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The purpose of this paper is to develop a theoretical model to investigate the determinants of continuance intention toward social networking sites (SNSs) by integrating the perspectives of the uses and gratifications theory, perceived interactivity and network externalities.

Data collected from 255 Facebook users in Taiwan were used to test the proposed model. The partial least squares method was used to test the measurement model and the structural model.

The findings reveal that emotional gratifications and social gratifications are the key predictors of users’ continuance intention toward SNSs. Further, the results indicate that perceived network size, perceived complementarity, machine interactivity and person interactivity influence information gratifications significantly, while perceived complementarity, machine interactivity and person interactivity exert positive effects on emotional gratifications. Finally, the results show that machine interactivity and person interactivity impact social gratifications positively, whereas perceived network size and perceived complementarity affect machine interactivity and person interactivity significantly.

This study is one of the earliest research inquiries to examine the effects of various types of gratifications on continuance intention. It is also one of the earliest studies to identify the antecedents of gratifications from social factors and technological attributes simultaneously.

The purpose of this study is to propose a research model based on the stimulus–organism–response (S–O–R) model to examine whether network externality, personalization and sociability as environmental feature antecedents to learners’ learning engagement (LE) can influence their learning persistence (LP) in massive open online courses (MOOCs).

Sample data for this study were collected from learners who had experience in taking MOOCs provided by the MOOC platform launched by a well-known university in Taiwan, and 371 usable questionnaires were analyzed using structural equation modeling in this study.

This study proved that learners’ perceived network externality, personalization and sociability in MOOCs positively affected their cognitive LE, psychological LE and social LE elicited by MOOCs, which jointly led to their LP in MOOCs. The results support all proposed hypotheses, and the research model accounts for 76.2% of the variance in learners’ LP in MOOCs.

This study uses the S–O–R model as a theoretical base to construct learners’ LP in MOOCs as a series of the inner process, which is affected by network externality, personalization and sociability. It is worth noting that three psychological constructs including cognitive LE, psychological LE and social LE are used to represent learners’ organismic states of MOOCs usage. To date, hedonic/utilitarian concepts are more often adopted as organisms in previous studies using the S–O–R model, and psychological constructs have received lesser attention. Hence, this study’ contribution on the application of capturing psychological constructs for completely expounding three types of environmental features as antecedents to learners’ LP in MOOCs is well documented.

The purpose of this study is to propose a research model based on the stimulus-organism-response (S-O-R) model to test whether network externality, gamification and media richness as environmental feature antecedents to learners' learning engagement (LE) can affect their continuance intention of massive open online courses (MOOCs).

Sample data for this study were collected from learners who had experience in taking the gamified MOOCs provided by the MOOC platform launched by a well-known university in Taiwan, and 315 usable questionnaires were analyzed using structural equation modeling in this study.

This study verified that learners' perceived network externality, gamification and media richness in MOOCs positively influenced their behavioral LE, emotional LE and social LE elicited by MOOCs, which collectively caused their continuance intention of MOOCs. The results support all proposed hypotheses, and the research model accounts for 75.6% of the variance in learners' continuance intention of MOOCs.

This study uses the S-O-R model as a theoretical groundwork to construct learners' continuance intention of MOOCs as a series of the internal process, which is influenced by network externality, gamification and media richness. Noteworthily, three psychological constructs, behavioral LE, emotional LE and social LE, are employed to represent learners' organisms of MOOCs usage. To date, the concepts of network externality, gamification and media richness are rarely together adopted as environmental stimuli, and psychological constructs as organisms have received lesser attention in prior MOOCs studies using the S-O-R model. Hence, this study's contribution on the application of capturing psychological constructs for completely expounding three types of environmental features as antecedents to learners' continuance intention of MOOCs is well documented.

Given a product or service, the number of its installed user base has a significant positive effect on the existing users’ loyalty and new users’ conversion. This effect is conceptualized as network externalities in economics. Network externalities are supposed to be particularly striking in nowadays online business-to-business (B2B) platforms, but yet the mystery behind their effects on user loyalty to online B2B platforms remains to be delicately unraveled. The purpose of this paper is to discover the factors driving users’ loyalty, especially buyers’ loyalty, to online B2B platforms, by highlighting the impacts of network externalities on loyalty and other mediating factors.

