Using Log-Linear Models to Analyze the Use of Hedonic Information Technologies on Corporate Websites

The Internet allows combining various multimedia applications in order to create a holistic online experience. Companies integrate utilitarian and hedonic elements in their Websites in order to improve their overall effectiveness and efficiency. In this exploratory research paper we present the results of a longitudinal study, in which we analyze the use of four different hedonic information technologies (wallpapers/screensavers, e-cards, sweepstakes, online games) on corporate websites. We differentiate between sites which offer high and low involvement products. We use log-linear models to visualize our results and to show which combinations of hedonic instruments are most popular over time. Our results show that in general companies have reduced the application of hedonic instruments over time while simultaneously specific combinations of instruments have become quite popular

Breaking Free from the Limitations of Classical Test Theory: Developing and Measuring Information Systems Scales Using Item Response Theory

Information systems (IS) research frequently uses survey data to measure the interplay between technological systems and human beings. Researchers have developed sophisticated procedures to build and validate multi-item scales that measure latent constructs. The vast majority of IS studies uses classical test theory (CTT), but this approach suffers from three major theoretical shortcomings: (1) it assumes a linear relationship between the latent variable and observed scores, which rarely represents the empirical reality of behavioral constructs; (2) the true score can either not be estimated directly or only by making assumptions that are difficult to be met; and (3) parameters such as reliability, discrimination, location, or factor loadings depend on the sample being used. To address these issues, we present item response theory (IRT) as a collection of viable alternatives for measuring continuous latent variables by means of categorical indicators (i.e., measurement variables). IRT offers several advantages: (1) it assumes nonlinear relationships; (2) it allows more appropriate estimation of the true score; (3) it can estimate item parameters independently of the sample being used; (4) it allows the researcher to select items that are in accordance with a desired model; and (5) it applies and generalizes concepts such as reliability and internal consistency, and thus allows researchers to derive more information about the measurement process. We use a CTT approach as well as Rasch models (a special class of IRT models) to demonstrate how a scale for measuring hedonic aspects of websites is developed under both approaches. The results illustrate how IRT can be successfully applied in IS research and provide better scale results than CTT. We conclude by explaining the most appropriate circumstances for applying IRT, as well as the limitations of IRT

The uneven price impact of energy efficiency ratings on housing segments and implications for public policy and private markets

In the literature, there is extensive, although in some cases inconclusive, evidence on the impact of Energy Performance Certificates (EPC) on housing prices. Nonetheless, the question of whether such an impact is homogenous across residential segments remains highly unexplored. This paper addresses this latter issue utilizing multifamily listing data in metropolitan Barcelona. In doing so, first the entire sample is analyzed using a hedonic model. Second, the sample is split on the basis of a multivariate segmentation. Finally, separated hedonic models are specified again. The results suggest that in general, there is a modest impact of EPC ratings on listing prices, nonetheless it is not homogeneous across housing segments: (1) for the most modern apartments, with state-of-the-art features and active environmental comfort, energy ratings seem to play a null role in the formation of prices; (2) conversely, for the cheapest apartments, apartments boasting the most basic features, and apartments located in low-income areas, the “brown discount” is enormously significant, potentially depreciating the equity of those who have the least resources to carry out an energy retrofit. These results have implications for the assessment of the EPBD and its Spanish transposition, since a very well-intentioned environmental policy could have potentially harmful social repercussions in the absence of corrective measures.Peer ReviewedPostprint (published version

3rd International Conference on Advanced Research Methods and Analytics (CARMA 2020)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information.As these sources, methods, and applications become more interdisciplinary, the 3rd International Conference on Advanced Research Methods and Analytics (CARMA) is an excellent forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges.Doménech I De Soria, J.; Vicente Cuervo, MR. (2020). 3rd International Conference on Advanced Research Methods and Analytics (CARMA 2020). Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/149510EDITORIA

