Determinants of purchase intention, satisfaction, and risk reduction: The role of knowledge and information search among mortgage buyers

Reddy, N. Srinivasa; Thanigan, Jayanthi Article Determinants of purchase intention, satisfaction, and risk reduction: The role of knowledge and information search among mortgage buyers Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Reddy, N. Srinivasa; Thanigan, Jayanthi (2023) : Determinants of purchase intention, satisfaction, and risk reduction: The role of knowledge and information search among mortgage buyers, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 11, Iss. 2, pp. 1-36, https://doi.org/10.1080/23322039.2023.2266659 This Version is available at: https://hdl.handle.net/10419/304228 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Determinants of purchase intention, satisfaction, and risk reduction: The role of knowledge and information search among mortgage buyers N Srinivasa Reddy & Jayanthi Thanigan To cite this article: N Srinivasa Reddy & Jayanthi Thanigan (2023) Determinants of purchase intention, satisfaction, and risk reduction: The role of knowledge and information search among mortgage buyers, Cogent Economics & Finance, 11:2, 2266659, DOI: 10.1080/23322039.2023.2266659 To link to this article: https://doi.org/10.1080/23322039.2023.2266659 © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Published online: 09 Oct 2023. Submit your article to this journal Article views: 1305 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 Determinants of purchase intention, satisfaction, and risk reduction: The role of knowledge and information search among mortgage buyers N Srinivasa Reddy and Jayanthi Thanigan Cogent Economics & Finance (2023), 11: 2266659 GENERAL & APPLIED ECONOMICS | RESEARCH ARTICLE Determinants of purchase intention, satisfaction, and risk reduction: The role of knowledge and information search among mortgage buyers N Srinivasa Reddy 1 * and Jayanthi Thanigan 1 Abstract: As housing demand rose post-COVID-19, new mortgage buyers with distinct preferences are entering the market. Nevertheless, the mortgage purchasing process can prove intricate and precarious for individuals lacking familiarity. Customers leverage various online platforms and supplementary sources to augment their knowledge and mitigate perceived risks, enabling them to make wellinformed decisions during the mortgage buying process. Despite exerting these efforts, customers continue to harbor unfavorable purchase intentions due to their subpar purchasing experience, leaving mortgage lenders grappling with retention issues. Research has highlighted that only 15% of mortgage transfer customers would buy a mortgage from the original provider. The present study examines ABOUT THE AUTHOR Srinivasa Reddy is currently an Assistant Professor in Marketing at T A Pai Management Institute, Manipal, India. His current research interests are in consumer behavior in housing and mortgage markets. He is interested in the challenges facing households in their homeownership journey. As more customers enter the housing markets in developing economies, studying their behavior offers interesting research avenues. He worked in the banking and mortgage industry for over 11 years before entering Academica. PUBLIC INTEREST STATEMENT The present study analyses customer satisfaction, knowledge, and perceived risk during mortgage buying. Several mortgage lenders face the challenge of adequately satisfying mortgage buyers. In addition, customers are reluctant to buy again from the same mortgage provider. In this context, the authors show that customer satisfaction positively affects purchase intention. The customer`s first experience with the mortgage provider must be good. Providing adequate expertise and reassurance appears to be vital to improving satisfaction. Further, knowledgeable customers are more satisfied with their mortgage provider. Due to the high product complexity in mortgages, customers perceive high risk in mortgage buying. In order to reduce perceived risk, providing mortgage buyers with clear and understandable information is crucial. The study also presents a new and integrated model to analyze customer information processing in mortgages that combines Customer Empowerment Theory and Uncertainty Reduction Theory. This model offers insights into reducing perceived risk and increasing customer knowledge during mortgage buying. The findings have implications for mortgage providers and policymakers in terms of boosting customer information search and offering default options based on demographic profiles. Reddy & Thanigan, Cogent Economics & Finance (2023), 11: 2266659 https://doi.org/10.1080/23322039.2023.2266659 Page 2 of 36 Received: 01 December 2022 Accepted: 30 September 2023 *Corresponding author: N Srinivasa Reddy, Marketing Area, T A Pai Management Institute, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India E-mail: [email protected] Reviewing editor: Jasman Tuyon, Faculty of Business and Management, Universiti Teknologi Mara, Malaysia Additional information is available at the end of the article © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. mortgage purchase intention and risk reduction by integrating customer empowerment and uncertainty reduction theories to contribute to the existing literature on mortgage decision-making. A survey was conducted among 554 mortgage buyers, and PLS-SEM was used to test the hypotheses. Knowledge positively influenced satisfaction (β = 0.15, p= = 0.01), and satisfaction positively affected purchase intention (β = 0.75, p = 0.01). The mediating mechanism of risk reduction through involvement and information search is also established. The