DecisionSciRN: Consumer Decision-Making (Sub-Topic)最新文献

{"title":"Catching the bus in Barcelona","authors":"Miquel-Àngel Garcia-López, Rosella Nicolini, Josep-Lluís Roig","doi":"10.2139/ssrn.3892321","DOIUrl":"https://doi.org/10.2139/ssrn.3892321","url":null,"abstract":"In our study, we seek to identify the basic factors in citizens’ decisions to regularly use public transportation, especially the decision to ride a bus in Barcelona, Spain. Our survey reveals that, contrary to other recent results, income is not a statistically significant factor in those decisions, whereas level of education and occupation are. Moreover, the effects of congestion emerge as a key factor in the decision to ride the bus regularly. Considering congestion’s impact on the likelihood of regularly using the bus transport system, regular commuters in the urban area and riders with a high level of education are more responsive to reductions in congestion. Those findings contribute to an important discussion about implementing public policies that support citizens’ use of public transportation systems in urban areas.","PeriodicalId":134754,"journal":{"name":"DecisionSciRN: Consumer Decision-Making (Sub-Topic)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134228456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Grocery Shopping under Cognitive Constraints and Restrictive Budget: An Algorithmic Approach","authors":"Calogero Brancatelli","doi":"10.2139/ssrn.3896880","DOIUrl":"https://doi.org/10.2139/ssrn.3896880","url":null,"abstract":"This paper presents an algorithmic approach to optimize the grocery shopping behaviour of consumers under specific circumstances: a highly restrictive budget and cognitive constraints. I develop a formal optimization model that accounts for the trade-off between dietary objectives and product preferences during the shopping process. I show how a heuristic algorithm and its implementation in an interactive web platform can be used to find local optima to a consumer’s choice problem, by alleviating the burden from limited processing capacity, especially overchoice. The approach presented in this paper has implications for retailers and policymakers as it points to concrete actions to enhance dietary imbalances and potential underconsumption experienced by low-income and cognitively constrained individuals. I discuss a specific use case of the algorithm for people with intellectual and developmental disabilities.","PeriodicalId":134754,"journal":{"name":"DecisionSciRN: Consumer Decision-Making (Sub-Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123551163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of Advertising, Price, and Quality of Service on Purchase Decisions on the Shopee Application (Studies at State University of Jakarta Students)","authors":"Chika Octavianti Yogasuria, Osly Usman","doi":"10.2139/ssrn.3768330","DOIUrl":"https://doi.org/10.2139/ssrn.3768330","url":null,"abstract":"This study discusses the effect of advertising, price, and service quality on purchasing decisions on the Shopee application at the State University of Jakarta using quantitative research methods. The purpose of this study was to find out whether there was a positive influence between advertising, price, and service quality on purchasing decisions. The number of samples used in this study was 100 respondents, namely students of the State University of Jakarta. The data collection technique used was to distribute Likert scale questionnaires with a score range of 1-5. Researchers tested using SPSS 23, namely test instruments, classical assumptions, and hypothesis testing. From these calculations, the results of multiple regression analysis are obtained Ŷ = 8.671 + 0.287X1 + 0.585X2 + 0.057X3 then the Fcount is obtained of 35.805> Ftable value of 2.70. Furthermore, the results of the calculation of the ttest of advertising have tcount value of 3.276 > ttable value of 0.677, price have tcount value of 5.710 > ttable value of 0.677, and service quality tcount value of 0.785 > ttable of 0.677. Therefore, it can be said that the advertising, price, and service quality variables influence the purchasing decision variable.","PeriodicalId":134754,"journal":{"name":"DecisionSciRN: Consumer Decision-Making (Sub-Topic)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114879253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Consumer Spending Responses to the COVID-19 Pandemic: An Assessment of Great Britain","authors":"Dimitris K. Chronopoulos, Marcel Lukas, John O. S. Wilson","doi":"10.2139/ssrn.3586723","DOIUrl":"https://doi.org/10.2139/ssrn.3586723","url":null,"abstract":"Since the first death in China in early January 2020, the coronavirus (COVID-19) has spread across