How Software Startups Survive: a Model based on Resource-based View and Dynamic Capabilities

Rapid advances of IT have encouraged many software startups to enter the market place. Our research seeks to investigate the little-studied phenomenon of startup survival. The research is based on the resource-based view and on dynamic capabilities. We present a research model that investigates factors affecting software startup survival by examining how startups survive in their competitive environment. First, we categorize the resources of software startups into three areas based on socio-technical theory. Specifically, we view entrepreneurial resources as influencing both IT innovation and environmental resources. Second, we view dynamic capabilities as mediating the relationship between interactions among resources and software startup survival. Finally, we investigate the effect of competitive actions in moderating the relationship between dynamic capabilities and software startup survival. Our research seeks to contribute to extant research by proposing the first empirical study of which we are aware that addresses factors influencing software startup survival

The Nature of IT Firms: A Systemic Literature Review and Analysis

IT firms have grown in economic importance in the last few decades. It is the interest of both practitioners and researchers across a range of business management and information system disciplines. Having conducted a systemic literature review, we proposed a new conceptual definition and conceptual frameworks which together provides a deeper understanding of the essence of “IT firms”. The definition we proposed of IT firm offers a way forward for identification of IT firms with increased understanding and a clear boundary. The conceptual framework synthesized the characteristic dimensions of IT firms. It not only improves current understanding of IT firms but also provides a solid foundation for further studies of IT firms. The types of IT firms developed in this paper offers a means of classifying different IT firms. The IT firm development framework in relation to innovation focus and improvisational capabilities not only advances understanding about IT firms, but also provides further research opportunities towards empirical testing and knowledge building. The insights discovered in the paper are also useful for business practitioners in strategy and consulting. The frameworks provide useful knowledge and advice for IT firms within the industry

A conceptual model of personalized virtual learning environments

The Virtual Learning Environment (VLE) is one of the fastest growing areas in educational technology research and development. In order to achieve learning effectiveness, ideal VLEs should be able to identify learning needs and customize solutions, with or without an instructor to supplement instruction. They are called Personalized VLEs (PVLEs). In order to achieve PVLEs success, comprehensive conceptual models corresponding to PVLEs are essential. Such conceptual modeling development is important because it facilitates early detection and correction of system development errors. Therefore, in order to capture the PVLEs knowledge explicitly, this paper focuses on the development of conceptual models for PVLEs, including models of knowledge primitives in terms of learner, curriculum, and situational models, models of VLEs in general pedagogical bases, and particularly, the definition of the ontology of PVLEs on the constructivist pedagogical principle. Based on those comprehensive conceptual models, a prototyped multiagent-based PVLE has been implemented. A field experiment was conducted to investigate the learning achievements by comparing personalized and non-personalized systems. The result indicates that the PVLE we developed under our comprehensive ontology successfully provides significant learning achievements. These comprehensive models also provide a solid knowledge representation framework for PVLEs development practice, guiding the analysis, design, and development of PVLEs. (c) 2005 Elsevier Ltd. All rights reserved

The Moderating Effect of Social Influence on Ethical Decision Making in Software Piracy

Software piracy has been a serious problem for decades and continues to cost software industry some billions of dollars each year. In this study we examine the Normative and Informational Social Influences that affect an individual’s decision to buy or use pirated software. Based upon previous research about ethical decision making in software adoption, and consumer susceptibility to social influence, we develop a research model designed to test the moderating effects of social influence on the ethical decision making process. An online survey is conducted to collect data. Our research extends the knowledge about software piracy and provides valuable and important insights for researchers, practitioners and policy/strategy makers in government

Uncovering Digital Platform Generativity: A Systematic Literature Review

Generativity is identified as the driver for digital innovation and platform growth by engaging a large number of actors with diverse skills. Generativity is also the signal of innovation, and it enables innovative process self-reinforcement, which leads the digital platforms to evolve in unanticipated ways. However, with the proliferation of generativity in the Information Systems (IS) literature growing, we find the understanding of generativity is inconsistent. We conduct a systematic literature review to clear the understanding mist and advance the understanding of generativity. Our study shows that generativity is a social-technical system in which social actors interact with each other by employing digital technologies. Generativity is not unequivocally positive to the digital platform due to the inherent tension but requires deliberate actions by the platform owners. Our study contributes to IS research by providing a comprehensive conceptual framework of digital platform generativity

HOW PARTS CONNECT TO WHOLE IN BUILDING DIGITAL GENERATIVITY IN DIGITAL PLATFORM ECOSYSTEMS

Generativity drives digital innovation and platform growth by engaging many other businesses with diverse digital skills and resources in a digital platform. As the proliferation of generativity research grows, the Information Systems (IS) literature demonstrates the basic understanding of this notion in the areas of properties of digital technologies, social events, and/or the interaction between these two without an integrated view of how generativity is raised to enable the digital innovation. Therefore, considering that digital platforms are a kind of ecosystem, we aim to develop a new understanding of this emerging phenomenon by employing a holistic perspective. Through the information ecology theoretical lens, we develop a digital generativity process model that explains how the technological and social resources interact to generate perpetual digital innovation in digital platform ecosystems (DPE). This study contributes to generativity research by providing a dynamic and holistic view of generativity formalization in DPEs

Effects of Source and Content on the Retransmission of Rumor, Information, and Misinformation on Social Media

In recent years, the use of social media has become a fact of life for civil society. While social media can be used to provide up-to-date, locally relevant information, making it an effective coordination tool in social movements, it also can be used to spread fake messages that can harm individuals, organizations, and even society. To date, research on social media use has focused on sharing general information; there is a significant need to extend our understanding of message retransmission on social media that accounts for the full spectrum of possibilities (information, misinformation and rumor). This research-in-progress paper proposes a model and a planned empirical approach that can provide such an integrative account. Our starting point is the observation that the presentation of a message—particularly the presentation of message source and content—is a key driver of message retransmission. Our work offers potentially important implications for research and practice

The dynamic predictive power of company comparative networks for stock sector performance

As economic integration and business connections increase, companies actively interact with each other in the market in cooperative or competitive relationships. To understand the market network structure with company relationships and to investigate the impacts of market network structure on stock sector performance, we propose the construct of a company comparative network based on public media data and sector interaction metrics based on the company network. All the market network structure metrics are integrated into a vector autoregression model with stock sector return and risk. Several findings demonstrate the dynamic relationships that exist between sector interactions and sector performance. First, sector interaction metrics constructed based on company networks are significant leading indicators of sector performance. Interestingly, the interactions between sectors have greater predictive power than those within sectors. Second, compared with the company closeness network, the company comparative network, which labels the cooperative or competitive relationships between companies, is a better construct to understand and predict sector interactions and performance. Third, competitive company interactions between sectors impact sector performance in a slower manner than cooperative company interactions. The findings enrich financial studies regarding asset pricing by providing additional explanations of company/sector interactions and insights into company management using industry-level strategies