Predicting Online Invitation Responses with a Competing Risk Model Using Privacy-Friendly Social Event Data

Predicting people's responses to invitations is an important issue for social event management, as the decision-making process behind member responses to invitations is complicated. The purpose of this paper is to suggest a privacy-friendly method to predict whether and when people will respond to open invitations. We apply the competing risk model to predict member responses. The predictive model uses past social event participation data to infer a network structure among people who accept or reject invitations. The inferred networks collectively show the extent to which people are likely to accept or reject invitations. Validated using real datasets including 31,230 people and 8,885 events, the proposed method not only presents the variables that predict attendance (such as past attendance and social network), but also those that predict faster responses. This approach is privacy friendly, as it requires no personal information regarding people and social events (such as name, age and gender or event content). This work contributes to the predictive modeling literature as the first study of a competing risk model developed for replies to a social invitation. Our findings will help event organizers predict how many people will attend events, allowing them to organize effectively

Behaviors Contributing to Native American Business Success

Native Americans start fewer businesses than do other U.S. populations, and the receipts and employment of those businesses are 70% lower than the U.S. average. However, little knowledge exists concerning Native American (NA) business success. The purpose of this quantitative study was to examine the likelihood that attitudes toward entrepreneurship, subjective norms, and perceived behavioral control predict business success amongst NA business owners. Understanding the factors that contribute to NA business success is imperative to developing best practices for business owners and business support agencies. The theory of planned behavior served as the theoretical framework for this study. Of the 550 invited NA business owners registered within a single tribe in the South Central United States, 79 participated in this study. A binary logistic regression analysis produced conflicting results: significant goodness-of-fit yet insignificant individual predictors. Information obtained from this study could assist NA and other underdeveloped business populations with understanding factors influencing entrepreneurial endeavors; however, readers must interpret findings with caution because of conflicting logistic regression results. NA business formation and success could enhance economic prosperity and decrease unemployment in NA communities

Customer Acceptance is the Key to Success of Electronic Bill Presentment and Payment

This thesis focuses on issue of broaden Customer Acceptance in Electronic Bill Presentation andPayment (EBPP). Following the overview of EBPP’s concept, benefit, snapshot of the overall marketplace, the thesis studies the current existing models with it’s entity, process, and relationship. The important part of the thesis is to explore the main elements to one of the key barriers of EBPP, Customer acceptance according to TAM (Technology Acceptance Model) and Diffusion of Innovation Model, and provides the several key solutions to broaden Customer acceptance of EBPP. The thesis concludes with pointing out the limitation of this thesis and the suggestion of possible future research and looking forward to the future market of EBPP. Thesis contains five chapters. The CHAPTER I. INTRODUCTION defines the EBPP is the delivery of bills from Billers to Customers mainly through Internet; reviews the benefits to the both Biller and Customer; realizes the EBPP’s potential market growth with current low’adoption rate tepid the EBPP deployment. The CHAPTER II. ENTITY, PROCESS, AND RELATIONSHIP OF EBPP MODELS studies the six entities of EBPP, included Biller, Biller Service Provider, Biller Payment Provider, Customer, Customer Service Provider, Customer Payment Provider, and process of EBPP with Service Initiation, Bill Presentment, and Payment and Remittance. The complex process with a range of models, which include direct, consolidator, and syndicator is discussed. CHAPTER III. EXPLORE THE ELEMENTS TO AFFECT CUSTOMER ACCEPTANCE TO DEPLOY EBPP points out that low Customer acceptance impedes EBPP growth, studies the EBPP literature and user acceptance model in MIS, and explores the four factors (usefulness, ease of use, observability, and risk) and related elements affect the Customer acceptance, which are Customer low awareness, lack of a compelling reason, lack of incentive, trust and risk, uncertainty about security and privacy, inaccuracy and unreliable, difficult to use, bank slow react, legal issue, standard, and poor Customer service. CHAPTER IV. SOLUTION ANDSTRATEGY TO BROADEN CUSTOMER ACCEPTANCE OF EBPP suggests six solutions to broaden the Customer acceptance, which are chose right model, build solid EBPP system, chose a right vendor, and provide good Customer service, make aggressive marketing approach, and be proactive bank and Biller. CHAPTER V. CONCLUSION provides the overall of future market of EBPP

