Comparison Shopping Research Articles

Shopping around: Empirical analysis of borrower comparison shopping and mortgage prices

AbstractUsing a novel approach to identifying mortgage price shopping on the universe of mortgages from 2018 to 2020, we find that relatively few borrowers shop, but those who do end saving roughly 11 basis points on average. We also find significant differences by race in the incidence and benefits of shopping. When borrowers do shop, a vast majority engage in simultaneous search. Shoppers show a swifter movement away from commercial banks toward fintechs. Shoppers tend to be more creditworthy, wealthier, live in more urban areas, and apply more often with nondepository lenders. Minority shoppers show less affinity to minority‐owned lenders than corresponding nonshoppers. We also provide evidence that shopping is likely significantly overreported in available survey data.

Optimizing Data Extraction using Preprocessing for Enhanced Efficiency

With the exponential growth of the internet, an abundance of information has become readily available. Extracting valuable data from the web is crucial for applications such as meta-querying and comparison shopping. However, the heterogeneous nature of web information poses a significant challenge to the extraction process. The web can be classified into the surface or visible web and the deep or invisible web. While conventional search engines can index the surface web, they fall short when it comes to the deep web. To access the deep web, users must submit queries to web databases, and the results are encapsulated in dynamically generated web pages containing data records. Traditional search engines struggle to index these dynamic pages, necessitating a specialized program for efficient information extraction from the deep web. Web search engines generate result pages based on user queries, making it crucial to automatically extract data from these pages for various applications. In this context, we propose an innovative data extraction method called Effective Data Extraction using Preprocessing (EDEP). The EDEP approach begins by parsing the input HTML page, constructing a tag tree, and subsequently eliminating irrelevant tags from the tree. Notably, our system efficiently handles scenarios where auxiliary information, such as recommendations or comments, is intermixed between query result records (QRRs), causing them to be non-contiguous. EDEP also effectively manages result pages containing single QRRs. Through experimental results, it is evident that EDEP outperforms existing data extraction methods, showcasing its efficacy in handling the complexities associated with web data extraction.

Optimizing Data Extraction using Preprocessing for Enhanced Efficiency

With the exponential growth of the internet, an abundance of information has become readily available. Extracting valuable data from the web is crucial for applications such as meta-querying and comparison shopping. However, the heterogeneous nature of web information poses a significant challenge to the extraction process. The web can be classified into the surface or visible web and the deep or invisible web. While conventional search engines can index the surface web, they fall short when it comes to the deep web.
 To access the deep web, users must submit queries to web databases, and the results are encapsulated in dynamically generated web pages containing data records. Traditional search engines struggle to index these dynamic pages, necessitating a specialized program for efficient information extraction from the deep web. Web search engines generate result pages based on user queries, making it crucial to automatically extract data from these pages for various applications.
 In this context, we propose an innovative data extraction method called Effective Data Extraction using Preprocessing (EDEP). The EDEP approach begins by parsing the input HTML page, constructing a tag tree, and subsequently eliminating irrelevant tags from the tree. Notably, our system efficiently handles scenarios where auxiliary information, such as recommendations or comments, is intermixed between query result records (QRRs), causing them to be non-contiguous. EDEP also effectively manages result pages containing single QRRs.
 Through experimental results, it is evident that EDEP outperforms existing data extraction methods, showcasing its efficacy in handling the complexities associated with web data extraction.

Do Products Labeled Retailer “Exclusive” Affect Consumer Behavior?

Many large retailers offer “advertised as retailer exclusive products” or AREPs. Retailers work with manufacturers to produce these unique, retailer exclusive versions of otherwise common products. While not all retailer exclusive products are advertised as “exclusive,” AREPs are advertised and labeled as retailer exclusives. The retailer exclusive product attributes are often “trivial,” but still may add customer value and discourage price comparison shopping. Here, two experiments assess AREPs effects on consumers. Contrary to managerial expectations, the findings suggest that AREP exclusivity promotions are ineffective at influencing consumers, but that the choice of exclusive, seemingly “trivial” attribute can sometimes be a significant negative influence on consumers. Further, the authors suggest contexts that may encourage specific responses to retailer exclusive product promotions and trivial attributes.

