Challenging Dominant Frames in Policies for IS Innovation in Healthcare through Rhetorical Strategies
Roberta Bernardi, Panos Constantinides, Joe Nandhakumar
Information Systems (IS) innovation in healthcare is a contested area often characterized by complex and conflicted relationships among different stakeholders. This paper aims to provide a systematic understanding of the mechanisms through which competing visions about health sector reforms are translated into policy and action generating contradictions in IS innovation. The paper argues that we can learn more about the source of such contradictions by examining how competing frames can affect IS innovation in healthcare. We adopt frame theory and rhetorical strategies analysis in the case of health sector reforms in Kenya, with a specific focus on the deployment of health information systems. We make the following contributions. First, we demonstrate that policy actorsí adherence to the interests and values represented in a frame is important in determining the choice of a rhetorical strategy and its influence on policy transformation and IS innovation. Second, we develop an understanding of how technology mediates the rhetorical strategies of different actors. In particular, we demonstrate the role of technology in giving continuity to frames, thus affecting policy change and IS innovation.
An Integrated Temporal Model of Belief and Attitude Change: An Empirical Test With the iPad
Jingjun (David) Xu, Sue Abdinnour, Barb Chaparro
Bhattacherjee and Premkumar (2004) proposed a temporal model of belief and attitude change to understand fluctuating patterns of Information Technology (IT) usage. However, they overlooked the role of perceived enjoyment in the process of temporal IT usage. PE has become an increasingly important part of how consumers assess technology. Thus, we build on Bhattacherjee and Premkumarís model (BP model), and propose a new model by investigating the comparative role of perceived usefulness (PU) and perceived enjoyment (PE), for explaining temporal changes in usersí beliefs and attitudes toward IT usage. We name our proposed model the Integrated Temporal Model (ITM). Through an empirical study of iPad usage in a classroom setting at three different stages, we found that 1) the effect of PE on PU is stronger at the pre-usage (vs. post-usage) stage, and its effect is stronger at the initial-usage (vs. later-usage) stage; 2) While PE has a stronger effect than PU on attitude at both pre-usage and initial-usage stages, it is not the case at the later-usage stage; Instead, the effect of PU on attitude is stronger at the later-usage (vs. initial-usage) stage; 3) the effect of disconfirmation on modified PU and PE is stronger at the initial (vs. later) stage, while the effect of PU and PE expectation on modified PU and PE is weaker at the initial (vs. later) stage. Our study is the first of its kind to examine the combined and relative impact of PU and PE on temporal usage of IT with hedonic elements.
Use of Online Social Networking Services from a Theoretical Perspective of Motivation-Participation-Performance Framework
Dan Kim, Mohammad Salehan, Changsu Kim
Social networking services (SNS) are platforms to form and manage personal connections and create a foundation for human relationships. Intending to identify why, how, and for what outcome users use SNS, this study contributes to the body of knowledge by analyzing how motivation, participation, and performance are related to each other in the SNS context. Drawing upon a theoretical perspective of motivation-participation-performance framework, we identify four significant why-motivations (i.e., vertical social, horizontal social, hedonic, and utilitarian motivations), two main ways (how) of participation (i.e., sharing and collaboration), and two ultimate benefits (for what outcome) of SNS use (i.e., personal and job performance). The analyzed results of empirical data collected from SNS users indicate that the identified motivations significantly influence participation in sharing and collaboration activities on SNS; and SNS participation significantly affects personal and professional/job-related performance. This study contributes to theory by providing a multidimensional view of SNS use, its predictors, and its consequences.
Political Maneuvering During Business Process Transformation: A Pluralist Approach
Sune Müller, Lars Mathiassen, Carol Saunders, Pernille Kræmmergaard
For years, managers have tried to improve organizational performance through Business Process Transformation (BPT), and their experiences have informed IS research and practice. Although extant theory acknowledges the political nature of these dynamic transformation initiatives, researchers have yet to empirically investigate and theorize how organizational politics impacts BPT behaviors and outcomes. Drawing on a pluralist methodology, we present an embedded case study of a company-wide BPT project across four business units at the high-tech firm Terma. First, we apply different perspectives on organizational politics to develop detailed accounts of each business unit's response to the transformation initiative, revealing four distinct patterns of BPT politics: applying-the-hammer, struggling-to-engage, walking-the-talk, and keeping-up-appearances. Next, we combine the empirical findings with extant literature to theorize how transformation agents and process users engage in politics during BPT implementation. As a result, our research leverages a pluralist approach to show how alternative political perspectives and forms of politics can help managers maneuver BPT initiatives in their roles as transformation agents and process users.
Research Perspectives: Management Misinformation Systems- A Time to Revisit?