The Positive Functioning of Post-9/11 Student Service Members/Veterans as a Predictor of Academic Performance

There is a dearth of empirical evidence on post-9/11 student veterans and what makes them successful in their transition from military service to postsecondary education. This study primarily examined post-9/11 student service members\u27/veterans\u27 (SSM/Vs) positive functioning (i.e., the building blocks of well-being) as a predictor of their academic performance. Positive psychology through Seligman\u27s (2010) PERMA model and Ecological Systems Theory (Bronfenbrenner, 1993) were used as the guiding theoretical frameworks. An SSM/V sample of convenience (N = 199) was derived from seven colleges and universities in three U.S. states. The following hypotheses were tested mostly using logistic regression: SSM/Vs\u27 positive functioning will be positively related to their academic performance (i.e., self-reported GPA, perceptions of being on time towards program completion, and beliefs of meeting academic goals); SSM/Vs\u27 perceived positive university environment (i.e., social climate) and sense of belonging (i.e., psychological sense of community) will likely be positively related to their academic performance. Additionally, confirmatory factor analysis (CFA) was conducted to assess the factor structure of a well-being measure, Positive Functioning at Work Scale (PF-W, Donaldson 2019; Donaldson & Donaldson, 2021). The findings from the study indicate that positive functioning is a predictor of SSM/Vs\u27 academic performance and explain up to 18% of the variance. The findings also confirm the original factor structure of the PF-W scale. CFA model suggested a good fit for the data: [chi]2 =38.064, p \u3c 0.009, [chi]2 /df = 1.903 CFI=.969, TLI = .957, SRMR =0.045, RMSEA = 0.067. SSM/Vs\u27 positive perceptions of campus environment and sense of belonging only partially predicted academic performance. The results of this study serve to inform theory, research, and practice for institutions of higher learning specifically on the value of SSM/V well-being on academic performance. Additionally, the study highlights the importance of assessing and promoting well-being in SSM/Vs to facilitate a successful transition in and out of higher education. Future research and application of college/university-wide positive psychology interventions are recommended for further exploration

Artificial intelligence applications in marketing: the chatbot of the Department of Economics and Management "Marco Fanno”

