Dirk S. Hovorka

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.

Quantitative approaches to content analysis: identifying conceptual drift across publication outlets

Unstructured text data, such as emails, blogs, contracts, academic publications, organizational documents, transcribed interviews, and even tweets, are important sources of data in Information Systems research. Various forms of qualitative analysis of the content of these data exist and have revealed important insights. Yet, to date, these analyses have been hampered by limitations of human coding of large data sets, and by bias due to human interpretation. In this paper, we compare and combine two quantitative analysis techniques to demonstrate the capabilities of computational analysis for content analysis of unstructured text. Specifically, we seek to demonstrate how two quantitative analytic methods, viz., Latent Semantic Analysis and data mining, can aid researchers in revealing core content topic areas in large (or small) data sets, and in visualizing how these concepts evolve, migrate, converge or diverge over time. We exemplify the complementary application of these techniques through an examination of a 25-year sample of abstracts from selected journals in Information Systems, Management, and Accounting disciplines. Through this work, we explore the capabilities of two computational techniques, and show how these techniques can be used to gather insights from a large corpus of unstructured text.

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.

Member engagement within digitally enabled social network communities: New methodological considerations

Digitally enabled social networks (DESN) are a complex assemblage of engagement, reflection, action, technology, organization and community. DESN create a unique challenge for researchers who aim to understand what social networks are, what they can become and what enablers and constraints underlie trajectories of member engagement. As DESN continually evolve, knowing them as stable and reified representations or as mere technology artefacts provides a limited understanding of their complexity and emergent properties. While DESN are, in part, the technology that supports the necessary actions for engagement, they are also the people and behaviours that constitute its community. Through the presentation of new methodological considerations towards Digg, a large DESN, we observe that social networks entail practices of engagement, change and evolution within a DESN community. We reveal how engagement is a communal endeavour and that the clash of socio‐technical trajectories can result in the emergence of new paths of member participation. Our findings demonstrate the potential of netnography and impressionist tales for contributing to the ongoing pluralistic investigations of DESN and also inform research on engagement and community design and change.

Reflecting, Tinkering, and Tailoring: Implications for Theories of Information System Design

The design and embedding of technical artifacts in complex task, social, and organizational environments is fundamental to IS. Yet in Design Science Research (DSR) and in the information system development process, the role of the humans who will use the system has been marginalized to that of a source in a requirements elicitation process, a subject in participatory design, or worse, a “user” of the designed technological artifact (Bannon 1991). While recent research (Kensing et al. 1998; Kensing and Blomberg 1998; Grudin and Pruitt 2002) has positioned end-users as participants involved in the design process, this work has largely focused on the primary design phase of technology artifacts. We have not seen a conscious, research driven approach which posits people as free, intelligent, and intentional designers in the ongoing recreation of information systems through a process of secondary design in the context of use. The hegemony of artifact design is so strong that workers’ deviation from prescribed uses of information systems and the creation of workarounds is frequently viewed as resistance (Ferneley and Sobreperez 2006) rather than as a secondary design process to tailor a system to fit the user’s situated tasks, metaphors, and use patterns. Although a number of recent special journal issues have addressed Design Science Research, few researchers focus attention on the activities of the humans using the systems. Nowhere is the human actor considered a designer in her own right. Yet an increasing number of technologies are intended to be tailored for the creation of information environments, where actors in the information process reflect on the context, tasks, and technologies to tinker with the system and tailor it to suit their own metaphors and use patterns (Germonprez et al. 2007).

Developing Interfield Nomological Nets

As behavioral research has expanded in Information Systems and other scientific fields, researchers are recognizing that construct proliferation increases the difficulty in identifying the nomological networks of constructs pertaining to any given research question. An Inter-Nomological Network uses semantic analysis to systematically identify, categorize, and predict relationships among the constructs that define the combined cognitive interest of behavioral scientific fields. Researchers can thereby identify concentrations in behavioral research around similar phenomena related to human experiences that transcend field boundaries, and that may in fact have common cognitive underpinnings. Interfield theory development is supported by discovery of nomological relationships between scientific fields. Preliminary results demonstrating confirmatory, exploratory, and interfield research applications are presented.

Incommensurability and Multi-paradigm Grounding in Design Science Research: Implications for Creating Knowledge

The problem identification–design–build–evaluate–theorize structure of design science research has been proposed as an approach to creating knowledge in information systems and in broader organizational and social domains. Although the approach has merit, the philosophical foundations of two specific components warrant attention. First, the grounding of design theory on potentially incommensurate kernel theories may produce incoherent design theory. In addition, design theory has no strong logical connection to kernel theories, and so cannot be used to test or validate the contributing kernel theories. Second, the philosophical grounding of evaluation may inadvertently shift from functionally based measures of utility and efficiency, to evaluation based on the pragmatic fulfillment of multidimensional human actions as people encounter information systems, resulting in evaluation errors. Although design and evaluation from a single paradigm is not desirable, sufficient, or representative of design science research, multi-paradigm grounding of design and evaluation must be realized and used consciously by the research community if the design science approach is to remain a legitimate approach to knowledge creation.

A Meta-theoretic Approach to Theory Integration in Information Systems

This research presents a meta-theoretic analysis of a nomological net for the purpose of identifying potential pathways for theory integration and multi-level theory development. Success in these two areas holds the potential to reduce theory clutter in IS and related social sciences. As a proof-of-concept, we identify theory domains that share ancillary variables or functional/structural components, using a 20-year sample of construct-based quantitative research published in core journals of the IS discipline. Identification of shared variables provides possible extension and integration development that will reduce theory fragmentation and may lead to discovery of fundamental unifying processes that underlie phenomena across disciplines.

Theories in Business and Information Systems Engineering

Even though the idea of science enjoys an impressive reputation, there seems to be no precise conception of science. On the one hand, there is no unified definition of the extension of activities subsumed under the notion of science. According to the narrow conception that is common in Anglo-Saxon countries, science is restricted to those disciplines that investigate nature and aim at explanation and prediction of natural phenomena. A wider conception that can be found in various European countries includes social sciences, the humanities and engineering. On the other hand and related to the first aspect, there is still no general consensus on the specific characteristics of scientific discoveries and scientific knowledge.