findings suggest that customers involved in decision-making and information search are more likely to be satisfied with their purchase and experience less risk. Mortgage companies should encourage customers to be more involved and provide complete information during buying. As involved customers experience less confusion; they will make sound financial decisions and remain satisfied and loyal to the mortgage company. Using behavioral finance, policymakers could provide customers necessary nudges to improve decision-making. Subjects: Microeconomics; Financial Services Industry; Business, Management and Accounting Keywords: mortgage satisfaction; Purchase intention Knowledge; Risk-reduction; Mediation; Involvement; Mortgage decision-making 1. Introduction to the study Consumer mortgage decision-making is fraught with uncertainty, high risk, and low customer search and satisfaction. The COVID-19 pandemic has impacted customers’ housing preferences (Gamber et al., 2022; Liu & Su, 2021). Mortgages makeup 40% of a typical household’s liabilities, and poorly bought mortgages have lasting financial consequences (Agarwal et al., 2017; Frydman & Camerer, 2016). Seeking information can help customers make better decisions, reducing their risk and increasing satisfaction (Andersen et al., 2020, 2020; Kim & Ziobrowski, 2016; Mesly, 2021; Sharma et al., 2022; Zhang et al., 2021). Mortgage risk arises when households cannot accurately forecast future interest rates, property value decreases, and their ability to make payments (Kim & Ziobrowski, 2016; Timmons et al., 2022). The literature on consumer risk reduction demonstrates the importance of information in reducing perceived risk (Daugherty et al., 2008; Lin et al., 2021). Along with word-of-mouth and salesperson guidance, the Internet is a crucial source of mortgage information (Boehm & Schlottmann, 2020; Hochstein et al., 2019). van Ooijen and van Rooij (2016) demonstrate that, though households can manage their daily finances, several households cannot comprehend financial risk during mortgage shopping. Hence, unprepared families face complex and risky mortgage purchases (Khan et al., 2022). Research into customer information search using offline and online sources is relatively new (Vinhas & Bowman, 2019). Recent work on the role of the Internet in consumer mortgage behavior has focused on the lending behavior of mortgage Fintechs (Fuster et al., 2022; Haupert, 2022), predicting mortgage delinquency (Chauvet et al., 2016), predicting mortgage demand (Carella et al., 2020; Pavlicek & Krištoufek, 2019), uptake of financial education (Chin & Williams, 2020), and savings from information search (Damen & Buyst, 2017). However, none of these studies comprehensively examines the role of internet information search in reducing risk and enhancing mortgage satisfaction and purchase intention. According to Jefferson and Thomas (2020), having access to more information is key for customers when deciding on a mortgage. High search levels empower customers to make informed decisions, but few studies have explored mortgage customer information searches (Damen & Buyst, 2017; Woodward & Hall, 2012). Because of the high stakes, customers actively seek information, as knowledge of mortgages improves customers’ risk assessment and the likelihood of buying a suitable mortgage (Bialowolski et al., 2022; Fornero et al., 2011). Hence, little is known about the mechanism to increase mortgage knowledge or reduce risk perception. When making Reddy & Thanigan, Cogent Economics & Finance (2023), 11: 2266659 https://doi.org/10.1080/23322039.2023.2266659 Page 3 of 36 mortgage and financial decisions, consumers often don’t have enough financial knowledge and don’t feel motivated to search for information (Damen & Buyst, 2017; Dawes et al., 2009; Nicholson et al., 2019; Xiao & Huang, 2021). Existing research emphasizes enhancing financial literacy and reducing customer uncertainty and risk in mortgage transactions, but does not examine the underlying mechanism (Andersen et al., 2020; Malliaris et al., 2022; Woodward & Hall, 2012). Also, mortgage customer satisfaction research is scarce (Amin, 2020; Amin et al., 2011; Loibl et al., 2020; Reddy & Thanigan, 2022). The opaque structure of the mortgage buying process and heterogeneous stakeholders leads to less trust and satisfaction, and customers report reduced satisfaction with mortgage providers (Bhattacharya et al., 2021; Power, 2021), with several consumers quick to refinance. Only 28% of mortgage lenders met the buyers’ criteria for expertise, guidance, and communication, causing low customer satisfaction levels (Power, 2022a). Further, only 15% of mortgage transfer customers were willing to purchase a mortgage from the original mortgage provider (Power, 2022b). Drawing on the gaps in the literature, this paper aims to address two research objectives: first, to assess the antecedents of mortgage satisfaction and purchase intention. Second, the study establishes the mechanism of consumer risk reduction during mortgage buying by assessing the mediating effect of information search and involvement in decision-making. Hence, the following questions arise: What key factors influence consumer satisfaction and purchase intention in the mortgage context (Reddy & Thanigan, 2022)? And, what role do information search and involvement play in mitigating perceived risk (Denegri-Knott et al., 2006; Hu & Krishen, 2019; Santos & Gonçalves, 2019)? To answer the research question, the study constructs a conceptual model that integrates two crucial and complementary theories of consumer information processing: the customer empowerment theory (CET) and the uncertainty reduction theory (URT). The customer empowerment theory states that knowledge empowers customers to make autonomous decisions (Denegri-Knott et al., 2006; Hu & Krishen, 2019). A key aspect of customer empowerment theory is customer involvement and knowledge’s role in enhancing satisfaction (Hu & Krishen, 2019; Wolf et al., 2015). Uncertainty reduction theory focuses on how buyers simplify complex and risky purchase tasks by seeking internal and external knowledge (Flavián et al., 2016; Kramer, 1999; Santos & Gonçalves, 2019). Uncertainty theory has recently been utilized to explain Internet search and consumer behavior (Lin et al., 2021; Lu & Chen, 2021; Santos & Gonçalves, 2019). This integrated model, proposed by the authors, explains the mechanism of reducing mortgage customer risk and increasing satisfaction. The authors surveyed 554 households who have recently purchased a mortgage. Since the analysis involved theory extension and testing, the partial least squares method (PLS-SEM) was used (Hair et al., 2019). The researchers show that knowledge significantly impacts satisfaction and purchase intention. The mechanism of risk reduction through involvement and information search is also established. The present study contributes to the understanding of mortgage decision-making by filling several gaps in the literature. First, the study extends the current literature on mortgage knowledge, customer satisfaction, and purchase intention (Reddy & Thanigan, 2022; Xiao & Porto, 2017). Second, the authors examine how search mediates between perceived risk and mortgage product knowledge. This examination explains the underlying mechanism by which the relationship between perceived risk and mortgage knowledge is mediated through involvement and search efforts. To the author’s knowledge, no recent studies have empirically examined the risk reduction process in mortgages. There are more recent studies on risk reduction from the Internet and mobile banking environment, but none on the mortgage environment (Marafon et al., 2018; Mulia et al., 2020). The present study expands the literature related to mortgage product complexity, risk perception, information search, involvement, knowledge, and purchase outcomes of satisfaction and purchase intention by examining the mechanisms of information search by integrating customer empowerment theory and uncertainty reduction theory. This integration contributes to the theoretical development of customer information processing in mortgages and the role of information in reducing complexity and risk while improving knowledge and satisfaction. Reddy & Thanigan, Cogent Economics & Finance (2023), 11: 2266659 https://doi.org/10.1080/23322039.2023.2266659 Page 4 of 36 The rest of the paper is organized as follows. The literature review and the development of the hypotheses are presented next. The third section introduces the research methodology. The fourth section presents the results and discussion; the final section highlights the conclusion and limitations of the study. 2. Literature review and hypotheses development 2.1. Theoretical Background 2.1.1. Information overload and product complexity Information overload occurs when the information load exceeds the limited cognitive capacity of the individual. Research highlights that there is a significant detrimental effect on consumer behavior due to information overload (Alba & Hutchinson, 1987). Overloading customers with information can slow their decision-making, lower decision quality, and increase anxiety. The complex neural mechanism of information overload is thoroughly studied by Peng et al. (2021). In the presence of information overload, the central brain area continues to process the information for a time, even beyond the formal decision-making phase. This suggests cognitive agility, where the brain evaluates and compares important information beyond decision-making. However, while this adaptability showcases the brain’s capacity, it also raises questions about the cognitive burden imposed by this prolonged engagement with information. The study conducted by Pernagallo and Torrisi (2022) explores the impact of consumer overload on the effectiveness of financial markets. In their work, Pernagallo and Torrisi (2022) suggest that abundant knowledge can disrupt the basic principles of market balance and go against the conventional belief in informational efficiency. This observation highlights the complex interplay between the processing of information and the behavior of the market. According to PhillipsWren and Adya (2020), consumer overload has been identified as a significant stressor in consumer decision-making. Decision support systems that aim to aid customers can cause stress by overwhelming consumers with information. A paradox can arise in high-stress decision-making, where tools created to help reduce stress may actually amplify it due to the nature of the information involved. Phillips-Wren and Adya (2020) suggest that it is imperative to find a middle ground between utilizing information to make well-informed decisions and avoiding the potential adverse effects of cognitive and psychological overload. Bhutta et al. (2015) state that policymakers believe that greater process transparency can empower households to make better quality decisions. As a result, policymakers mandate that more regulatory and competitive information be shared with customers (Nicholson et al., 2019). For example, there is an increase in mortgage comparison portals (Damen & Buyst, 2017; Damen & Schildermans, 2022; Haupert, 2022). However, customers who access too much market information perceive product complexity (Huang, 2000). Complexity and information overload increase