the globe and dominated the news headlines leading to fundamental changes in the health, social and political landscape, and an unprecedented negative impact on the current and future prospects of households, businesses and the macro-economy. In this paper, we examine consumer spending responses to the onset and spread of COVID-19, and subsequent government imposed lockdown in Great Britain, GB (England, Scotland, Wales). Our sample period spans January 1st 2020 to 18th June 2020. This allows us to observe consumer spending behavior from the initial incubation phase of the crisis. We partition the sample period into incubation (1st-17th January), outbreak (January 18th-February 21st), fever (February 22nd-March 22nd), lockdown (March 23rd–May 10th 2020) and stay alert (May 11th- June 18th) phases. Using a high frequency transaction level proprietary dataset comprising 101,059 consumers and 23 million transactions made available by a financial technology company, we find that discretionary spending declines during the fever period as the government imposed lockdown becomes imminent, and continues to decline throughout the lockdown period. Shortly after the May 10th ‘stay alert’ announcement by Prime Minister Johnson, a short-term decline in spending across all nations occurs. However, a week later, spending is at the same level as that observed prior to the announcement. There is a strong increase in groceries spending consistent with panic buying and stockpiling behaviour in the two weeks following the World Health Organisation (WHO) announcement describing COVID-19 as a pandemic. Variations in the level and composition of consumer spending across nations and regions (particularly during the early stages of the outbreak period), and by age, gender and income level are also observed. Our results are of particular relevance to government agencies tasked with the design, execution and monitoring economic impacts arising from the spread of the virus and the public health measures imposed to mitigate the health costs of the crisis.","PeriodicalId":134754,"journal":{"name":"DecisionSciRN: Consumer Decision-Making (Sub-Topic)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129578144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Bright Side of Bad Choices: Evidence from Restaurant Exploration","authors":"Manuel E. Sosa, Clara Carrera, Víctor Martínez-de-Albéniz","doi":"10.2139/ssrn.3628270","DOIUrl":"https://doi.org/10.2139/ssrn.3628270","url":null,"abstract":"The value derived from hedonic goods is affected by reference effects at the time of consumption, \u0000usually in the form of quality standards. Consumption typically involves two steps: first, the consumer chooses a given good, among a pool of available choices; then, the consumer experiences the good and derives a satisfaction from it. We investigate the role of references in these two phases, taking into account potential selection bias in the satisfaction outcome. Using novel longitudinal data from restaurant reviews, we find evidence of quality loss aversion in the choice decision, in accordance with prospect theory. That is, people tend to disproportionally avoid choosing restaurants that are below their quality reference. At the same time, choosing a lower-quality good does not penalize satisfaction in the chosen restaurant as much as one would expect, which is consistent with the fact that consumers adjust their expectations downward when they choose a lower-quality option. This suggests that expectation adjustment protects consumers when they make apparent “bad\" choices. Given these results, we show that it may be optimal to visit restaurants in a zigzag that alternates between high- and low-quality choices.","PeriodicalId":134754,"journal":{"name":"DecisionSciRN: Consumer Decision-Making (Sub-Topic)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123596819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linear Programming Based Online Policies for Real-Time Assortment of Reusable Resources","authors":"Yiding Feng, Rad Niazadeh, A. Saberi","doi":"10.2139/ssrn.3421227","DOIUrl":"https://doi.org/10.2139/ssrn.3421227","url":null,"abstract":"Motivated by applications of rental services in e-commerce, we consider real-time assortment of reusable products. In our model, arriving consumers with heterogeneous types choose rental products from the offered assortment, pay the rental fees, and return the product to the platform after a rental time. Consumers' types specify their choice models, rental fees and rental time distributions for various products. Our goal is to design competitive online policies against an appropriate benchmark for both the prior-free setting, in which types are arbitrary (or