Common or Not? Variations in E-Commerce Adoption Factors Between India and the USA

ABSTRACT The e-commerce adoption rate is not the same across countries, and there are significant variations. While most technology adoption research has been carried out in on ecommerce across countries, this study focuses on introducing novel constructs with a mix of cross-cultural study. The present study therefore uses a sample mix from a developed nation (the USA) and a developing nation (India) to explore the e-commerce adoption factors. In this study, the supportive factors of perceived trust are hypothesized as causal factors for e-commerce adoption and studied with USA and India results. The factors considered in the proposed model are: hedonic motivation; comparison shopping; and perceived trust (further classified as honesty, benevolence, and competence). The study used Amazon’s MTurk to collect data from Indian (n = 471) and US (n = 440) respondents. The results of the multi-group moderation technique reveal that honesty, comparison shopping, and hedonic motivation were stronger for the US sample, while benevolence and comfort were stronger for the Indian sample. Based on the results, theoretical contributions, managerial implications, and future research directions are discussed.

Consumers with math anxiety, a financially vulnerable group? Unpacking the negative relation between math anxiety and performance on a price comparison task

Comparison shopping is good financial practice, but situations involving numbers and computations are challenging for consumers with math anxiety. We asked North Americans (N = 256) to select the better deal between two products differing in volume and price. As predicted, math anxiety was negatively related to performance on this Price Comparison Task. We then explored the mechanism underlying this relation by testing math competency, price calculation ability, need for cognition, and cognitive reflection as potential mediators. The results from a competing mediator analysis indicated that all factors, apart from need for cognition, served as significant independent mediators between math anxiety and performance on our Price Comparison Task. This study has important implications for how–and why–math anxiety relates to a person’s ability to accurately compare product prices. These data suggest that consumers higher in math anxiety may represent a financially vulnerable population, particularly in the context of financial tasks that are inherently mathematical.

Investigation of Online Shopping Cart Abandonment on the Perspective of E- Procrastination Behavior

Online cart abandonment is one of the most important key factors affecting e- commerce and showing up when consumers place products in their online shopping carts without making a purchase. It is also an important indicator of lost sales, as a revenue-reducing factor for retailers. With the effect of increasing online sales in recent years, online cart abandonment has become a major concern for retailers. Considering the economic impact of the subject, it is very important to understand the factors that lead consumers to this behavior. The purpose of this research was to investigate the factors that affect online cart abandonment. For this purpose, it was aimed to examine the effects of e-procrastination behavior, comparison shopping, need for more information, research and organization need, and emotional ambivalence on online cart abandonment. The partial least square method of structural equation modeling was employed to examine the proposed research model. An online survey was applied to 197 consumers selected by convenience sampling method, who had online cart abandonment experience, and the data set was analyzed using SmartPLS 3 software. Obtained findings showed that comparison shopping and need for more information, need for research and organization, and emotional ambivalence had an effect on e-procrastination behavior, while e-procrastination affected online cart abandonment. In the study, it was also determined that e-procrastination behavior had a partial mediation effect between the need for comparison shopping and more information, the need for research and organization, emotional ambivalence and online shopping cart abandonment behavior. DOI : https://doi.org/10.54663/2182-9306.2022.v10.n19.66-89

Payer-Negotiated Prices in the Diagnosis and Management of Prostate Cancer in 2022