User experience in cross-cultural contexts

This dissertation discusses how interdisciplinary UX teams can consider culturally sensitive design elements during the UX design process. It contributes a state-of-the-art meta review on UX evaluation methods, two software tool artifacts for cross-functional UX teams, and empirical insights in the differing usage behaviors of a website plug-in of French, German and Italian users, website design preferences of Vietnamese and German users, as well as learnings from a field trip that focused on studying privacy and personalization in Mumbai, India. Finally, based on these empirical insights, this work introduces the concept culturally sensitive design that goes beyond traditional cross-cultural design considerations in HCI that do not compare different approaches to consider culturally sensitive product aspects in user research

Social Media Use by College Students and Teachers: An Application of UTAUT2

Social media has been increasingly used in education to facilitate innovative instruction. Mainland Chinese people could not use popular social media platforms such as FacebookTM because the government blocked them. Little research studied social media use by Mainland Chinese students and teachers in the isolated network environment. This quantitative study utilized social constructivism, connectivism, and the unified theory of acceptance and use of technology (UTAUT2) as the theoretical base. Research questions explored the influence of 6 UTAUT2 predictors on social media use intention and the influence of social media use intention on social media use behavior. The study used a convenience sample of 197 undergraduate students and 54 full-time faculty from 2 public science and technology universities in Guangzhou, Guangdong province, Mainland China. Survey data were analyzed using descriptive statistics, simple regression, multiple regression, and moderation analysis. The findings showed that performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, and habit significantly influenced social media use intention, and social media use intention significantly influenced social media use behavior. Age moderated the relationship between facilitating conditions and social media use intention, and gender moderated the relationship between habit and social media use intention. The findings might be used to promote positive social change by providing insights of social media use by Chinese students and teachers for university administrators, government, and social media platform designers. Better understanding might facilitate adoption of social media in education and therefore improve teaching and learning for Mainland Chinese students

Sustainability, Digital Transformation and Fintech: The New Challenges of the Banking Industry

In the current competitive scenario, the banking industry must contend with multiple challenges tied to regulations, legacy systems, disruptive models/technologies, new competitors, and a restive customer base, while simultaneously pursuing new strategies for sustainable growth. Banking institutions that can address these emerging challenges and opportunities to effectively balance long-term goals with short-term performance pressures could be aptly rewarded. This book comprises a selection of papers addressing some of these relevant issues concerning the current challenges and opportunities for international banking institutions. Papers in this collection focus on the digital transformation of the banking industry and its effect on sustainability, the emergence of new competitors such as FinTech companies, the role of mobile banking in the industry, the connections between sustainability and financial performance, and other general sustainability and corporate social responsibility (CSR) topics related to the banking industry. The book is a Special Issue of the MDPI journal Sustainability, which has been sponsored by the Santander Financial Institute (SANFI), a Spanish research and training institution created as a collaboration between Santander Bank and the University of Cantabria. SANFI works to identify, develop, support, and promote knowledge, study, talent, and innovation in the financial sector

Three Essays on Big Data Consumer Analytics in E-Commerce

Consumers are increasingly spending more time and money online. Business to consumer e-commerce is growing on average of 20 percent each year and has reached 1.5 trillion dollars globally in 2014. Given the scale and growth of consumer online purchase and usage data, firms\u27 ability to understand and utilize this data is becoming an essential competitive strategy. But, large-scale data analytics in e-commerce is still at its nascent stage and there is much to be learned in all aspects of e-commerce. Successful analytics on big data often require a combination of both data mining and econometrics: data mining to reduce or structure (from unstructured data such as text, photo, and video) large-scale data and econometric analyses to truly understand and assign causality to interesting patterns. In my dissertation, I study how firms can better utilize big data analytics and specific applications of machine learning techniques for improved e-commerce using theory-driven econometrical and experimental studies. I show that e-commerce managers can now formulate data-driven strategies for many aspect of business including cross-selling via recommenders on sales sites to increasing brand awareness and leads via social media content-engineered-marketing. These results are readily actionable with far-reaching economical consequences