A Framework and Guide for Understanding the Creation of Consumer Trust

This paper develops a framework and model for building trust by combining research on the creation of trust with the dimensions of trust: 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 with which to categorize and understand trust production. The framework is developed and analyzed in the light of literature on consumer 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

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?

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

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

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

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

Data competence maturity: developing data-driven decision making

Purpose - The purpose of this paper is to lay out the data competence maturity model (DCMM) and discuss how the application of the model can serve as a foundation for a measured and deliberate use of data in secondary education. Design/methodology/approach - Although the model is new, its implications, and its application are derived from key findings and best practices from the software development, data analytics and secondary education performance literature. These principles can guide educators to better manage student and operational outcomes. This work builds and applies the DCMM model to secondary education. Findings - The conceptual model reveals significant opportunities to improve data-driven decision making in schools and local education agencies (LEAs). Moving past the first and second stages of the data competency maturity model should allow educators to better incorporate data into the regular decision-making process. Practical implications - Moving up the DCMM to better integrate data into their decision-making process has the potential to produce profound improvements for schools and LEAs. Data science is about making better decisions. Understanding the path laid out in the DCMM to helping an organization move to a more mature data-driven decision-making process will help improve both student and operational outcomes. Originality/value - This paper brings a new concept, the DCMM, to the educational literature and discusses how these principles can be applied to improve decision making by integrating them into their decision-making process and trying to help the organization mature within this framework

Mortality Among Adults With Cancer Undergoing Chemotherapy or Immunotherapy and Infected With COVID-19

Importance: Large cohorts of patients with active cancers and COVID-19 infection are needed to provide evidence of the association of recent cancer treatment and cancer type with COVID-19 mortality. // Objective: To evaluate whether systemic anticancer treatments (SACTs), tumor subtypes, patient demographic characteristics (age and sex), and comorbidities are associated with COVID-19 mortality. // Design, Setting, and Participants: The UK Coronavirus Cancer Monitoring Project (UKCCMP) is a prospective cohort study conducted at 69 UK cancer hospitals among adult patients (≥18 years) with an active cancer and a clinical diagnosis of COVID-19. Patients registered from March 18 to August 1, 2020, were included in this analysis. // Exposures: SACT, tumor subtype, patient demographic characteristics (eg, age, sex, body mass index, race and ethnicity, smoking history), and comorbidities were investigated. // Main Outcomes and Measures: The primary end point was all-cause mortality within the primary hospitalization. // Results: Overall, 2515 of 2786 patients registered during the study period were included; 1464 (58%) were men; and the median (IQR) age was 72 (62-80) years. The mortality rate was 38% (966 patients). The data suggest an association between higher mortality in patients with hematological malignant neoplasms irrespective of recent SACT, particularly in those with acute leukemias or myelodysplastic syndrome (OR, 2.16; 95% CI, 1.30-3.60) and myeloma or plasmacytoma (OR, 1.53; 95% CI, 1.04-2.26). Lung cancer was also significantly associated with higher COVID-19–related mortality (OR, 1.58; 95% CI, 1.11-2.25). No association between higher mortality and receiving chemotherapy in the 4 weeks before COVID-19 diagnosis was observed after correcting for the crucial confounders of age, sex, and comorbidities. An association between lower mortality and receiving immunotherapy in the 4 weeks before COVID-19 diagnosis was observed (immunotherapy vs no cancer therapy: OR, 0.52; 95% CI, 0.31-0.86). // Conclusions and Relevance: The findings of this study of patients with active cancer suggest that recent SACT is not associated with inferior outcomes from COVID-19 infection. This has relevance for the care of patients with cancer requiring treatment, particularly in countries experiencing an increase in COVID-19 case numbers. Important differences in outcomes among patients with hematological and lung cancers were observed

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

ABSTRACT 7 Myths of Common Data Warehousing Practices: An Examination of Consumer, Business, and Societal Value

This paper begins a dialog on the ethical usage of data warehousing practices. Seven myths are explored and counter-myths are provided. Three myths focus on consumer value including data collection (privacy), customer profiling, and persuasive (targeted) marketing. Two business value myths focus on productivity and reputation. Two additional myths focus on societal values that include discussion on the economy and environmental issues and finally national security issue. This paper is presented in a discussion-based format designed to further discussion of information ethics in the context of data warehousing practices