openL'intelligenza artificiale (AI) offre numerose applicazioni nel marketing, ma allo stesso tempo ci sono diverse limitazioni da considerare nella sua adozione. Dopo la prima parte di analisi generale delle applicazioni e degli aspetti negativi dell'AI e dei chatbot, la tesi si concentra sul caso dell'implementazione di un chatbot da parte del Dipartimento di Economia e Management “Marco Fanno” dell'Università di Padova. La domanda di ricerca è volta a capire se il chatbot implementato dal Dipartimento sia stato efficace nell'alleggerire e supportare il lavoro dell'ufficio amministrativo e nel rispondere alle domande degli studenti. A tal fine, il documento analizza se il numero di email è diminuito dopo l'introduzione del chatbot. Inoltre è stato svolto un questionario per valutare l'esperienza che gli studenti del Dipartimento hanno avuto con il chatbot di ateneo. Il sondaggio ha anche chiesto agli studenti quali servizi vorrebbero che il chatbot aggiungesse a quelli attuali. Inoltre, è stata condotta un'analisi economica su benefici e costi per valutare se il chatbot genererà un risultato economico positivo. Questo studio consente di valutare l'impatto che un chatbot potrebbe avere nel campo dell'istruzione. In particolare, può fornire informazioni alle università sul fatto che un chatbot possa migliorare il coinvolgimento con gli studenti, liberare il personale da compiti ripetitivi e generare benefici economici netti nel lungo periodo. Il questionario stesso è stato condotto attraverso un sondaggio web su Google Forms e un sondaggio attraverso un chatbot. In questo modo ho anche analizzato quale dei due metodi sia il più efficace per condurre un'indagine. Alcune prove rivelano come i sondaggi condotti attraverso un chatbot possano portare a risposte più accurate da parte degli intervistati. Confrontando i risultati ottenuti della due modalità di sondaggio ho potuto verificare queste evidenze con un nuovo campione di partecipanti, gli studenti di Economia. I risultati della tesi non hanno mostrato prove chiare del fatto che il chatbot consentisse di ridurre il numero di e-mail. Ma si suggerisce un'indagine su un periodo più lungo. Successivamente i risultati hanno evidenziato un buon apprezzamento degli studenti per il chatbot e hanno suggerito l'introduzione di notifiche push che ricordano delle scadenze universitarie come le tasse. La stima dell'analisi costi-benefici prevedeva un risultato netto positivo su tre anni con un ROI del 29%. Inoltre, il sondaggio chatbot ha parzialmente confermato la tendenza ad ottenere risposte più accurate rispetto ad un classico sondaggio web.Artificial intelligence (AI) offers numerous applications in marketing, but at the same time, there are several limitations to consider in its adoption. After the first part about a general analysis of the applications and negative aspects of AI and chatbots, the thesis focuses on the case of the implementation of a chatbot by the Department of Economics and Management “Marco Fanno” of the University of Padua. The research question turns towards understanding whether the chatbot implemented by the Department was effective in easing and supporting the work of the administrative office and answering students questions. For this purpose, the paper analyses if the number of emails is decreased after the chatbot introduction. In addition, a questionnaire was carried out to evaluate the experience that the students of the Department have had with the university chatbot. The survey also asked students what services they would like the chatbot to add to their current ones. Moreover, an economic analysis on benefits and costs was conducted to estimate whether the chatbot will generate a positive outcome. This study allows evaluating the impact a chatbot could have in the education field. In particular, it can provide insight to universities on whether a chatbot could enhance the engagement with students, offload staff from repetitive tasks and generate net economic benefits in the long period. The questionnaire itself was conducted through a web survey on Google Forms and a chatbot survey. In this way, it could also be verified which of the two methods is the most effective to conduct a survey. Some evidence finds how chatbot surveys can lead to less satisfactory answers by respondents. Comparing the two survey results, I can verify these past findings with a different sample of participants, the students of Economics. The results did not show clear evidence of whether the chatbot allowed reducing the number of emails. But an investigation over a longer period is suggested. Then, findings highlighted a good appreciation of students for the chatbot and suggested the introduction of push notifications that remember university deadlines such as taxes. The estimation of the benefits-cost analysis forecasted a net positive outcome over three years with an ROI of 29%. Also, the chatbot survey partially confirmed the encouraging finding in reducing satisficing by respondents.

THE IMPACT OF SOCIAL MEDIA POLICY AND USE ON VALUE CREATION: A SURVEY RESEARCH

Organizations are increasingly employing social media in business and this phenomenon has also attracted attention from researchers. Social media is used in value co-creation to allow consumers to take an active role and co-create value for companies. However, the way in which social media can enhance an enterprise’s value co-creation capability has not been studied to the same extent. Based on the technology affordance theory and value co-creation theory, this study examines how social media can help to leverage value co-creation. We present an exploratory case study based on the qualitative date collected in an online travel service company. We find that the case company applies social media due to its dialogue, accessibility, monitorability, and transparency affordances, which aid cooperation and co-create value with customers. The findings of the investigation provide empirical evidence illustrating the social media affordances of value co-creation