stress, unpleasant emotions, and consumer dissatisfaction (Mick et al., 2004). Mortgage brokers and bankers use confusing language to sell to customers with little mortgage knowledge or experience (Woodward & Hall, 2012). In the realm of financial services, such as mortgages, issues related to bank transparency and information concealment have gained prominence in recent years (Nicholson et al., 2019). This is particularly pertinent because mortgages exhibit two pivotal characteristics associated with complex products: firstly, they are accompanied by low levels of consumer knowledge, and secondly, there exists a significant risk of consumer exploitation by market participants (Mützel & Kilian, 2016). To navigate this product complexity, consumers employ strategies rooted in information processing theories, as they strive to identify diagnostic and salient product attributes that offer high informational value prior to making decisions (Mitchell, 1999; Mitchell & McGoldrick, 1996; Simon, 2000). Hence, customers constructively use information relevant to the type of problem they face (Bettman et al., 1998). However, limited research has explored the intricate relationship between product complexity and information in the mortgage context. Reddy & Thanigan, Cogent Economics & Finance (2023), 11: 2266659 https://doi.org/10.1080/23322039.2023.2266659 Page 5 of 36 2.1.2. Perceived risk One of the critical outcomes of information overload and complexity is a higher perceived risk (Hu & Krishen, 2019; Soto-Acosta et al., 2014). Perceived risk refers to the uncertainty experienced by customers when they are unable to anticipate the outcomes of their purchase (Goyal, 2008, pp. 332–333). Hence, product knowledge significantly mitigates risk and uncertainty by allowing customers to set accurate expectations. Further, in services, perceived risk is higher because of the experiential nature of the product (Goyal, 2008; Song et al., 2020). A meta-analysis by Li et al. (2020) showed that high-risk perception and customer purchase intention are negatively correlated. Li et al. (2020) discovered an effect size of − 0.239 and showed the importance of understanding how risk perception affects consumer decision-making. In their study, Holzmeister et al. (2020) investigate the perception of risk associated with financial products and identify its considerable repercussions for the overall well-being of consumers. Recognising the complex and often opaque nature of financial products, Holzmeister et al. (2020, p. 3987) advocate implementing “risk facts labels” on financial products to improve consumer decision-making. Holzmeister et al. (2020) highlight that such “risk facts labels” would serve as a powerful tool to empower consumers and enhance their decision-making capabilities. Therefore, the perception of risk among customers substantially influences their purchase intention and behaviors. Recent research shows that perceived risk negatively influences customer satisfaction and loyalty (Hasan et al., 2021; Khasbulloh & Suparna, 2022). Hence, recent studies identify that perceived risk generally has a negative impact on customer satisfaction, loyalty, and purchase intentions. Therefore, scholarly literature supports the notion that individuals who have made the decision to acquire a residential property should consider several approaches to minimize their perceived risk when choosing a mortgage lender. This is because after individuals have chosen a house, they cannot withdraw from the mortgage process. Understanding and mitigating consumers’ perceived risks are crucial for banks and mortgage providers to enhance customer satisfaction and bolster purchasing inclinations. Typically, people buy mortgages after choosing a home. Therefore, those choosing a mortgage provider must find a way to lower their perceived risk because they cannot opt out (Perry & Lee, 2012). In the mortgage context, perceived risk relates to the variance in the mortgage provider’s performance. For example, if the bank charges an origination fee of 1.5% instead of the advertised 0.25%, this increases the cost, or if the invitation rate is 2% and the bank raises it to 4% after six months, the family budget is at risk. Consumer stress in the U.S. and U.K. mortgage markets from rate hikes is too recent and needs no documentation (Savino, 2022). The process of consumer risk management is constructive; customers respond to risk based on the purchasing context and personal goals (Bettman et al., 2008; Conchar et al., 2004). Consistent with Taylor’s (1974) risk management model, the present study examines the importance of information search as a consumer-perceived risk reduction strategy (Chaudhuri, 2000). Mitchell and McGoldrick (1996) summarise the consumer risk reduction process. They propose that in high-risk decisions, customers try to increase the certainty that the decision will not fail rather than be concerned about lessening the repercussions of a poor decision. Customers increase certainty by researching information sources. Further, customers seek knowledge that either clarifies or simplifies the decision. Clarification helps the customer improve their ability to diagnose the information, whereas simplification involves following others’ advice. For example, a simplification strategy is when a customer buys a mortgage with the lowest origination fees. A clarifying technique uses the same origination fee information to analyze whether additional benefits—fast processing, fewer bank trips, and home service—are worth the fee. Because home loans are high-stakes products, customers seek a clarification strategy of increasing knowledge of the critical attributes of mortgages before