adversarial), and the Bayesian setting, in which types are drawn independently from known distributions. \u0000 \u0000Our contribution is threefold. We first introduce offline linear programming benchmarks in both settings, that use time-varying inventory constraints to capture feasibility of a policy under rentals, and are required to satisfy these constraints only in expectation. Second, in the prior-free setting, we develop a randomized primal-dual framework based on our introduced LP to settle that inventory balancing policies of Golrezaei et al. (2014) obtain same (asymptotically optimal) competitive ratios as with non-reusable resources when product rental times are fixed over time. As a corollary, we obtain the optimal competitive ratio of (1 − 1/e) when inventories are large. We also show this family of policies are constant competitive under i.i.d. (over time) stochastic rental times. \u0000 \u0000Third, we change gear to the Bayesian setting by introducing simulation-based policies that use the expected LP solution as guidance. By using primal-dual analysis, we obtain a (1/2)-competitive simple and static simulation-based policy against the expected LP for general type-varying rental time distributions and rental fees. We further show optimal (1 − 1/ (cmin+3)0.5)-competitive adaptive policies against the same benchmark when rental times are infinite, where cmin is the smallest product inventory. Our analysis extends tools in the literature on prophet inequalities to design discarding threshold rules that maintain feasibility of a simulation-based policy in the Bayesian real-time assortment. We further justify the revenue performance of our proposed policies using numerical simulations.","PeriodicalId":134754,"journal":{"name":"DecisionSciRN: Consumer Decision-Making (Sub-Topic)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114655394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fundamental Basket Size Patterns and Their Relation to Retailer Performance","authors":"James Martin, M. Nenycz-Thiel, J. Dawes, Arry Tanusondjaja, Justin Cohen, B. McColl","doi":"10.2139/ssrn.3396827","DOIUrl":"https://doi.org/10.2139/ssrn.3396827","url":null,"abstract":"This study uses a sample of approximately 60,000 US households to document fundamental shopping basket size patterns across a range of retail types, and examines them in relation to retailer performance metrics (unit sales and dollar revenue). Specifically, this research addresses two main questions: 1) how do shopping basket metrics (mean and median number of items, the distribution of one, two, three … n items) differ by retail type, and 2) how does the Pareto ratio (sales generated by the heaviest 20%, and lightest 80% of buyers) apply to shopping baskets. The results show that basket size patterns in retailers are predictable. Shoppers purchase more items on average in retailers that offer a greater variety of items, and the distribution of basket sizes follows the Poisson lognormal model. The results also show that the largest 20% of shopping baskets on average generate 50% of unit sales, and 40% of dollar revenue. These results set additional benchmarks of the patterns that can be expected when looking at data on a basket-level. This research offers implications to practitioners by showing the importance of small and large baskets for retailer revenue and growth, which can guide more informed decision making to better manage their stores and brands.","PeriodicalId":134754,"journal":{"name":"DecisionSciRN: Consumer Decision-Making (Sub-Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129261275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inferring Consideration Sets from Sales Transaction Data","authors":"Srikanth Jagabathula, Dmitry Mitrofanov, Gustavo J. Vulcano","doi":"10.2139/ssrn.3410019","DOIUrl":"https://doi.org/10.2139/ssrn.3410019","url":null,"abstract":"Understanding consumer preferences is critical when optimizing prices and planning in retail operations, and when matching supply and demand in online platforms. In pursuing such objective, the identification of the consideration set of the consumers (i.e., the set of products really accounted for by consumers prior to making a choice) is indeed a fundamental input. In this paper we propose a methodology to identify consideration sets from sales transactions data in a data driven way. We assume that customers are boundedly rational and make their purchases in a two-stage process. First, they sample their consideration set and then purchase the most preferred item therein. Our contribution to the literature is two-fold. Theoretically, we address the problem of identifiability of consider-then-choose models from data. Since calibrating this