Learning analytics for the global south

Learning Analytics for the Global South is a compilation of papers commissioned for the Digital Learning for Development (DL4D) project. DL4D is part of the Information Networks in Asia and Sub-Saharan Africa (INASSA) program funded jointly by the International Development Research Centre (IDRC) of Canada and the Department for International Development (DFID) of the United Kingdom, and administered by the Foundation for Information Technology Education and Development (FIT-ED) of the Philippines. DL4D aims to examine how digital learning could be used to address issues of equity, quality, and efficiency at all educational levels in developing countries. Over the past two years, DL4D has brought together leading international and regional scholars and practitioners to critically assess the potentials, prospects, challenges, and future directions for the Global South in key areas of interest around digital learning. It commissioned discussion papers for each of these areas from leading experts in the field: Diana Laurillard of the University College London Knowledge Lab, for learning at scale; Chris Dede of Harvard University, for digital game-based learning; Charalambos Vrasidas of the Centre for the Advancement of Research and Development in Educational Technology, for cost-effective digital learning innovations; and for learning analytics, the subject of this compilation, Dragan Gašević of the University of Edinburgh Moray House School of Education and School of Informatics. Each discussion paper is complemented by responses from a developing country-perspective by regional experts in Asia, Latin America, Africa, and the Middle East. Learning Analytics for the Global South considers how the collection, analysis, and use of data about learners and their contexts have the potential to broaden access to quality education and improve the efficiency of educational processes and systems in developing countries around the world. In his discussion paper, Prof. Gašević articulates these potentials and suggests how learning analytics could support critical digital learning and education imperatives such as quality learning at scale and the acquisition of 21st century skills. Experts from Africa (Paul Prinsloo of the University of South Africa), Mainland China (Bodong Chen of the University of Minnesota, USA and Yizhou Fan of Peking University, People’s Republic of China), Southeast Asia (Ma. Mercedes T. Rodrigo of the Ateneo de Manila University, Philippines), and Latin America (Cristóbal Cobo and Cecilia Aguerrebere, both of the Ceibal Foundation, Uruguay) situate Prof. Gašević’s proposals in their respective regional contexts, framing their responses around six key questions: 1. What are the main trends and challenges in education in your region? 2. How can learning analytics address these challenges? 3. What models of learning analytics adoption would be most effective in your region? 4. What are the barriers in adoption of learning analytics in your region and how could these be mitigated? 5. How do you envision ethical use and privacy protection in connection with learning analytics being addressed in your region? 6. How can the operationalization of learning analytics be futureproofed in your region? We hope that this compilation will serve as a springboard for deeper conversations about the adoption and sustained use of learning analytics in developing countries – its potential benefits and risks for learners, educators, and educations systems, as well as the ways to move forward that are rigorous, context-appropriate, ethical, and accountable.This work was created with financial support from the UK Government’s Department for International Development and the International Development Research Centre, Canada. The views expressed in this work are those of the authors and do not necessarily represent those of the UK Government’s Department for International Development; the International Development Research Centre, Canada or its Board of Governors; the Foundation for Information Technology Education and Development; or the editors

Consumer attitudes and intentions toward personalization of fair trade apparel

The purpose of this study was to examine the attitudes and purchase intentions of customers of Marketplace: Handwork of India (Marketplace) toward personalized apparel. The theory of uniqueness, theory of perceived risk, involvement, and body size were used as theoretical frameworks. These frameworks were integrated into the part of the theory of reasoned action being tested in the study. The proposed model was empirically tested through an online survey. Structural equation modeling was used to examine the fit of the proposed model;Various scales were used to measure all the variables included in the research. Body size was measured using the body mass index formula. The online survey was e-mailed to a random sample of 2,500 Marketplace customers. A total of 246 usable responses was received making the response rate 12.32%. A non-response bias test was conducted to confirm the generalizability of results;The multi-item scales used to measure each construct were tested for reliability, based on Cronbach alphas, and all the scales were found to be reliable. The two measures of perceived risk, financial and social perceived risks, were tested to ensure they were distinct constructs. Structural modeling analysis included analysis of the measurement model and analysis of the hypothesized model. Based on the results of the hypothesized model, an alternate model was proposed and tested;Marketplace customers were highly educated customers with an average age of 52 years, had a high level of familiarity with the Internet and often used the Internet to gather information and make purchases. The respondents were satisfied with Marketplace purchases, and willing to pay more and wait longer for a personalized product as compared to a regular Marketplace product;Analysis of the hypothesized model showed that consumers with greater need for self-uniqueness and higher BMI had a positive attitude toward personalized apparel. Consumes with a positive attitude toward personalized apparel had an intention to purchase personalized fair trade apparel. Greater need for self-uniqueness was associated with lowered perceived financial and social risks among fair trade consumers and increased consumer apparel involvement. The results of this study provide fair trade organizations with direction toward implementing personalization of apparel