deciding. Contrary to this rational view of the customer (Bettman et al., 2008), contend that first-time customers overlook risk when risk probabilities cannot be decreased to zero. This means that new customers ignore risks that cannot be completely eliminated. Few customers react emotionally to perceived risk. Customers overreact to risk by focusing on safety while ignoring other information or Reddy & Thanigan, Cogent Economics & Finance (2023), 11: 2266659 https://doi.org/10.1080/23322039.2023.2266659 Page 6 of 36 under-react when they realize that risk probability cannot be decreased to zero. For example, the possibility of a bank raising interest rates in the next six months is non-zero for the consumer; thus, they may under-react or ignore this information but overreact to negotiable closing fees. The authors attempt to clarify this literature and understand the actual behavior of mortgage-buying customers. 2.1.3. Involvement Involvement is a customer’s interest in a product class (Abdel Wahab et al., 2023; Dholakia, 1997), and the customer’s level of purchase decision involvement reflects the decision’s importance. Research has highlighted that when it comes to high-risk purchases like mortgages, perceived risk is a key factor in people’s level of involvement (Aldlaigan & Buttle, 2001; Bloch et al., 1986; Dholakia, 2001; Laurent & Kapferer, 1985). Customers cannot increase their level of knowledge if they are not involved in learning more about the product or service. While advertising relies on rote learning, customer involvement improves knowledge retention for products like mortgages, where decisions are made based on comprehensive understanding. Higher involvement enhances customer knowledge and self-persuasion efforts (Chaiken & Ledgerwood, 2011; Petty & Cacioppo, 1986; Silic & Ruf, 2018). So, competent consumers can acquire new insights from old facts. 2.1.4. Information search Customer information search efforts include the perceived benefits of the search and the Internet sources visited by the customer. Perceived search benefits are about the customer’s perception that searching for more information would benefit the purchase decision and measure the subjective perceptions of the customer. Customers seek information from multiple sources, such as friends, family, third parties, and banks (Choudhary & Zhang, 2023; Devlin, 2002; Lee & Hogarth, 2000). The relevance of social media in augmenting customer search is underscored in a recent study conducted by Pop et al. (2022). Glogovețan et al. (2022) emphasize that when engaging in online purchasing, individuals are subject to several influences, including the opinions and recommendations of their social network, personal characteristics such as personality traits, and their degrees of knowledge and curiosity. The number of internet sources (Dawes et al., 2009) reflects the objective search effort by the customers. Customers use bank and comparison websites to find more mortgage information (Damen & Buyst, 2017). According to a study conducted by Laffey and Gandy (2009) in the United Kingdom, comparison websites provide consumers with the convenience of efficiently comparing various products, typically arranged by price. These websites serve as a valuable resource for sellers, connecting them with potential customers who have already narrowed their preferences through comparison. According to the survey conducted for the present study, the typical consumer examined an average of 3.6 websites while deciding on a mortgage. Research by (Damen & Buyst, 2017) has shown that adequate customer information search leads to higher knowledge and landing a good deal. 2.1.5. Satisfaction and purchase intention Oliver (2014, p.8) defines customer satisfaction as “a customer’s judgment that a service provided a pleasurable level of fulfilment” and is related to the psychological distance between customer expectations and actual firm performance. Recent research shows that customers with a strong knowledge of financial products are satisfied (Barbu et al., 2021; Garrett & James Iii, 2013; Joo & Grable, 2004; Xiao & Porto, 2017). Customers reduce risk by buying the same product when satisfied or the competition when dissatisfied (Mitra et al., 1999; Torres-Moraga et al., 2008). Consumer satisfaction strongly influences purchase intention (Tuu et al., 2011). In a recent study on home buying behavior, Dash et al. (2021) found that high satisfaction leads to high purchase intention. Despite similar satisfaction levels, industryspecific purchase intentions vary (Power, 2021; Szymanski & Henard, 2001). 2.2. Theoretical underpinnings of the research model According to Ravitch and Riggan (2016), theoretical frameworks are the formal theories that address the research questions, enable the researcher to understand the phenomenon and provide a rationale for the hypothesis and methodology. For the present study, the authors use the framework of customer empowerment theory and uncertainty reduction theory to examine the role of information in reducing risk and improving consumer knowledge and satisfaction (Hu & Reddy & Thanigan, Cogent Economics & Finance (2023), 11: 2266659 https://doi.org/10.1080/23322039.2023.2266659 Page 7 of 36 relationships is a test of the plausibility of the hypotheses proposed by the researcher. Because the PLS-SEM technique processes multiple endogenous and exogenous variables simultaneously, this technique is appropriate for analyzing the antecedents of mortgage satisfaction and purchase intention (Hair et al., 2019). Further, PLS-SEM is a technique that allows for extending theories in complex fields, such as mortgage buying (Hair et al., 2019). Appendix Figure A1 summarizes the methodology adopted in the present study. 