434 research outputs found

    A Framework and Model for Understanding the Creation and Sources of Trust

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    With the advent of the internet and rise of less personal, face to face interaction in online commerce, as well as increasing reports of fraud and security breaches, trust has become a critical part of conducting business in the digital economy. This paper develops a framework and model for understanding and building trust by combining some landmark research on the creation and production of trust with the dimensions of trust identified in the literature: ability, benevolence and integrity. By combining these dimensions in a matrix with the types of trust production based on characteristics, process and institutions, the paper develops a robust 3 x 3 matrix which to categorize and understand trust production and sources of trust. This framework can help researchers, practitioners and consumers understand trust creation and assist businesses in developing a comprehensive strategy for managing trust

    Doing The Right Thing for the Environment Just Got Easier With a Little Help from Information Systems

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    When it comes to the environment, most people want to do the right thing; they just need help in getting there. No one really wants their friends to perceive them to be careless polluters. Businesses do not really want their customers to believe that by buying their products they are destroying the planet we live on. Most people claim that they will pay more for a green product, they just need a little help with the follow through. Information Systems can play a critical role in helping people and business follow through on their good intentions when it comes to the environment. Some of the ways that Information Systems can help us do the right things include efficiency systems, forecasting, reporting and awareness, energy efficient home computing, and behavior modification. This article calls on professors, educators, and citizens of the world to develop and use information systems to help people do the right thing

    Green Business and Online Price Premiums: Will Consumers Pay More to Purchase from Environmentally Friendly Technology Companies?

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    This study explores the “green” business model for the digital economy. Specifically, it asks whether online consumers will pay more to purchase from a company that they perceive to be socially responsible when it comes to the environment. We conduct an experiment where consumers are presented with different facts regarding the environmental practices of a fictional online retailer of digital music, movies and MP3 players, and are then asked to indicate the maximum price they would be willing to pay for these products. Each consumer first reacts to an environmentally neutral company, followed by an environmentally friendly company and an environmentally unfriendly company presented in a random order. Results show a significant difference between the maximum prices consumers are willing to pay for products with each group, with the environmentally friendly company receiving a modest premium over the neutral group and with the environmentally unfriendly company experiencing a steep price drop for their products compared to the neutral group where many consumers indicate that they would not purchase at any price from the environmentally unfriendly company. Our findings have practical implications for the digital economy as companies look for ways to differentiate themselves from competitors

    Are Lengthy and Boilerplate Risk Factor Disclosures Inadequate? An Examination of Judicial and Regulatory Assessments of Risk Factor Language

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    Prior research finds that lengthy and boilerplate risk factor disclosures are associated with negative capital market consequences. Yet regulators and users of financial statements continue to criticize corporate risk factor disclosures as excessively long and boilerplate. We investigate two potential sources of firms’ incentives to issue lengthy, boilerplate risk factor disclosures by examining how judicial and regulatory assessments of firms’ risk factor disclosures correlate with measures of disclosure length and disclosure boilerplate. Our results suggest that lengthy and more boilerplate risk factor disclosures are less likely to be considered inadequate under judicial and regulatory review. Specifically, risk factor disclosures that are lengthier and less specific are less likely to be flagged as inadequate for safe harbor purposes under the Private Securities Litigation Reform Act. In addition, more standardized risk factor disclosures are less likely to be targeted by an SEC comment letter during the SEC’s filing review process

    New West Indian Coleoptera

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    4 p. : 1 ill. ; 24 cm.Includes 1 bibliographical reference (p. 4)

    Flies

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    10 p. ; 24 cm.Includes bibliographical references (p. 9-10)

    Scarab genus Acoma

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    13 p. : map ; 24 cm.Includes bibliographical references (p. 12)

    The Impact of Consumer Perceptions of Information Privacy and Security Risks on the Adoption of Residual RFID Technologies

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    In today’s global competitive environment, organizations face a variety of challenges. Continuous improvement in organizational efficiencies and improving the entire supply chain are necessary to stay competitive. Many organizations are adopting radio frequency identification technologies (RFID) as part of their information supply chains. These technologies provide many benefits to the organizations that use them. However, how these technologies affect the consumer and their willingness to adopt the technology is often overlooked. Many of these RFID tags remain active after the consumers purchase them. These RFID tags, placed in a product for one purpose and left in the product after the tags have served their purpose, are residual RFIDs. Residual RFID technology can have many positive and negative effects on consumers’ willingness to buy and use products containing RFID, and thus, on the business’s ability to sell products containing RFID. If consumers refuse to buy products with residual RFID tags in them, the business harm is greater than the business benefit, regardless of any gain in supply chain efficiency. In this study, we outline some of the advantages and disadvantages of Residual RFID from the consumer perspective, then follow up with an in depth survey and analysis of consumer perceptions. Using structural equation modeling (SEM) we demonstrate that consumers’ perceptions of privacy risk likelihood and privacy risk harm negatively impact their intentions to use this technology. The implications of these findings need to be considered before the pending implementation of residual RFID technologies in the supply chain on a mass scale

    Moving from Forecast to Prediction: How Honors Programs Can Use Easily Accessible Predictive Analytics to Improve Enrollment Management

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    Most enrollment management systems today use historical data to build rough forecasts of what percentage of students will likely accept an offer of enrollment based on historical acceptance rates. While this aggregate forecast method has its uses, we propose that building an enrollment model based on predicting an individual’s likelihood of matriculation can be much more beneficial to an honors director than a historical aggregate forecast. Many complex predictive analytics techniques and specialized software can build such models, but here we show that a basic approach can also be easily accessible to honors directors where a small amount of data collection and basic spreadsheet software allow them to capture most of the benefits without needing the skills of a data scientist. The first step comes in understanding the difference between a forecast and a prediction. A forecast is an estimate of a future event, generally in aggregate form. For example, today I might forecast that our ice cream store will likely sell 1,000 scoops of ice cream based on weather, time of year, day of the week, and regional events—all useful information for staffing and inventory management as well as profitability analysis. Historically, an honors administrator might use this approach to predict the total number of students matriculating to the university or to an individual program. However, with predictive analytics one can acquire even more detail that could be useful in a setting like an honors program where not just the total number of “customers” matter but which ones will create a well-rounded, diverse honors program with students from multiple backgrounds (Siegel). In the ice cream case, a predictive analytics example might predict not just how many total ice cream scoops might be sold but how likely each individual is to buy ice cream. Deeper analysis might predict the type of ice cream, time of day customers might come, and how frequently they might visit the store. Predictive analytics might also lead to prescriptive analytics, where you learn what might be done to persuade someone who was not planning to buy ice cream to do so, e.g., what it might take to change a consumer’s mind so that she will buy ice cream today or how we can we get her to buy two scoops instead of one or to bring a friend. This type of predictive and prescriptive analytics has helped many organizations improve their efficiency and effectiveness (Siegel), and we believe that honors directors can also use it. In this approach, each potential honors student would receive an individualized probability score reflecting his or her likelihood of accepting an offer of admission. This score could still be aggregated into a direct forecast of how many students would likely attend, but it would also show the likelihood that any individual student would attend. The scores could predict how many from a certain group (e.g., science majors or Hispanic students) are likely to attend. This information could help strategically determine scholarship offers as well as the staff’s time commitments to recruitment and follow-up activities
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