires were collected. Data was analyzed using structural equation modeling.

The results show that GMOs have a significant positive impact on GSCM capability, environmental performance and economic performance. Moreover, GSCM capability is positively related to environmental and economic performance. The results also show that GMOs have a significant indirect influence on environmental performance and economic performance through GSCM capability.

The findings of this study suggest how Taiwan’s electrical and electronic manufacturers, while faced with pressure from competitors, customers and regulations (i.e. waste electrical and electronic equipment directive, Restriction of Hazardous Substances Directive and energy-using products directives), should efficiently and effectively implement GMO and enhance GSCM capability to improve organizational performance.

This study fills up the gap between MO and performance indistinct relationships. It has also integrated two perspectives, namely, NRBV and DCV, to explain GSCM capability as a mediator between GMO and organizational performance relationship and to examine the relationships among GMO, GSCM capability and organizational performance.

This study aims to offer a bibliometric analysis of the already substantial and growing literature on global virtual teams (GVTs).

Using a systematic literature review approach, it identifies all articles in the Web of Science from 1999 to 2021 that include the term GVTs (in the title, the abstract or keywords) and finds 175 articles. The VOSviewer software was applied to analyze the bibliometric data.

The analysis revealed three dialogizing research clusters in the GVTs literature: a pioneering management information systems and organizational cluster, a general management cluster and a growing international management and behavioural studies cluster. Furthermore, it highlights the most cited articles, authors, journals and nations, and the network of strong and weak links regarding co-authorships and co-citations. Additionally, this study shows a change in research patterns regarding topics, journals and disciplinary approaches from 1999 to 2021. Finally, the analysis illustrates the position and centrality in the network of the most relevant actors.

The findings can guide management practitioners, educators and researchers to the most meaningful clusters of publications on GVTs, and help navigate and make sense of the vast body of the available literature. The importance of GVTs has been growing in the past two decades, and Covid-19 has accelerated the trend.

This study provides an updated and comprehensive systematic literature review on GVTs. To the best of the authors’ knowledge, it is also the first systematic literature review and bibliometry on GVTs. It concludes by suggesting future research paths.