<h3>Purpose/Objective(s)</h3> Prostate cancer is the most common cancer diagnosed among American men. Patients face significant financial costs associated with the diagnosis, staging, and treatment of prostate cancer. Recent federal price transparency rules have attempted to allow patients to comparison shop. The purpose of this study was to evaluate efficacy of such policies for prostate cancer-associated services. <h3>Materials/Methods</h3> A cross-sectional analysis of negotiated Blue Cross Blue Shield (BCBS) rates was performed in hospitals across North Carolina. Inclusion criteria were limited to hospitals with greater than 35 beds that provided prostate cancer treatment services (imaging, radiation, and labs). Price transparency was measured by the percentage of hospitals publicizing negotiated rates for each service. Hospital sizes were estimated by bed count and grouped into quartiles. ANOVA testing was used to assess associations between hospital size and BCBS prices for each service. Chi-square analysis was used to compare percentage price transparency between small and large hospitals. <h3>Results</h3> Ninety hospitals were included in this study, with 86.7% of all services displaying negotiated prices. Smaller hospitals (bed size ≤ 150) were less likely to list negotiated prices for all services (81.4% vs. 91.4%, p=0.002). Of the 46 hospitals with gross IMRT charges listed, only 65% included a BCBS negotiated price, which was lower than that for other prostate-cancer related services. There was no apparent association between service price variation and hospital size (Table 1). <h3>Conclusion</h3> Although federal price transparency policies have mandated hospitals to provide accurate price information to patients, such platforms remain difficult to navigate, and smaller hospitals are less likely to comply with these rules. Interestingly, despite the perceived increased negotiating power of larger hospitals, we did not find that larger hospitals command lower BCBS negotiated rates in our study, though these findings are limited due to paucity of negotiated prices listed in smaller hospitals' chargemasters. Additional initiatives are necessary to provide patients with prices in a consistent format to allow true comparison shopping for prostate cancer care services.

A STUDY OF DIGITAL MARKETING AND ITS IMPACT ON CONSUMER BEHAVIOUR IN THE INDIAN CONTEXT

Changes in marketing strategies and consumer habits have been prompted by the rapid growth of technologies, the globalisation of the financial system, and a variety of other external variables. In business parlance, "marketing" refers to the activities carried out by a company in an effort to increase demand for its products or services. With the use of marketing, the company seeks clients or customers for its products or services in order to fulfil orders. Customers are given the opportunity to view the information regarding the product that is being offered by the company, and they are able to be prepared to do appropriate comparison shopping. As a result, customers can appreciate their right to decision and can place orders whenever 24*7 at any location. The concept of "responsibility" is one of the most important factors that goes into internet buying. In internet marketing, this helps to gauge a potential customer's level of discernment prior to making a purchase. Customers who shop for goods and services online should have their orders delivered within the time frame and with the level of quality that has been promised by the merchants. The trust of one's customers is another essential component in the growth of an internet company. Because of new technologies and an increase in the number of people who are committed to using the internet, the interconnectedness of consumers is gradually growing. As a result, consumer behaviour has changed, and businesses should learn to understand the behaviour of their customers. The purpose of this research was to evaluate the impact that digital marketing has on the shopping behaviours of consumers. This focus primarily is on the evolution of digital marketing in promoting companies on a global scale, gaining a better understanding of buyer preferences, and analysing the behaviour of customers as an example.

Financial (dis-)information: Evidence from a multi-country audit study

We conduct an audit study in Ghana, Mexico and Peru to assess whether mandated disclosures are enforced and whether products offered meet the needs of low-income customers. Trained auditors visited financial institutions seeking credit and savings products. We find that staff only provides cost information when asked, disclosing less than a third of the total cost voluntarily. With this limited information, the advantages to comparison shopping are small. In addition, clients are rarely offered the least costly product.

Moderating effect of cognitive conflict on the relationship between value consciousness and online shopping cart abandonment

ABSTRACT Online shopping cart abandonment has become a major problem for e-tailing firms. This study aims to understand consumers’ online shopping behavior and provide valuable insights regarding psychological and cognitive factors behind shopping cart abandonment. The study uses structural equation modeling and SPSS process Macro for data analysis. The results show that ‘comparison shopping’ mediates the relationship between value-consciousness and shopping cart abandonment. Further, cognitive conflicts moderate the mediating effect of value-consciousness on shopping cart abandonment through comparison shopping. This study contributes to consumer behavior and retailing literature by identifying factors that influence online shopping cart abandonment behavior while examining cognitive consistency theory in the Indian online market. It enumerates several important implications and strategies for e-tailers, which they could implement to reduce shopping cart abandonment.