Kalle Lyytinen, Varun Grover
In this essay, we revisit Ackoffís classic ìManagement Misinformation Systemsî and its five myths. The article was published at the dawn of the discipline and therein Ackoff shattered popular assumptions about Information Systemsí (IS) design and use. The manuscript shaped the direction and scope of scholarly discourse around information systems; in contrast to dominant claims at that time, he argued that managers swam in the abundance of irrelevant information, were victims of poor modeling and consequent understanding of their own decisions, participated in destructive communication due to conflicting goals, and had a poor understanding of how systems work. Despite the passage of 50 years (and many revolutions in Information Technology), Ackoffís arguments are still regarded as valid and rarely debated. Yet, given the new information-rich environments and our nearly limitless capability to collect and analyze data, these arguments may need to be re-examined to frame correctly contemporary effects of information systems on managerial decision making. We scrutinize with a critical eye Ackoffís five assumptions in light of todayís IT and data rich environments and identify key tenets that will reframe the disciplinary discourse concerning the effects of information systems. We identify significant shifts in research on decision making including the role of abduction, data layering and options and intelligence augmentation. The manuscript honors the extraordinary legacy of this Ackoffís remarkable paper as an IS scholar by shaping the fieldís future inquiries in the spirit of the original paper.
The Online Waiting Experience: Using Temporal Information and Distractors to Make Online Waits Feel Shorter
Younghwa Lee, Andrew Chen, Traci Hess
Research on how to manage the online waiting experience is starting to emerge but has primarily focused on the use of distracting cues for online wait management (e.g., text and images that distract the user from the wait). The use of temporal information in waiting pages (e.g., text and images that convey the duration of the wait) has received little attention from the information systems literature, and there is limited understanding of how the two types of cues, temporal information and distractors, affect wait time estimation. We address this gap by developing a theoretical model of how these cues affect the waiting experience and perceived waiting time. The model is tested with a 2x2x2x2 controlled lab experiment and 1025 participants using progress bar treatments that include temporal information (spatial and text description of the wait time duration), and distractors (progress bar animation and accelerated filling of the progress bar), with both short and long wait conditions. Our findings suggest that the two types of cues reduce perceived waiting time through different nomological paths. Temporal cues reduce perceived uncertainty about the wait, while distractor cues direct attention away from the wait, increasing perceived enjoyment and wait time distortion, with the enhanced waiting experience reducing the perceived waiting time. Further, these cues are effective in managing the online waiting experience with both short and long waits.
Brownie: A Platform for Conducting NeuroIS Experiments
Anuja Hariharan, Marc Adam, Verena Dorner, Ewa Lux, Marius M¸ller, Jella Pfeiffer, Christof Weinhardt
In the field of NeuroIS, experimental software needs to simultaneously present experimental stimuli to participants while recording, analyzing, or displaying neurophysiological measures. For example, a userís heart beat (neurophysiological measure) might be recorded during interaction with an e-commerce website (stimulus) to track changes in user arousal, or a user might be shown changing arousal levels during an exciting game. In this paper, we identify requirements for a NeuroIS experimental platform that we call Brownie, and present its architecture and functionality. We then evaluate Brownie by means of a literature review and a case study that demonstrates Brownieís capability to meet the requirements in a complex research context. We also verify the usability of Brownie by means of a quantitative study with prospective experimenters who implemented a test experiment in Brownie and an alternative software. The salient features of Brownie can be summarized as follows: (i) it integrates neurophysiological measurements, (ii) it incorporates real-time processing of neurophysiological data, (iii) it facilitates research on individual and group behavior in the lab, (iv) it offers a large variety of options for presenting experimental stimuli, and (v) it is open-source and easily extensible with open-source libraries. In summary, we conclude that Brownie is path breaking in its potential to reduce barriers for IS researchers by fostering replicability and research collaboration, and supports NeuroIS as well as interdisciplinary research in cognate areas, such as management, economics, or human-computer interaction.
Guidelines for Conceptual Modeling of User-generated Content
Roman Lukyanenko, Jeffrey Parsons, Yolanda Wiersma, Gisela Wachinger, Benjamin Huber, Robert Meldt
The increasing reliance of organizations on externally produced information, such as online user-generated content (UGC), challenges common assumptions about conceptual modeling in information systems development. This paper evaluates these assumptions in the context of online citizen science that relies on UGC to collect data from ordinary people to support scientific research. Using a theoretical approach based in philosophy and psychology, we develop alternative guidelines for modeling UGC, demonstrate how the proposed guidelines have been implemented in a real information system and provide interview evidence on user impressions of the system. We conclude by considering implications for conceptual modeling research and practice.
An Extensive Examination of Linear Regression Models with a Binary Outcome Variable
Galit Shmueli, Suneel Babu Chatla
Linear regression is among the most popular statistical models in social sciences research. Linear probability models (LPMs) - linear regression models applied to a binary outcome - are used in various disciplines. Surprisingly, LPMs are rare in the IS literature,where logit and probit models are typically used for binary outcomes. LPMs have been examined with respect to specific aspects, but a thorough evaluation of their practical pros and cons for different research goals under different scenarios is missing. We perform an extensive simulation study to evaluate the advantages and dangers of LPMs, especially with big data that is now common in IS research. We evaluate LPM for three common uses of binary outcome models: inference and estimation, prediction and classification, and selection bias. We compare its performance to logit and probit under different sample sizes, error distributions, and more. We find that coefficient directions, statistical significance, and marginal effects yield results similar to logit and probit. In addition, LPM estimators are consistent for the true parameters up to a multiplicative scalar. This scalar, although rarely required, can be estimated assuming an appropriate error distribution. For classification and selection bias, LPM is on par with logit and probit in terms of class separation and ranking, and is a viable alternative in selection models. LPM is lacking when the predicted probabilities are of interest, because predicted probabilities can exceed the unit interval. We illustrate some of these results by modeling price in online auctions, using data from eBay.