4.1.1. Measurement model The evaluation of reliability in the context of structural equation modeling refers to the examination of the consistency and stability of the measurement instruments employed to capture latent constructs (Hair et al., 2019). Reliability is evaluated using internal validity and composite reliability tests (Henseler et al., 2016). The evaluation of internal validity, as measured by Cronbach’s alpha, measures the extent to which the indicators within a latent construct reliably capture exactly the same underlying psychological concept. The Cronbach’s alpha coefficient exceeded 0.6 for all latent constructs, thus demonstrating an adequate degree of internal validity. Composite reliability offers an assessment of the extent to which the observed indicators accurately represent the actual scores of the latent construct. In this study, the latent variables have composite reliability values above 0.7 (Hair et al., 2019; Peterson & Kim, 2013). Hence, the measurement model proposed by the authors has high reliability. The authors use Harman’s single-factor test to check common method bias (Podsakoff et al., 2003). Single-factor extraction was 19.7%, well below the 50% criterion, indicating no common method bias. Table 1. Demographic profile Frequency Percent Gender Male 365 65.9 Female 184 33.2 Did not disclose 5 0.9 Total 554 100 Occupation Salaried 390 70.4 Self-employed 162 29.2 Did not disclose 2 0.4 Total 554 100 Property Type Detached House 47 8.5 Apartment 350 63.2 Home extension 82 14.8 Plot of land 75 13.5 Total 554 100 Loan amount (in lakhs) Mean 28.1 Median 25 Minimum 2 Maximum 150 Loan to value Mean 0.61 Median 0.56 Adapted from survey data. Reddy & Thanigan, Cogent Economics & Finance (2023), 11: 2266659 https://doi.org/10.1080/23322039.2023.2266659 Page 14 of 36 Guide and Ketokivi (2015) propose that it is imperative for researchers to establish causality prior to undertaking the study. Reverse causality occurs when the anticipated cause-and-effect relationship is contrary to the initial hypothesis. The concept of reverse causality holds significant relevance in the field of structural equation modeling. In order to address this concern, Kock (2022) recommends employing the Nonlinear Bivariate Causality Direction Ratio (NLBCDR) approach as a means to ascertain causality. According to Kock (2022), it is recommended that the ratio be equal to or greater than 0.7. The researchers tested the Nonlinear bivariate causality direction ratio (NLBCDR) and found that the NLBCDR value was 0.80, indicating that reverse causality was not an issue. Moreover, the model’s R 2 value of 56.4% indicates a strong relationship between the variables. Additionally, the Tenenhaus Goodness-of-Fit (GoF) measure is 0.382, the Simpson’s paradox ratio and the Statistical suppression ratio are both 1.000, indicating that there is no evidence of causality being a concern in this study. The assessment of convergent and discriminant validity in PLS-SEM is paramount as it guarantees the accurate representation of the latent constructs by the measurement instruments and facilitates their differentiation from each other. Convergent validity is “the degree to which two measures of the same concept are correlated” (Hair et al., 2019, p 162). Convergent validity is the evaluation of the degree to which the items intended to measure a particular concept exhibit strong intercorrelations. Convergent validity is assessed using average variance extracted (AVE), the mean of all the squared loadings associated with a construct (Hair et al., 2019). A higher average extracted variance (AVE) value indicates more convergent validity, suggesting that the indicators collectively demonstrate an excellent representation of the underlying construct. Table 3 shows that AVE is above 0.5 for all constructs, establishing convergent validity (Fornell & Larcker, 1981). Discriminant validity evaluates the “degree to which two conceptually similar concepts are distinct” (Hair et al., 2019, p162). Discriminant validity evaluates whether the magnitude of the correlation between indicators within a specified construct surpasses the correlation observed among indicators from different constructs. The Fornell-Larcker criterion has conventionally been employed for establishing discriminant validity. However, contemporary advancements in PLS-SEM methodology advocate for the evaluation of discriminant validity by utilising the bootstrap HTMT (hetereotrait-monotrait ratio) table (Hair et al., 2019; Henseler et al., 2015). HTMT is the “mean value of the indicator correlations across constructs relative to the mean of the average correlations of indicators measuring the same construct” (Hair et al., 2019, p 776). According to the findings presented in Table 4, it can be observed that the bootstrapped HTMT values, which are below the threshold of 0.9, provide evidence in support of discriminant validity (Henseler et al., 2015). The assessment of the measurement model provides confirmation that the constructs exhibit internal consistency and possess adequate discriminant validity, thereby establishing the foundation for the subsequent analysis of the structural model. 