Comparison shopping: Is neuropsychological evaluation more expensive than neuroimaging?

Objective: This study empirically examined if neuropsychological evaluation (NPE) is expensive compared to other diagnostic procedures such as neuroimaging. Method: We aggregated data on charges for NPE and common neuroimaging procedures (e.g., head CT and brain MRI) from hospitals in the U.S. Charges for five-hour NPE and eight-hour NPE were compared to charges for head CT and brain MRI, respectively. Difference scores were calculated between five-hour NPE and head CT and between eight-hour NPE and brain MRI. A charge difference of $250 or less was considered minimal. NPE and neuroimaging charges were compared across U.S. regions. Results: Median head CT charges were $1942 to $2699. Median brain MRI charges were $3103 to $4487. Median five-hour NPE charges were $1855 to $1977. Median eight-hour NPE charges were $2757 to $2917. Head CT and five-hour NPE charges were not significantly different. Eight-hour NPE and brain MRI charges were not significantly different. Charge differences between five-hour NPE and head CT were minimal in 32.3% of hospitals. Charge differences between eight-hour NPE and brain MRI were minimal in 21.2% of hospitals. U.S. regions were not significantly different in charges for NPE or neuroimaging. Conclusions: Findings provide preliminary data on charges for NPE in relation to charges for common imaging procedures. NPE does not appear to be more expensive than neuroimaging and, in fact, appears comparable. Future research might expand the information on NPE charges to include additional settings.

Exploring energy-saving refrigerators through online e-commerce reviews: an augmented mining model based on machine learning methods

PurposeBased on climate issues and carbon emissions, this study aims to promote low-carbon consumption and compel consumers to actively shift to energy-saving appliances. In this big data era, online reviews in social and electronic commerce (e-commerce) websites contain valuable product information, which can facilitate firm business strategies and consumer comparison shopping. This study is designed to advance existing research on energy-saving refrigerators by incorporating machine learning models in the analysis of online reviews to provide valuable suggestions to e-commerce platform managers and manufacturers to effectively understand the psychological cognition of consumers.Design/methodology/approachThis study proposes an online e-commerce review mining and management strategy model based on “data acquisition and cleaning, data mining and analysis and strategy formation” through multiple machine learning methods, namely, Bayes networks, support vector machine (SVM), latent Dirichlet allocation (LDA) and importance–performance analysis (IPA), to help managers.FindingsBased on a case study of one of the largest e-commerce platforms in China, this study linguistically analyzes 29,216 online reviews of energy-saving refrigerators. Results indicate that the energy-saving refrigerator features that consumers are generally satisfied with are, in sequential order, logistics, function, price, outlook, after-sales service, brand, quality and space. This study also identifies ten topics with 100 keywords by analyzing 18 different refrigerator models. Finally, based on the IPA, this study allocates different priorities to the features and provides suggestions from the perspective of consumers, the government and manufacturers.Research limitations/implicationsIn terms of limitations, future research may focus on the following points. First, the topics identified in this study derive from specific points in time and reviews; thus, the topics may change with the text data. A machine learning-based online review analysis platform could be developed in the future to dynamically improve consumer satisfaction. Moreover, given that consumers' needs may change over time, e-commerce platform types and consumer characteristics, such as user profiles, can be incorporated into the model to effectively analyze trends in consumers' perceived dimensions.Originality/valueThis study fills the gap in previous research in this field, which uses small-sample data for qualitative analysis, while integrating management ideas and proposes an online e-commerce review mining and management strategy model based on machine learning methods. Moreover, this study considers how consumers' emotional and thematic preferences for products affect their purchase decision-making from the perspective of their psychological perception and linguistically analyzes online reviews of energy-saving refrigerators using the proposed mining model. Through the improved IPA model, this study provides optimizing strategies to help e-commerce platform managers and manufacturers.