A conceptual model of buyer loyalty under network externalities is elaborated. The reliability and validity of the instruments of the latent model constructs are assessed by confirmatory factor analysis, and the hypothesized causal relationships among the constructs are tested by structural equation modeling, on 710 valid buyer samples collected from a famous online B2B platform in China.

The analysis demonstrates that: perceived value, user satisfaction and switching costs are the major predictors of buyer loyalty to online B2B platforms characterized by network externalities; network externalities positively account for buyer loyalty by contributing to perceived value, user satisfaction and switching costs; and direct network externality (measured by perceived network size and perceived external prestige) has a significant effect on indirect network externality (measured by perceived compatibility and perceived complementarity).

The findings allow the authors to conclude meaningful managerial implications for online B2B service providers to build up loyal user bases through improving users’ perceptions of network externalities, switching costs and value.

In the mining industry, a run-of-mine (ROM) stockpile is a temporary storage unit, but it is also widely accepted as an effective method to reduce the short-term variations of ore grade. However, tracing ore grade at ROM stockpiles accurately using most current fleet management systems is challenging, due to insufficient information available in real time. This study aims to build a three-dimensional (3D) model for ROM stockpiles continuously based on fine-grained grade information through integrating data from a number of ore grade tracking sources.

Following a literature review, a framework for a new stockpile management system is proposed. In this system, near real-time high-resolution 3D ROM stockpile models are created based on dump/load locations measured from global positioning system sensors. Each stockpile model contains a group of layers which are separated by different qualities.

Acquiring the geometric shapes of all the layers in a stockpile and cuts made by front wheel loaders provides a better understanding about the quality and quality distribution within a stockpile when it is stacked/reclaimed. Such a ROM stockpile model can provide information on predicating ore blend quality with high accuracy and high efficiency. Furthermore, a 3D stockyard model created based on such ROM stockpile models can help organisations optimise material flow and reduce the cost.

The modelling algorithm is evaluated using a laboratory scaled stockpile at this stage. The authors expect to scan a real stockpile and create a reference model from it. Meanwhile, the geometric model cannot represent slump or collapse during reclaiming faithfully. Therefore, the model is expected to be reconcile monthly using laser scanning data.

The proposed model is currently translated to the operations at OZ Minerals. The use of such model will reduce the handling costs and improve the efficiency of existing grade management systems in the mining industry.

This study provides a solution to build a near real-time high-resolution multi-layered 3D stockpile model through using currently available information and resources. Such novel and low-cost stockpile model will improve the production rates with good output product quality control.

This study aims to review the literature on graph-theoretic mining methods for node importance in both static and dynamic world city networks, which is correspondingly categorised by graph-theoretic node importance mining on network topologies and transmission mechanisms.

The authors overview the graph-theoretic indicators of node importance: centrality and power. Then, the methods of graph-theoretic node importance mining on network topologies are assessed with node relevance, centrality- and power-based measurements, heterogeneous fusion and other miscellaneous approaches. The latest progress in transmission mechanisms is also reviewed in this study involving network evolution, node immunisation and robustness in dynamics. Finally, the findings are analysed and future directions in this field are suggested.

The method development of node importance mining is driven by complex application-based problems within a transmission mechanism. Fusion measurements, based on centrality and power, are extended by other graph mining techniques in which power has a significant role. In conclusion, the trends of node importance mining focus on power-embedded fusion measurements in the transmission mechanism-based complex applications.

This is the first systematic literature review of node importance from the view of graph-theoretic mining.

The purpose of this study is to propose a synthetic post-adoption model based on the expectation-confirmation model (ECM) and flow theory to examine whether the fit factor, network factors and psychological factors as antecedents to end-users’ beliefs can affect their continuance intention of the robo-advisor.

This study used the research model based on ECM and flow theory to examine the effects of the fit factor, network factors and psychological factors on end-users’ beliefs and continuance intention of the robo-advisor. Sample data were collected from end-users at three financial services companies in Taiwan. A total of 450 questionnaires were distributed and 360 (80.0%) usable questionnaires were analyzed using structural equation modeling.

