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Various systems development life cycles and business development models have been popularized by information systems researchers and practitioners over a number of decades. In the case of systems development life cycles, these have been targeted at software development projects within an organization, typically involving analysis, design, programming, testing, and deployment. For business development models, phase-based approaches for developing generic businesses have been proposed. With the recent surge in popularity of online businesses, and particularly web-based hosted services for online start-ups, a gap has emerged in the information systems literature for development processes specifically tailored to developing internet-based retail businesses. In this paper, we present such a process, which we dub the "Rainmaker" process for developing internet-based businesses. We demonstrate, through a real case study, how the Rainmaker model can be successfully applied.

In this study, we develop a model that assesses product risk using online reviews from Amazon.com. We first identify unique words and phrases capable of identifying hazards. Second, we estimate risk severity using hazard type weights and risk likelihood using total reviews as a proxy for sales volume. In addition, we obtain expert assessments of product hazard risk (risk likelihood and severity) from a sample of high‐ and low‐risk consumer products identified by a computerized risk assessment model we have developed. Third, we assess the validity of our computerized product risk assessment scoring model by utilizing the experts' survey responses. We find that our model is especially consistent with expert judgments of hazard likelihood but not as consistent with expert judgments of hazard severity. This model helps organizations to determine the risk severity, risk likelihood, and overall risk level of a specific product. The model produced by this study is helpful for product safety practitioners in product risk identification, characterization, and mitigation. [ABSTRACT FROM AUTHOR]

In the natural sciences, collecting, cataloguing, and comparing living specimens have long been a popular, collaborative mode of discovery and learning. New species are discovered, and the relationships between species are theorized. From Aristotle's "History of Animals" to Darwin's "On the Origin of Species", and beyond, this process of learning through expedition has yielded bountiful knowledge and insights to students of science. In this paper, we propose that expeditionary learning can be applied to a softer science: the field of information systems. In the field of information systems, specimens (information systems) are created by humans, evolving with great speed. Expeditionary learning--where students collaboratively discover, catalog, and compare rapidly evolving information system specimens--is an innovative approach for learners in the IS field, and is effective in increasing IS program visibility and delivering significant, measurable community impact. Our results indicate that learning by expedition has a strong, positive community impact through educating external learners (such as high school and community college students, and the general public), and compares favorably to conventional experiential and service learning styles. Though expeditionary learning does not yield improved course evaluations, we demonstrate that learners' perceived self-knowledge and intentions to pursue the subject area are improved.

Active, experiential learning is an important component in information systems education, ensuring that students gain an appreciation for both practical and theoretical information systems concepts. Typically, students in active, experiential classes engage in real world projects for commercial companies or not-for-profit organizations. In the latter case, such engagements are often referred to as "service learning" or "community-engaged education". In this paper, we describe a novel capstone information systems class where, instead of undertaking a conventional single-team, single-project experiential engagement, the students initiated a fully-fledged new not-for-profit organization from the ground up. The not-for-profit organization, The Online Business Guidebook, was founded with the mission of providing public education on how to start and grow an online business. In an unusual twist on a typical e-commerce class, the students both implemented e-commerce technologies ("active learning by "doing"") and created and disseminated e-commerce training materials ("reflective learning / learning by "teaching""), rather than solely being recipients of instructional resources. This paper describes the manner in which this class was run, the learning outcomes set and evaluation methods used, problems encountered, and recommendations. We propose a replicable model and specific learning outcomes for information systems educators who wish to teach e-commerce classes with an active and reflective pedagogical approach. (Contains 3 figures.)

Food contamination and food poisoning pose enormous risks to consumers across the world. As discussions of consumer experiences have spread through online media, we propose the use of text mining to rapidly screen online media for mentions of food safety hazards. We compile a large data set of labeled consumer posts spanning two major websites. Utilizing text mining and supervised machine learning, we identify unique words and phrases in online posts that identify consumers' interactions with hazardous food products. We compare our methods to traditional sentiment‐based text mining. We assess performance in a high‐volume setting, utilizing a data set of over 4 million online reviews. Our methods were 77–90% accurate in top‐ranking reviews, while sentiment analysis was just 11–26% accurate. Moreover, we aggregate review‐level results to make product‐level risk assessments. A panel of 21 food safety experts assessed our model's hazard‐flagged products to exhibit substantially higher risk than baseline products. We suggest the use of these tools to profile food items and assess risk, building a postmarket decision support system to identify hazardous food products. Our research contributes to the literature and practice by providing practical and inexpensive means for rapidly monitoring food safety in real time. [ABSTRACT FROM AUTHOR]

As patient satisfaction is heavily linked to their choice of provider and medical outcomes, hospital administrations routinely consider a bevy of factors to improve patient satisfaction. These considerations are complex, so targeting the most important areas for improvement is challenging. However, consumers' online reviews of their hospital experience provide a vital lens into the factors associated with their satisfaction. In this study, we use a large dataset of Facebook reviews to construct a taxonomy of potential service attributes that consumers discuss online. We find partial overlap between this taxonomy and prior works and more traditional survey measures; the specific mix of service attributes found in these reviews is unique. Next, we utilize regression modeling to determine which service attributes are most closely associated with star ratings, which we use to measure overall satisfaction. This study demonstrates that mentions of waiting times, treatment effectiveness, communication, diagnostic quality, environmental sanitation, and cost considerations tend to be most associated with patients' overall ratings. Finally, we construct text analyses to rapidly detect consumers' mentions of these service attributes in an automated manner. We derive a set of "smoke terms," or terms especially prevalent in posts that mention specific service attributes. We find that these are generally non‐emotive terms, indicating limited utility of traditional sentiment analysis. Managerially, this information helps to prioritize the areas in greatest need of improvement. Additionally, generating smoke terms for each service attribute aids health care policy makers and providers in rapidly monitoring concerns and adjusting policies or resources to improve service. [ABSTRACT FROM AUTHOR]

The recent surge in the usage of social media has created an enormous amount of user-generated content ( UGC). While there are streams of research that seek to mine UGC, these research studies seldom tackle analysis of this textual content from a quality management perspective. In this study, we synthesize existing research studies on text mining and propose an integrated text analytic framework for product defect discovery. The framework effectively leverages rich social media content and quantifies the text using various automatically extracted signal cues. These extracted signal cues can then be used as modeling inputs for product defect discovery. We showcase the usefulness of the framework by performing product defect discovery using UGC in both the automotive and the consumer electronics domains. We use principal component analysis and logistic regression to produce a multivariate explanatory analysis relating defects to quantitative measures derived from text. For our samples, we find that a selection of distinctive terms, product features, and semantic factors are strong indicators of defects, whereas stylistic, social, and sentiment features are not. For high sales volume products, we demonstrate that significant corporate value is derivable from a reduction in defect discovery time and consequently defective product units in circulation. [ABSTRACT FROM AUTHOR]