4.1.2. Structural Model Evaluation The partial least squares approach (PLS-SEM) is a variance-based technique that allows the researcher to study the complex relationship between multiple variables simultaneously (Chin, 1998; Hair et al., 2019). The standardized root mean square residual (SRMR) is a statistical measure used to assess the goodness-of-fit of a structural model (Hu & Bentler, 1999). It quantifies the disparity between the observed covariance matrix and the covariance matrix predicted by the model. A smaller standardized root mean square residual (SRMR) score suggests a stronger alignment between the model and the observed data. The estimated model had an SRMR value of 0.043, which is lower than the SRMR of 0.08 proposed by Hu and Bentler (1999). Multicollinearity manifests itself as the existence of strong correlations between two or more predictor variables, leading to potential difficulties in precisely estimating and analyzing relationships within the model. The Variance Inflation Factor (VIF) is a statistical metric employed for assessing the existence of multicollinearity among predictor variables within a specified model. As the VIF Reddy & Thanigan, Cogent Economics & Finance (2023), 11: 2266659 https://doi.org/10.1080/23322039.2023.2266659 Page 15 of 36 Table 2. Factor loadings and reliability assessment Construct Measurement Items Factor Loading Product Complexity (Heitmann et al., 2007) (Composite reliability 0.93, Cronbach’s alpha 0.86) The bank offerings in home loans were difficult to understand (Fixed, Floating Rate, Schemes, MCLR) 0.94 The number of product features in Home Loans was overwhelming. (Interest rate, Processing fees, legal, technical, down payment) 0.93 Perceived Risk (DelVecchio & Smith, 2005) (Composite reliability 0.92, Cronbach’s alpha 0.89) Considering the investment involved, purchasing a Home loan is risky. 0.84 Given the financial expenses (Interest, EMI, Fees) associated with purchasing a Home loan, there is substantial financial risk. 0.83 I worry about the cost of purchasing a Home loan 0.87 Given the financial commitment, I may regret purchasing a Home loan 0.82 I could lose a significant amount of money if I ended up with a Home loan that did not work. (Delayed payments, poor service) 0.82 Involvement in purchase decision (Kim & Sung, 2009) (Composite reliability 0.88, Cronbach’s alpha 0.81) I cared a lot about selecting the Bank from many other choices available in the market 0.83 It was very important for me to make the right choice of Bank from the market 0.88 When selecting the Home loan bank or finance company, I was very concerned about the outcome of my choice 0.83 Perceived Search benefits (Srinivasan & Ratchford, 1991) (Composite reliability 0.89, Cronbach’s alpha 0.84) I learned which Home loans are suitable for me by shopping around 0.84 Shopping around at various banks and Housing finance companies helped me to find the lowest price (interest rate, EMI) when I bought my Home loan 0.8 I got exactly what I wanted by searching enough before I bought my Home Loan 0.74 By searching for more information about home loans, I am certain of making the best buy. 0.88 Number of internet sources used (Dawes et al., 2009), Single item How many internet sites did you enquire from when you were shopping for the home loan? 1 (Continued) Reddy & Thanigan, Cogent Economics & Finance (2023), 11: 2266659 https://doi.org/10.1080/23322039.2023.2266659 Page 16 of 36 (Variation Inflation Factor) in the present study is below 3.0, there is no multicollinearity (Hair et al., 2019). The model R 2 of 56.4% demonstrates a significant effect (Hair et al., 2019). Bootstrapping is a resampling approach that is utilized within the context of Partial Least Squares Structural Equation Modeling (PLS-SEM). Its primary objective is to evaluate the reliability and statistical significance of multiple model parameters. The parameters encompass path coefficients, loadings, and correlations pertaining to latent variables. In the current investigation, the technique of bootstrapping was utilized to evaluate the statistical significance of the path coefficients within the structural model (Hair et al., 2019; Henseler, 2020). Table 5 indicates the bootstrapped path coefficients. Six of the ten proposed hypotheses were accepted. The findings from the PLS-SEM analysis, as presented in Table 5, provide evidence in favor of the relationships proposed in six hypotheses, while four hypotheses were not supported. The perceived Construct Measurement Items Factor Loading Knowledge (Laroche et al., 2005) (Composite reliability 0.92, Cronbach’s alpha 0.9) My knowledge of Home loans is better when compared with my friends and acquaintances, 0.79 In general, my knowledge of Home loans is good 0.85 Compared with experts in Home loans, my knowledge of Home loans is better 0.8 The information search I have performed on Home loans is very thorough 0.78 I consider myself knowledgeable about Home loans 0.84 I don’t have much experience in purchasing Home loans. (Reverse scaled) 0.81 Mortgage process satisfaction (Critchfield et al., 2019) (Composite reliability 0.92, Cronbach’s alpha 0.9) Overall, How satisfied are you with the Home Loan Bank/HFC you used 0.85 Overall, How satisfied are you with the Application process 0.84 Overall, How satisfied are you with the Documentation process required for the loan 0.83 Overall, How satisfied are you with the Timeliness of the service 0.82 Overall, How satisfied are you with the Loan Disbursal process 0.85 Purchase intention (D’Souza et al., 2021) (Composite reliability 0.92, Cronbach’s alpha 0.89) I think the Home loan is worth buying from the Bank I chose 0.84 I am willing to buy the Home Loan from the same Bank again 0.89 I intend to buy a Home loan again from the same Bank 0.88 I will always buy home loans from the same Bank 0.86 If I am going to buy a Home Loan I would consider buying from the same Bank 0.79 I would definitely purchase the Home Loan from the same Bank 0.81 Reddy & Thanigan, Cogent Economics & Finance (2023), 11: 2266659 https://doi.org/10.1080/23322039.2023.2266659 Page 17 of 36 risk is considerably influenced by