Factors affecting the adoption of online auctions by internet users in Hong Kong

This is an exploratory empirical study with the aim to identify the factors that affect the adoption of online auctions by Internet users in Hong Kong. The frameworks used were the TAM (Technology Acceptance Model), TCE (Transaction Cost Economics) and SERVQUAL (Service Quality). It was found that the dimensions that affected the customer’s perceived value of the online auction are benefits, costs, risks and service quality. Data was collected from four pilot focus groups, one online survey and a final focus group. The subjects in the focus groups were 21 undergraduates, whereas the subjects in the online survey were 152 internet users. The results of the pilot focus groups guided the design of the online survey. The results of the survey was analysed using the Kruskal-Wallis test. The final focus group was used to seek explanations to some issues arose from the online survey. It was found that the factors in the benefit dimension were liquidity, enjoyment, and price transparency. The factors in the cost dimension were time, effort, service charge and reputation of the user. The factor in the risk dimension was financial risk. The factors in the service quality dimension were efficiency and system availability. The final focus group revealed that the auctioneer’s role in policing the auction web site was important. For differences among the subjects, it was also found that the adult users consider their reputation in auction website, young adults are worried about financial risks, and female users are more concerned about financial risks than male users. The implications of these differences are discussed. The main academic contribution was the development of a questionnaire and a model which can be used in further research about other forms of auction

An Analysis of the Factors Affecting Attitudes toward Drone Delivery and the Moderating Effect of COVID-19

This research explored the factors affecting attitudes toward drone delivery and the moderating effect of COVID-19. Government effort to address the COVID-19 pandemic has led to social distancing and shelter-in-place guidelines. Many states have imposed additional regulations, restricting retailers from offering in-store shopping and restaurants from offering indoor dining. As a result, the use of delivery services has increased. In a further effort to reduce virus spread, some delivery services now offer a contact-free option. The contact-free option permits orders to be left at a designated location, eliminating the physical-human interaction upon delivery. The contact-free nature and potential speed of drone delivery make using the technology a viable option amidst today’s social distancing guidelines. The findings of this study support drone delivery as a feasible delivery option. The perceived attributes, COVID-19 variable, performance risk, and drone familiarity were found to be predictors of attitude toward drone delivery. All the predictors have a positive relationship with attitude except perceived risk. Attitude, the perceived attributes, the presence of COVID-19, and gender are the best predictors of intention to use drone delivery. Perceived risks are not significant predictors of intention to use drone delivery. Compared to a pre-COVID-19 study conducted in 2018, the perceived attributes account for 26% more and the perceived risks account for 81% less of the variance in predicting attitude. Similarly, compared to the pre-COVID-19 study, the perceived attributes account for 58% more and the perceived risks account for 74% less of the variance in predicting intention. This indicates that the perceived attributes of drone delivery weigh more and the perceived risks weigh less on attitude and intention than the earlier study. To draw these conclusions, a model based on the Technology Acceptance Model and Diffusion of Innovations theory was adopted and modified from a previous study. Then, a survey was administered on August 26th, 2020, to Americans aged 18 and older via Amazon’s Mechanical Turk platform. A round of interviews was also administered. Following collection of the data, exploratory and confirmatory factor analyses were conducted. Model fit was confirmed through confirmatory factor analysis. Three hierarchical multiple regressions were carried out. The first identified the significant predictors of attitude as stated above. The second regression revealed that COVID-19 had a direct effect in lieu of a moderator effect on attitudes toward drone delivery. The third and final regression identified the significant predictors of intention as previously stated. Contributions of this study include identifying a direct effect of COVID-19 on attitudes towards drone delivery, discovering the best predictors of attitudes and intention to use drone delivery and a model with replicable results that may be used to measure attitude and intention in the future