Kai R. Larsen

Threat or coping appraisal: determinants of SMB executives’ decision to adopt anti-malware software

This study presents an empirical investigation of factors affecting small- and medium-sized business (SMB) executives’ decision to adopt anti-malware software for their organizations. A research model was developed by adopting and expanding the protection motivation theory from health psychology, which has successfully been used to investigate the effect of threat and coping appraisal on protective actions. A questionnaire-based field survey with 239 U.S. SMB executives was conducted, and the data were analyzed using partial least squares (PLS). This study demonstrates that threat and coping appraisal successfully predict SMB executives’ anti-malware software adoption intention, leading to SMB adoption. In addition, considerable variance in adoption intention and actual SMB adoption is addressed by social influence from key stakeholders and situation-specific variables, such as IT budget and vendor support. Further, the generalizability of the model was tested using industry type and IS expertise. The adoption intention of IS experts and IT intensive industries was mainly affected by threat appraisal and social influence, while that of non-IS experts and non-IT intensive industries was significantly influenced by coping appraisal and IT budget. Vendor support was a key facilitator of the anti-malware adoption for IS experts and IT intensive industry groups, while IT budget was for non-IS expert and non-IT intensive industry groups. Key implications for theory and practice are discussed.

A Taxonomy of Antecedents of Information Systems Success: Variable Analysis Studies

Research in the information systems (IS) field has often been characterized as fragmented. This paper builds on a belief that for the field to move forward and have an impact on practitioners and other academic fields, the existing work must be examined and systematized. It is particularly important to systematize research on the factors that underlie success of organizational IS. The goal here is to conceptualize the IS success antecedents (ISSA) area of research through surveying, synthesizing, and explicating the work in the domain. Using a combination of qualitative and quantitative research methods, a taxonomy of 12 general categories is created, and existing research within each category is examined. Important lacunae in the direction of work have been determined. It is found that little work has been conducted on the macro-level independent variables, the most difficult variables to assess, although these variables may be the most important to understanding the ultimate value of IS to organizations. Similarly, ISSA research on success variables of consequence to organizations was found severely lacking. Variable analysis research on organizational-level success variables was found to be literally nonexistent in the IS field, whereas research in the organizational studies field was found to provide useful directions for IS researchers. The specifics of the 12 taxonomy areas are analyzed and directions for research in each of them provided. Thus, researchers and practitioners are directed toward available research and receive suggestions for future work to bring ISSA research toward an organized and cohesive future.

Enabling agile adoption practices through network organizations

As distributed organizations increasingly rely on technological innovations to enhance organizational efficiency and competitiveness, interest in agile practices that enable adoption of information technology (IT) based innovations has grown. This study examines the influence of a network organization environment on the ability to develop agile adoption practices. An exploratory case study design was used to investigate the interactions between network structure, social information processing, organizational similarity (homophily), and absorptive capacity during the adoption of a large-scale IT system in two network organization environments within New York State. The data suggest that network organization characteristics and communication processes that reinforced social influence and supported knowledge transfer positively influenced adoption agility. We propose a model of agile adoption practices and discuss implications for the development of theory about network organization characteristics and capabilities to adopt IT-based innovations.

Analyzing unstructured text data: Using latent categorization to identify intellectual communities in information systems

The Information Systems field is structured by the research topics emphasized by communities of journals. The Latent Categorization Method categorized and automatically named IS research topics in 14,510 abstracts from 65 Information Systems journals. These topics were clustered into seven intellectual communities based on publication patterns. The technique develops categories from the data itself, it is replicable, is relatively insensitive to the size of the text units, and it avoids many of the problems that frequently accompany human categorization. As such LCM provides a new approach to analyzing a wide array of textual data.

A cost and performance model for Web service investment

that has attracted the attention of organizations ranging from the largest multinational corporations to the smallest mom-and-pop local businesses. Led by promises of anytime-anyplace contact with customers and suppliers, dissemination of information worldwide through a modest investment of resources, and the ability to conduct e-commerce efficiently and reliably, many organizations have sought to deliver an improved level of service or competitive advantage by way of services delivered on the Web. Need to decide whether developing a Web site or service is worth the financial investment and the trouble? Use this model to calculate the payoff. And get in (at the right level) while you can.