The Effect of Online Q&amp;As and Product Reviews on Product Performance Metrics: Amazon.com as a Case Study

Online reviews have received an overwhelming interest in the recent decades. Comparatively speaking, the online product questions and answers (Q&amp;As) have received less attention than online reviews, despite that they both affect the image and the value of a project. Although online reviews and Q&amp;As are both forms of user generated knowledge ion online communities, they may affect customers decision making differently. Furthermore, Q&amp;As are very useful for pre-purchase information-searching and comparison shopping, especially when online product reviews either do not provide the needed answer or getting the desired information requires additional “cost” (i.e. time and effort) to sort out. Our findings show that Q&amp;A traits had a varying effect on the product performance. We also found that review helpfulness is another important factor that affects product sale and popularity on e-commerce sites. The present study adds to existing electronic word-of-mouth (eWOM) and product review literature.

Changes in key food purchasing practices of Supplemental Nutrition Assistance Program (SNAP)-eligible older adults following SNAP benefit receipt.

Little is known about how the utilization of Supplemental Nutrition Assistance Program (SNAP), the largest federal food assistance program, affects food purchasing practices (FPP) among low-income older adults who typically have low SNAP participation rates. This study examined changes in FPP of SNAP-eligible non-participating older adults as they became SNAP recipients. A longitudinal mixed-methods approach was employed to assess six key FPP constructs among 10 SNAP-eligible non-participating older Georgians (median age: 65.5 [IQR 62.5, 73.25], 80% female, 100% African American) at three time points: (a) before SNAP benefit receipt, (b) 1 month after SNAP benefit receipt, and (c) 3 months after SNAP benefit receipt. Data were collected using individual in-depth interviews, interviewer-administered surveys, food purchase receipt collection, and grocery shopping trip observations. Appropriate exploratory and descriptive data analyses were conducted. Changes occurred in all six FPP constructs following SNAP benefit receipt. Participants shopped primarily at supermarkets, reporting shopping fewer times per month, while increasing the number of purchases per trip. Participants supplemented their SNAP benefits with their own money, reported spending more on groceries, purchased more types of foods, and employed strategies like comparison shopping. The findings suggest that SNAP benefit receipt affects FPP of low-income older adults and that identifying changes in key FPP related to SNAP benefit receipt could inform strategies to maximize the impact of SNAP benefits on improving food security and nutrition of low-income older Americans.

Spatial clustering: Influence of urban street networks on retail sales volumes

As is often believed that the more centrally located a shop, the higher its sales volume, this paper analyzed relationships between the spatial clustering of retail stores, their respective transaction volumes, and the urban street networks to determine whether, and to what extent, the accessibility and density of a store’s location was correlated with its transaction volume. While this hypothesis is widely accepted, its veracity is underexplored and rarely validated using large-scale empirical datasets, possibly owing to the lack of access. Therefore, transaction datasets and accessibility indicators were first examined; a clear, positive correlation between density and revenue was found for specialty stores wherein people do “comparison shopping,” and for stores that complemented each other for activities such as “one-trip shopping,” the revenues were positively correlated when the stores were clustered. Generally, daily-use stores’ revenues were more sensitive to local access and those of non-daily-use stores were more sensitive to global access. In conclusion, these findings would not have been found using conventional methodology focused on the retail sector as a whole, because aggregate market mechanisms would have hidden the observed effects on specific store categories. Therefore, upon disaggregating the data, we found a distinct heterogeneity across the different store types for what concerns the relationship between revenue and location.

No Alarms and Many Surprises: Salience as a Basis For Excessive Pricing Intervention in an Antitrust Context

Abstract This article explores how consumers’ bounded rationality can justify antitrust intervention when a firm becomes a monopoly and exploits a product attribute that was not policed by market forces when there was competition for the market. Behavioral economics predicts product complexity leads consumer demand to be a function of salient costs and benefits rather than of actual costs and benefits of products. The divergence between the former and the latter hinders and distorts competition. In fact, comparison shopping is costlier, and sellers can backload part of their prices to nonsalient product attributes. Consumers perceive only a distorted lower price by focusing on salient product features, which leaves room for inefficient matching and opportunistic behavior given the risk of ex post exploitation. These are behavioral limits of competition. In this work, I argue that when (i) there is a lock-in problem, (ii) consumers do not control the probability of triggering a hidden price, and (iii) a typical consumer could not have reasonably expected to find such a hidden price, antitrust intervention would not only deter ex post exploitation but would also enhance competition on the real price of goods. Antitrust would correct a behavioral market failure.

