پذیرش نوآوری IT توسط شرکت : کشف دانش از طریق تجزیه و تحلیل متنی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|2336||2013||11 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Decision Support Systems, Volume 54, Issue 2, January 2013, Pages 1044–1054
Enterprise adoption of information technology (IT) innovations has been a topic of tremendous interest to both practitioners and researchers. The study of technological, managerial, strategic, and economic factors as well as adoption processes and contexts has led the field to become a rich tapestry of many theoretical and conceptual foundations. This paper provides a comprehensive multi-disciplinary classification and analysis of the scholarly development of the enterprise-level IT innovation adoption literature by examining articles over the past three decades (1977–2008). We identify 472 articles and classify them by functional discipline, publication, research methodology, and IT type. The paper applies text analytic methods to this document repository to (1) identify salient adoption determinants and their relationships, (2) discover research trends and patterns across disciplines, and (3) suggest potential areas for future research in IT innovation adoption at the enterprise level.
Adoption of information technology (IT) innovations has been a topic of significant interest to researchers and practitioners over the past three decades (e.g. ,  and ). Broadly, there are two complementary perspectives on IT innovation adoption: the first and more frequently examined perspective includes the adoption of IT innovations by the individual user. Often referred to as the bottom–up view, individual IT innovation adoption research has focused on user characteristics, behavioral motivation, and contextual elements. Enterprise IT innovation adoption on the other hand focuses on the firm and firm-level characteristics.1 This perspective has gained particular interest due to enterprises' increasing dependence on IT as well as some highly publicized successes and failures over the past two decades. Consequently enterprise-level IT innovation adoption studies have focused on why, how, and under what conditions enterprises have succeeded or failed in adopting and implementing IT innovations. These issues have been examined for a wide range of different IT innovations, including enterprise information systems, electronic commerce, database management systems, network and telecommunications infrastructure, computer hardware, enterprise architecture components, and business productivity applications, among many others. As a result, previous studies have identified drivers and inhibitors, explored the influence of important technological, individual, organizational, strategic, economic, and managerial, and environmental factors, and examined key processes and stages associated with the adoption of IT innovations. Because IT touches upon virtually all aspects of an enterprise's value chain, researchers have drawn on theories, frameworks and models from a variety of complementary academic reference disciplines such as information systems, computer science, economics, organizational sciences, marketing, and strategic management. In doing so, enterprise adoption research has thus become a rich tapestry of a plethora of theoretical and conceptual foundations. Despite arguments that research within this domain is exhausted, enterprise adoption of IT continues to be a topic of interest to decision makers, managers, vendors, and users alike. Practitioners have argued that within corporate IT, the pace of technology change has increased so significantly that executives who do not embrace IT innovations at some level risk ending up behind the competition. Indeed, in today's global and competitive environment, IT innovations can provide enterprises with the ability to streamline and transform their organization, create new forms of organization, provide enhanced collaboration capabilities, generate new competitive advantages, and potentially enable them access to new industries and markets ,  and . However, IT innovation adoption strategies must be carefully evaluated and balanced as IT budgets are tightening. Consequently, with the continuous emergence of IT innovations, there will be not only a need but also an opportunity to understand and study how and why enterprise adoption occurs, what contextual factors have changed, what fundamental value IT innovations can deliver, and in what ways they align or transform corporate strategy. As a result, we anticipate that IT innovation adoption research will continue to proliferate. The objective of this paper is not to suggest new theories or propositions concerning enterprise adoption of IT innovations, as there is no lack of these. Given the vast nature and diversity of the enterprise adoption literature, there is also no attempt made to offer a comprehensive recitation of research findings or methodologies. The purpose is, rather, to provide a sufficient assessment of the current state of enterprise adoption research by providing a comprehensive classification and analysis of the scholarly development of the literature. Our work differs from previous literature review and meta-analytic studies in several ways ,  and . While IT innovation adoption is predominantly a phenomenon studied by information systems researchers, it is a complex topic that touches upon a plethora of issues central to other disciplines, including operations management, strategy, marketing, and organizational behavior. Consequently, examination of solely IS publications would not provide an accurate and holistic reflection of IT innovation adoption research . In contrast to earlier review studies, this paper therefore does not focus only on IS studies or research in a single discipline, but examines all relevant academic disciplines. A second key difference of this study is the application of text analytic methods to identify, explore, and evaluate the rich literature on enterprise adoption of IT innovations. Traditionally, literature reviews required manual identification, evaluation, and coding of relevant text sources. The resulting process is extraordinarily resource-intensive; consequently, researchers often limit the scope and scale of their analysis. Often times, review studies focused their assessments on title, abstracts, and keywords of research articles only, which has been identified as a clear limitation for comprehensive analysis of a topic domain . More recently, it has been argued that text mining techniques could significantly improve literature assessments as they enable researchers to examine both structured and unstructured full-text data more rapidly and accurately . In this paper, we harvest this power by using a full-text mining approach to extract key phrases and information, identify central themes, and apply concept linking. Lastly, a key difference of this paper is the examination of over three decades of IT innovation adoption research. In contrast to most previous review studies, which limited their analysis to five to ten-year time spans, we provide a comprehensive picture of the scholarly development and trajectory of research and are thus able to identify important longitudinal trends in enterprise-level IT innovation adoption topics, methods, drivers, inhibitors, and contexts. The remainder of this paper is structured as follows. Section 2 describes the research methodology and text mining technique used to conduct the multi-disciplinary literature classification and analysis. Section 3 presents the results of the study. Research implications are discussed in Section 4. The paper concludes in Section 5.
نتیجه گیری انگلیسی
This study provided a comprehensive multi-disciplinary classification and analysis of the scholarly development of the enterprise-level IT innovation adoption literature. We identified 472 articles from leading journals over the past three decades and classified them by functional discipline, publication, research methodology, and IT type. We introduced and applied text analytic methods to this document repository to identify salient adoption determinants and their relationships, discover research trends and patterns across disciplines, and suggest potential areas for future research. Our paper makes several important contributions. From a theoretical perspective, this paper contributes to our overall understanding of IT innovation adoption by enterprises and the impact of organizational, technological, and environmental determinants. From a methodological perspective, we demonstrate the design, applicability, and value of text analytics for multi-disciplinary knowledge discovery and literature reviews. Our study is one of the few studies in the organizational and management sciences that has used text analytics for gaining insights into a broad set of literature. Our approach can easily be applied to other topical areas or functional domains, thus providing a systemic mean to evaluate the growing and increasingly multi-disciplinary body of knowledge. Our study does have some limitations. First, we did not set a common starting date across journals since our objective was to be inclusive of all relevant articles. This may have impacted our comparative analysis of journals since some have been in circulation longer than others. Another limitation is the inclusion of the relatively larger number of IS/CS journals. This may have skewed our results since there is a greater probability that an article on IT innovation adoption exists in IS/CS. However, our goal was to identify articles in high quality journals applicable to the study of IT innovation adoption. From a text mining perspective, one challenge we faced was when results in a study were summarized in a table format, but only one occurrence of determinant significance occurred; based on our proximity approach multiple determinants could have been deemed significant. Understanding the “layout” of a document thus becomes important and an interesting future research for text analytics. While text mining could have also been used to automatically extract articles from online databases, without any editorial filtering, our analysis was seeded with articles and journals identified in an earlier comprehensive study conducted by the authors. Each of these limitations presents intriguing opportunities for future research.