class of choice models is a hard problem, we propose a framework to effectively estimate them and infer consideration sets. The methodology to model the consideration set formation is founded on machine learning techniques that can account for nonlinear-in-parameter utilities in a tractable way. Then we apply the proposed methodology to retail store data and data obtained from a car-sharing platform, and observe that accounting for consideration sets can boost the predictive performance in comparison with classical choice-based demand benchmarks. Our findings suggest that consider-then-choose models tend to be rather robust to the degree of ambiguity in the consideration set definition, and their relative importance in prediction tasks increases with this noise. Moreover, we show that the consider-then-choose type of choice models can provide important managerial insights about the consideration set formation.","PeriodicalId":134754,"journal":{"name":"DecisionSciRN: Consumer Decision-Making (Sub-Topic)","volume":"29 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123216514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resistance To Medical Artificial Intelligence","authors":"Chiara Longoni, Andrea Bonezzi, Carey K. Morewedge","doi":"10.1093/JCR/UCZ013","DOIUrl":"https://doi.org/10.1093/JCR/UCZ013","url":null,"abstract":"\u0000 Artificial intelligence (AI) is revolutionizing healthcare, but little is known about consumer receptivity to AI in medicine. Consumers are reluctant to utilize healthcare provided by AI in real and hypothetical choices, separate and joint evaluations. Consumers are less likely to utilize healthcare (study 1), exhibit lower reservation prices for healthcare (study 2), are less sensitive to differences in provider performance (studies 3A–3C), and derive negative utility if a provider is automated rather than human (study 4). Uniqueness neglect, a concern that AI providers are less able than human providers to account for consumers’ unique characteristics and circumstances, drives consumer resistance to medical AI. Indeed, resistance to medical AI is stronger for consumers who perceive themselves to be more unique (study 5). Uniqueness neglect mediates resistance to medical AI (study 6), and is eliminated when AI provides care (a) that is framed as personalized (study 7), (b) to consumers other than the self (study 8), or (c) that only supports, rather than replaces, a decision made by a human healthcare provider (study 9). These findings make contributions to the psychology of automation and medical decision making, and suggest interventions to increase consumer acceptance of AI in medicine.","PeriodicalId":134754,"journal":{"name":"DecisionSciRN: Consumer Decision-Making (Sub-Topic)","volume":"6 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128012637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Về ý định mua quần áo trẻ em tại tỉnh Bình Dương (About Purchasing Intention of Kids Garment in Binh Duong Province)","authors":"Giao Hà Nam Khánh","doi":"10.2139/ssrn.3685126","DOIUrl":"https://doi.org/10.2139/ssrn.3685126","url":null,"abstract":"<b>Vietnamese abstract:</b> Nghiên cứu kiểm định mức độ tác động của các yếu tố ảnh hưởng đến ý định mua quần áo trẻ em tại Bình Dương, bằng việc khảo sát 155 khách hàng. Phương pháp phân tích Cronbach’s Alpha, phân tích EFA cùng với phân tích hồi quy bội được sử dụng với phương tiện SPSS. Kết quả cho thấy mức độ ảnh hưởng của các yếu tố đến ý định mua quần áo trẻ em tại Bình Dương - theo tầm quan trọng giảm dần: (1) Kiến thức về hàng hóa cao cấp, (2) giá trị thiết thực, (3) chủ nghĩa vật chất, (4) hình ảnh thương hiệu, (5) tính độc đáo, (6) chất lượng sản phẩm và (7) không gian cửa hàng. Nghiên cứu đề ra một số hàm ý quản trị cho các nhà quản lý nhằm tăng ý định mua quần áo trẻ em tại Bình Dương.<br><br><b>English abstract:</b> The research investigates how the factors affect purchasing intention of kids garment in Binh Duong province, by interviewing 155 customers. The method of Cronbach’s Alpha analysis, EFA analysis and multiple regression analysis were used with the SPSS program. The result shows that the affects of the factors on affect purchasing intention of kids garment in Binh Duong province decreasingly: (1) Luxury goods knowledge, (2) Practical, (3) Materialism, (4) Brand image, (5) Uniqueness, (6) Product quality, (7) Store atmosphere. The research also suggests some solutions to the stores management to enhance purchasing intention of kids garment in Binh Duong province.","PeriodicalId":134754,"journal":{"name":"DecisionSciRN: Consumer Decision-Making (Sub-Topic)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127981274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}