This study proposes a solid research model that based on ECM and flow theory, three types of factors, namely, fit factor, network factors and psychological factors, as antecedents to end-users’ continuance intention of the robo-advisor have been examined and this study’s results strongly support the research model with all hypothesized links being significant.

It is particularly worth mentioning that a synthetic post-adoption model can be proposed in this study by introducing the fit factor extracted from task-technology fit model, network factors originated from the theory of network externalities and psychological factors derived from uses and gratifications theory as antecedents to perceived usefulness, confirmation, satisfaction and continuance intention referred in ECM and flow experience derived from flow theory. Thus, this study’s research model and findings can reveal deep insights into the evaluation of determinants in the field of end-users’ continuance intention of the robo-advisor.

This study's purpose is to propose an integrated post-adoption model based on expectation-confirmation model (ECM) and cognitive absorption (CA) theory to examine whether network factors, gamification factor, and quality factors as antecedents to end-users' beliefs can affect their continuance intention of the robo-advisor.

A total of 600 questionnaires were distributed in three sample banks in Taiwan, and sample data for this study were collected from these three banks' customers who had experience in using these banks' own robo-advisor to make their investment decisions. Consequently, 381 useable questionnaires were analyzed using structural equation modeling in this study, with a useable response rate of 63.5%.

This study proposes a solid research model that based on ECM and CA theory, three types of factors, network factors, gamification factor, and quality factors, as antecedents to end-users’ continuance intention of the robo-advisor have been examined, and this study's results strongly support the research model with all hypothesized links being significant.

This study contributes to end-users' continuance intention of the robo-advisor based on ECM, CA theory, theory of network externalities, gamification, and updated DeLone and McLean IS success model, and reveals deep insights into the evaluation of determinants in the field of end-users' continuance intention of the robo-advisor. Hence, it is especially worth mentioning that three types of determinants (i.e. network factors, gamification factor, and quality factors) are simultaneously evaluated, and extrinsic and intrinsic motivators are both taken into account in this study's research model development of end-users' continuance intention of the robo-advisor to acquire a more all-round and robust analysis.

The purpose of this paper is to explore the determinants of continuance intention of social networking site (SNS) users through a new perspective and discuss how to retain SNS users.

The author proposed a research model by integrating network externalities and social support. Three dimensions of social support and two types of network externalities were analyzed, respectively, to explore the direct and indirect effects on continuance intention. Online questionnaires were adopted to collect data, and 513 valid samples were analyzed by structural equation modeling approach.

The findings show that network externalities have a significant indirect effect on user’ continuance intention through the mediation effects of social support, and among the three dimensions of social support, network management plays a more important role on continuance intention.

The findings suggest that network externalities can trigger the function of social support to keep the “stickiness” of SNS users, and network management is the key dimension of online social support. Some other theoretical and practical implications are also provided.

The study is novel in exploring users’ continuance intention of SNSs by integrating social support and network externalities. Meanwhile, the author also intends to compare the effect of different dimensions of social support on SNS usage and discuss their internal relationships.

In the previous decade, a substantial amount of research has been undertaken to measure the digitalized supply chain (DSC) performance. This paper aims to present a systematic literature review on DSC performance measurement metrics to apprehend current practices, recognize gaps and advocate future research itineraries.

To guarantee a replicable, rigorous and transparent research process, the authors used a systematic literature review methodology to synthesize the research. A combination of 25 keywords was used to obtain 248 scientific studies in the first step. The balance scorecard (BSC) model was used to categorize 299 gathered performance metrics into 4 BSC perspectives.

The review highlighted the need for qualitative performance measuring metrics for DSC. During the review, only one study was identified that primarily focused on developing performance metrics for DSC. Additionally, the review identified that metrics related to internal and financial perspectives received the most attention while the “growth and learning” perspective received the least attention. The review also identified that external partners, such as distributors and suppliers, were virtually ignored in previous literature.

Although numerous literature reviews have been conducted in the past on the performance measuring metrics for supply chain management, no literature review aiming to synthesize the measuring metrics for DSC has yet been undertaken.