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Competitive advantage may be gained from two main sources: assets and the capabilities that enable assets to be deployed advantageously (Dierickx and Cool 1989). Day (1994, p.38) defined capabilities as "complex bundles of skills and accumulated knowledge, exercised through organizational processes, which enable firms to coordinate activities and make use of their assets." Management's task is to exploit and leverage firm specific assets and capabilities (Mahoney and Pandian 1992).This research focuses on the relationships to performance of two types of capabilities: customerfocused capabilities and information-focused capabilities. The connection between customerfocused capabilities and firm performance has long been recognized in marketing literature (e.g., Day 1994). In the logistics field, increased attention has been given to the relationships among customer-focused logistics capabilities and firm performance (Innis and La Londe 1994;Novack, Langley, and Rinehart 1995;Stank and Lackey 1997). In particular, Bowersox, Closs, and Stank (1999) incorporated key customer-focused capabilities into a "customer integration" construct that formed one core competence of firms achieving high levels of supply chain logistical integration.Information technology and information-focused capabilities have increasingly been viewed as key predictors of firm performance. For example, EDI (electronic data interchange), e-commerce, e-logistics, ERP (enterprise resource planning), data warehousing, and data mining are current buzzwords in the business community. In logistics, the importance of information technology has long been recognized (e.g., Fawcett, Calantone, and Smith 1996;Williams et al. 1997). Bowersox, Closs, and Stank positioned information-focused capabilities as important enablers of improved firm performance in best practice firms.The first goal of this research is to propose and test a model of the relationships among customerfocused capabilities, information-focused capabilities, and firm performance. We begin by grounding each independent construct in prior research in marketing, IT, and logistics, and then develop hypotheses from this review. A methodology for testing the hypothesized model is described and followed by a discussion of results. BACKGROUND Customer-Focused CapabilitiesVarious research streams in marketing and logistics support the notion that firms have to develop customer-focused capabilities in order to achieve superior performance. The concept of "market orientation" represents superior skills in understanding and satisfying customers. Empirical evidence supports the proposition that market orientation is positively associated with superior performance (Deshpandé, Farley, and Webster 1993;Hunt and Morgan 1995;Jaworski and Kohli 1993;Narver and Slater 1990). Day argued that organizations could become more market oriented by identifying and building the special internal and external capabilities that set marketdriven organizations apart. In particular, market-driven organizations have superior c...

Background and aimsAdolescent smartphone addiction has received increased attention in recent years, and peer relationship has been found to be a protective factor in adolescent smartphone. However, little is known about the mediating and moderating mechanisms underlying this relation. The aim of this study was to investigate (a) the mediating role of self-esteem in the association between student–student relationship and smartphone addiction, and (b) the moderating role of the need to belong in the indirect relationship between student–student relationship and adolescent smartphone addiction.MethodsThis model was examined with 768 Chinese adolescents (mean age = 16.81 years, SD = 0.73); the participants completed measurements regarding student–student relationship, self-esteem, the need to belong, and smartphone addiction.ResultsThe correlation analyses indicated that student–student relationship was significantly negatively associated with adolescent smartphone addiction, and the need to belong was significantly positively associated with adolescent smartphone addiction. Mediation analyses revealed that self-esteem partially mediated the link between student-student relationship and adolescent smartphone addiction. Moderated mediation further indicated that the mediated path was weaker for adolescents with lower levels of the need to belong.Discussion and conclusionHigh self-esteem could be a protective factor against smartphone addiction for adolescents with a strong need to belong as these students appeared to be at elevated risk of developing smartphone addiction.

Innovations usually have an initial impact on very few people. The period of learning or early evaluation precedes the diffusion of the technology into the wider addressed population. More than a transfer, this is best characterized as communication of benefits, costs, and compatibility with earlier technologies and a relative assessment of the new state of the art. Innovation development by an organization or individual creates not just a device (i.e., process or tacit knowledge) but concomitantly a capacity on the part of other organizations or persons to use, adopt, replicate, enhance, or modify the technology, skills, or knowledge for their own purposes. How innovations actually diffuse is to understand the communication of progress, and this framing helps one to design innovations and also design the marketing and testing programs to ready innovations for market and launch them efficiently. Diffusion theory's main focus is on the flow of information within a social system, such as via mass media and word-of-mouth communications. This theory presents often in the form of mathematical models of innovation and imitation. Distinct from classical diffusion models, however, consumers are not all identical in how they connect to others within a market or how they respond to information. We examine the effects of various network structures and relational heterogeneity on innovation diffusion within market networks. Specifically, network topology (the structure of how individuals in the market are connected) and the strength of communication links between innovator and follower market segments (a form of relational heterogeneity) are studied. Several research questions concerning network heterogeneity are addressed with an agent-based modeling approach. The present study's findings are based on simulation results that show important effects of network structure on the diffusion process. The ability to speed diffusion varies significantly according to within-and cross-segment communications within a heterogeneous network structure. The implications of the present approach for new product diffusion are discussed, and future research directions are suggested that may add useful insights into the complex social networks inherent to diffusion. A simple summary is that discovery of significant prime communicator nodes in a network allows innovation development practices to be better calibrated to realistically multiple market segments.

In this expository paper we give an overview of some statistical methods for the monitoring of social networks. We discuss the advantages and limitations of various methods as well as some relevant issues. One of our primary contributions is to give the relationships between network monitoring methods and monitoring methods in engineering statistics and public health surveillance. We encourage researchers in the industrial process monitoring area to work on developing and comparing the performance of social network monitoring methods. We also discuss some of the issues in social network monitoring and give a number of research ideas.

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In this work we propose a method for optimal treatment assignment based on individual covariate information for a patient. For the K treatment ([Formula: see text]) scenario, we compare quantities that are suitable surrogates to true conditional probabilities of outcome variable of each treatment dominating outcome variables for all other treatments conditional on patient specific scores constructed from patient-specific covariates. As opposed to methods based on conditional means, our method can be applied for a broad set of models and error structures. Furthermore, the proposed method has very desirable large sample properties. We suggest Single Index Models as appropriate models connecting outcome variables to covariates and our empirical investigations show that correct treatment assignments are highly accurate. The proposed method is also rather robust against departures from a Single Index Model structure. Furthermore, selection of a treatment using the proposed metric appears to incur no losses in terms of the average reward for cases when two treatments are close in terms of this metric. We also conduct a real data analysis to show the applicability of the proposed procedure. This analysis highlights possible gains both in terms of average response and survival time if one were to use the proposed method.