Avatar e-mail versus traditional e-mail: Perceptual difference and media selection difference

The study investigates how knowledge workers perceive avatar e-mail differently from traditional e-mail, and how they select traditional versus avatar e-mail when different levels of task equivocality and different types of communication direction are present. Three field studies were conducted with knowledge workers who have used avatar and traditional e-mail. This study demonstrates that overall perception toward avatar e-mail is significantly different from traditional e-mail with respect to media richness and social presence characteristics. In addition, this study found individuals used different e-mail selection approaches when conducting tasks with different equivocality (high versus low equivocal tasks) and tasks for different communication direction (lateral versus upward). Avatar e-mail users also sent lengthier messages than traditional e-mail users when conducting a highly equivocal task.

Explanation in information systems

Abstract.  Explanation of observed phenomena is a major objective of both those who conduct and those who apply research in information systems (IS). Whereas explanation based on the statistical relationship between independent and dependent variables is a common outcome of explanatory IS research, philosophers of science disagree about whether statistical relationships are the sole basis for the explanation of phenomena. The purpose of this paper is to introduce an expanded concept of explanation into the realm of IS research. We present a framework based on the four principle explanation types defined in modern philosophy: covering‐law explanation, statistical‐relevance explanation, contrast‐class explanation and functional explanation. A well‐established research stream, media richness, is used to illustrate how the different explanation types complement each other in increasing comprehension of the phenomenon. This framework underlies our argument that explanatory pluralism can be used to broaden research perspectives and increase scientific comprehension of IS phenomena above and beyond the methodological and ontological pluralism currently in use in IS research.

Predicting Survey Responses: How and Why Semantics Shape Survey Statistics on Organizational Behaviour

Some disciplines in the social sciences rely heavily on collecting survey responses to detect empirical relationships among variables. We explored whether these relationships were a priori predictable from the semantic properties of the survey items, using language processing algorithms which are now available as new research methods. Language processing algorithms were used to calculate the semantic similarity among all items in state-of-the-art surveys from Organisational Behaviour research. These surveys covered areas such as transformational leadership, work motivation and work outcomes. This information was used to explain and predict the response patterns from real subjects. Semantic algorithms explained 60–86% of the variance in the response patterns and allowed remarkably precise prediction of survey responses from humans, except in a personality test. Even the relationships between independent and their purported dependent variables were accurately predicted. This raises concern about the empirical nature of data collected through some surveys if results are already given a priori through the way subjects are being asked. Survey response patterns seem heavily determined by semantics. Language algorithms may suggest these prior to administering a survey. This study suggests that semantic algorithms are becoming new tools for the social sciences, opening perspectives on survey responses that prevalent psychometric theory cannot explain.

A Mathematical Approach to Categorization and Labeling of Qualitative Data: the Latent Categorization Method

As text databases increasingly become available to researchers, the limits to human cognition are rapidly reached. Focusing on examining objective realities, this paper introduces the latent categorization method, a novel new research method for analysis of large and midsize data sets. This method clusters text artifacts and extracts the words that were most important in creating the clusters. Further, it demonstrates a set of techniques for extracting knowledge from a representative data set involving 6135 abstracts from a variety of business-related journals.

Behavior change interventions: the potential of ontologies for advancing science and practice

A central goal of behavioral medicine is the creation of evidence-based interventions for promoting behavior change. Scientific knowledge about behavior change could be more effectively accumulated using “ontologies.” In information science, an ontology is a systematic method for articulating a “controlled vocabulary” of agreed-upon terms and their inter-relationships. It involves three core elements: (1) a controlled vocabulary specifying and defining existing classes; (2) specification of the inter-relationships between classes; and (3) codification in a computer-readable format to enable knowledge generation, organization, reuse, integration, and analysis. This paper introduces ontologies, provides a review of current efforts to create ontologies related to behavior change interventions and suggests future work. This paper was written by behavioral medicine and information science experts and was developed in partnership between the Society of Behavioral Medicine’s Technology Special Interest Group (SIG) and the Theories and Techniques of Behavior Change Interventions SIG. In recent years significant progress has been made in the foundational work needed to develop ontologies of behavior change. Ontologies of behavior change could facilitate a transformation of behavioral science from a field in which data from different experiments are siloed into one in which data across experiments could be compared and/or integrated. This could facilitate new approaches to hypothesis generation and knowledge discovery in behavioral science.