Search results

This study aims to augment the understanding of dynamic capabilities (DCs) by exploring the interrelations among the DC categories (sensing, seizing, reconfiguring) and the distinct impact of each DC on firm performance under low and high levels of competitive intensity.

The analysis is based on a cross-sectional survey of 139 managers in Israel. The data were collected through Web-based questionnaires using the Qualtrics software. A two-stage data analysis was performed using structural equation modeling (SEM).

The findings indicate that DCs follow a sequence in which sensing drives seizing, which, in turn, enhances reconfiguring. The effects of sensing are mainly manifested through its direct impact on seizing, with no evidence for an impact of sensing on company performance. Moreover, under low competitive intensity, only seizing appears to impact performance, while under high competitive intensity, reconfiguring joins seizing in improving firm performance.

The study's findings advance the debate on the direct vs sequential nature of DCs by indicating an internal DC sequence. Our research also advocates for a crucial role of sensing in enhancing DCs, regardless of the level of competitive intensity. Furthermore, this research expands the understanding of the consequences of DCs and enables the prioritization of DC categories under low and high competitive intensity.

The purpose of this paper is to propose a method for statistical inference on data from power law distributions in order to explain behavior and social phenomena associated with web‐based social spaces such as discussion forums, question‐and‐answer sites, web 2.0 applications and the like.

The paper starts by highlighting the importance of explaining behavior in social networks. Next, the power law nature of social interactions is described and a hypothetical example is used to explain why analyzing sub‐sets of data might misrepresent the relationship between variables having power law distributions. Analysis requires the use of the complete distribution. The paper proposes logarithmic transformation prior to correlation and regression analysis and shows why it works using the hypothetical example and field data retrieved from Microsoft's Netscan project.

The hypothetical example emphasizes the importance of analyzing complete datasets harvested from social spaces. The Netscan example shows the importance of the logarithmic transformation for enabling the development of a predictive regression model based on the power law distributed data. Specifically, it shows that the number of new and returning participants are the main predictors of discussion forum activity.

This paper offers a useful analysis tool for anyone interested in social aspects of the Internet as well as corporate intra‐net systems, knowledge management systems or other systems that support social interaction such as cellular phones and mobile devices. It also explains how to avoid errors by paying attention to assumptions and range restriction issues.

This paper sets out to present the concept of the value of information, review the descriptive, rational, social and behavioral approaches for assessing the value of information, and explain why user‐centered rather than information‐centered evaluations are the most relevant.

The paper starts by highlighting the main facets and market characteristics which influence the value of information. Next, four approaches to assessing the value of information are explained, including a discussion of advantages and limitations of each approach. The approaches reviewed include descriptive, rational, social and behavioral research. Finally, an information value assessment recommendation is given and a theoretical framework is offered.

The descriptive approach is useful in raising new angles for theory development. The rational approach assumes that the value is inherent in information and offers models that describe how information should be valued. The social perspective suggests that markets are enhanced by social activity. The behavioral aspect teaches that value perception changes by person and circumstance and is a key influence on information markets.

This paper offers a concentrated multi‐dimensional theoretical basis on a topic of central importance to anyone interested in Internet research, information consumption and production. Theory offered here constitutes a basis for a large number of potential empirical research endeavors.

The purpose of this paper is to examine the impact of the customer- and firm-focused driving factors, relationship quality (RQ), and identification on customer citizenship behaviors (CCB) on corporate social networking sites (SNS), as well as the impact of service attribute in such relationship.

A survey was conducted among the Facebook members of 7-Eleven (318) and Starbucks (316) in Taiwan to test the proposed framework. A structural equation modeling was used to test the validity of the research model and hypotheses.

The results reveal that SNS RQ and SNS identification are key factors affecting CCB, whereas financial bond (firm-focused) and consumer-company identification (customer-focused) are critical initiators. The findings reveal contingencies across service attributes for such effects. Experienced service firms can better encourage CCB by intimating SNS relationships with followers. Search firms should secure online identification to enable customers to perform CCB. The results also confirm the mediating effects of SNS RQ and SNS identification.

This study contributes to the literature by simultaneously examining the firm (external)- and individual (internal)-level of incentives, and to further reveal the main drivers encouraging CCB on corporate SNS. This study also belongs to the limited studies that discuss consumer voluntary behaviors on the corporate SNS. The results shed light on the existence of a contingency role for service attribute on SNS, and further suggest how firms with distinct attributes can effectively allocate their limited resources when encouraging CCB on SNS.