Effects of a food hub initiative in a disadvantaged community: A quasi-experimental evaluation.

A quasi-experiment evaluated a food hub's (FH) impact in a low-income/low-access (food desert) setting on fruit and vegetable (F&V) intake, diet quality, kilocalories, perceived food environment, BMI, and farmers' market shopping versus a matched community (n=265 FH, n=262 Comparison). Comparison shoppers had better baseline perceptions of their food environment, but FH shoppers improved significantly more than Comparison shoppers. Comparison shoppers significantly increased F&V intake versus FH shoppers. Effects were not significant for other diet outcomes, BMI, or farmers' market shopping. Factors besides spacial access to healthy food need consideration to address dietary intake and obesity in disadvantaged communities.

A Pure Visual Approach for Automatically Extracting and Aligning Structured Web Data

Database-driven websites and the amount of data stored in their databases are growing enormously. Web databases retrieve relevant information in response to users’ queries; the retrieved information is encoded in dynamically generated web pages as structured data records. Identifying and extracting retrieved data records is a fundamental task for many applications, such as competitive intelligence and comparison shopping. This task is challenging due to the complex underlying structure of such web pages and the existence of irrelevant information. Numerous approaches have been introduced to address this problem, but most of them are HTML-dependent solutions that may no longer be functional with the continuous development of HTML. Although a few vision-based techniques have been introduced, various issues exist that inhibit their performance. To overcome this, we propose a novel visual approach, i.e., programming-language-independent, for automatically extracting structured web data. The proposed approach makes full use of the natural human tendency of visual object perception and the Gestalt laws of grouping. The extraction system consists of two tasks: (1) data record extraction, where we apply three of the Gestalt laws (i.e., laws of continuity, proximity, and similarity), which are used to group the adjacently aligned visually similar data records on a web page; and (2) data item extraction and alignment, where we employ the Gestalt law of similarity, which is utilized to group the visually identical data items. Our experiments upon large-scale test sets show that the proposed system is highly effective and outperforms the two state-of-art vision-based approaches, ViDE and rExtractor. The experiments produce an average F1 score of 86.02%, which is approximately 55% and 36% better than that of ViDE and rExtractor for data record extraction, respectively; and an average F1 score of 86.19%, which is approximately 39% better than that of ViDE for data item extraction.

Content Analysis of Interviews with Hydrogen Fuel Cell Vehicle Drivers in Los Angeles

Hydrogen fuel cell vehicles (HFCVs) are zero-emission vehicles (ZEVs) and their widespread adoption may help to mitigate some of the issues arising from fossil-fuel usage in the transportation sector. Only in recent years have these vehicles become available for purchase or lease in the United States, and only within the State of California. In 2018, nearly 5,500 HFCVs had been sold or leased in California, supported by a developing refueling infrastructure there. This population represents a unique opportunity, as previous studies on HFCV adoption have largely employed hypothetical stated preference surveys distributed to likely adopters. Seeking to investigate the real experiences of actual adopters from their own perspectives, semi-structured interviews were conducted with 12 early adopters of HFCVs in the Los Angeles metropolitan area. Thematic content analysis of these interviews was conducted to identify the prevalence of factors deductively derived from published literature. All respondents considered lifetime cost of vehicle ownership, engaged in comparison shopping, and assessed the adequacy of the refueling infrastructure by various geographical criteria. Environmental concerns motivated many respondents to pursue HFCV adoption, though only if it made financial sense. Respondents chose HFCVs over battery electric vehicles after consideration of range, refueling time, and cost. Early HFCV adopters consistently cast their adoption of the technology as a contribution to a diverse ZEV marketplace. Strategies for the promotion of HFCV technology must account for this range of variation in early-adopter motivations, concerns, and behaviors which might complicate targeted HFCV promotion strategies.