the complexity of the product, as indicated by H1 (β = 0.511). The hypothesis H2a did not receive support in the study, however H2b (β = 0.126) received support. The influence of perceived risk on the probability of utilizing internet sources was shown to be significant. The hypothesis H3, which posited a positive relationship between perceived risk and involvement, did not receive empirical support. The fourth hypothesis (H4), which posits that an increase in perceived search benefits leads to a usage of higher number of Internet sources, was not substantiated by the findings of this study. The study investigated the impact of information search on knowledge through the examination of Hypotheses H5a and H5b. The results indicate that H5a is supported (β = 0.126), but H5b is not supported. The hypothesis H6, which suggests that more consumer involvement has a favorable impact on mortgage knowledge, is supported (β = 0.559). The study supports H7, which proposed that an increase in customer knowledge positively impacts customer satisfaction (β = 0.105). Furthermore, H8, which proposed that greater levels of customer satisfaction lead to an increase in purchase intention, is also supported (β = 0.753). Subsequently, the researchers conducted a mediation analysis in order to elucidate the underlying mechanisms responsible for the reduction of perceived risk. The mediation test is employed to assess if the mediator modifies the effect of the constructs that are posited to mediate. The authors use the bootstrapping procedure of PLS-SEM, which gives more accurate results than PROCESS (Hair et al., 2021; Nitzl et al., 2016; Sarstedt et al., 2020). Table 6 presents the results indicating that two of the three mediating relationships are statistically significant. Specifically, the findings demonstrate that involvement (H9a) and perceived search benefits (H9b) act as parallel mediators, fully mediating the relationship between perceived risk and consumer knowledge (Hair et al., 2021; Sarstedt et al., 2020). 5. Discussion of Results The results show that increasing consumer knowledge, involvement, and information search while reducing perceived risk increases consumer purchase intent and satisfaction with mortgages. The findings echo previous work focused on financial literacy (Xiao & Porto, 2017), providing trusted advice (Argento et al., 2019) and trusted websites (Damen & Buyst, 2017; Nicholson et al., 2019), thereby contributing to the theories of uncertainty reduction and consumer empowerment. This study examined two related research objectives: 1) the antecedents of customer satisfaction and purchase intent in mortgages and 2) the mechanism of perceived risk reduction. To address the objectives, a conceptual model based on the uncertainty reduction theory and customer empowerment theory was developed (Hu & Krishen, 2019; Santos & Gonçalves, 2019). A detailed evaluation of the results follows. The first hypothesis (H1), proposing that higher product complexity positively affects perceived risk, is supported. This result is consistent with prior studies (Linciano et al., 2018; Perry & Lee, 2012; Van Raaij, 2016) highlighting customer confusion and cognitive challenges when buying mortgages. The large effect size reiterates that mortgage buyers need financial education or counseling to lower risk perception (Argento et al., 2019; Xiao & Porto, 2017). This result supports the view that customers are overwhelmed by the complex attributes to consider when purchasing mortgages. Customers may be confused and unable to evaluate options clearly, leading them to perceive high levels of risk. The second hypothesis (H2a and H2b) proposed that perceived risk positively affects perceived search benefits and internet sources. H2a is not supported, and customers with high perceived risk appear to emphasize seeking mortgage information negatively. Jacoby et al. (1974) emphasized that in complicated product markets like mortgages, new customers are reluctant to invest time or cognitive effort in gathering information, and the results confirm this view. The results support H2b that customers with a high perceived risk visit more websites (Hu & Krishen, 2019; Soto-Acosta et al., 2014). There is an inconsistency in customer search behaviorhigher objective search behavior and less subjective search intentions. This may be attributed to the “Google effect,” Reddy & Thanigan, Cogent Economics & Finance (2023), 11: 2266659 https://doi.org/10.1080/23322039.2023.2266659 Page 18 of 36 Table 3. Convergent validityFornell Larcker Table Construct Perceived Risk Involvement Knowledge Product Complexity Perceived Search Benefits Internet Sources Satisfaction Purchase intention Perceived Risk 0.7 Involvement 0.01 0.72 Knowledge 0.02 0.32 0.66 Product Complexity 0.26 6.19e-05 0.01 0.88 Perceived Search Benefits 0.03 0.0 0.02 0.05 0.66 Internet Sources 0.01 4.95e-06 0.01 0.01 0.0 Satisfaction 0.01 0.0 0.01 0.0 0.0 9.08e-05 0.71 Purchase intention 0.0 0.0 0.01 0.0 0.02 0.0 0.55 0.65 Squared correlations; AVE in the diagonal. Reddy & Thanigan, Cogent Economics & Finance (2023), 11: 2266659 https://doi.org/10.1080/23322039.2023.2266659 Page 19 of 36 Table 4. Discriminant validity—bootstrapped HTMT values Construct Perceived Risk Involvement Knowledge Product Complexity Perceived Search Benefits Internet Sources Satisfaction Purchase intention Perceived Risk Involvement 0.22 Knowledge 0.23 0.75 Product Complexity 0.65 0.11 0.21 Perceived Search Benefits 0.28 0.12 0.26 0.35 Internet Sources 0.21 0.08 0.15 0.15 0.09 Satisfaction 0.14 0.16 0.2 0.14 0.18 0.08 Purchase intention 0.12 0.15 0.2 0.12 0.26 0.11 0.88 95% bootstrap quantiles. Reddy & Thanigan, Cogent Economics & Finance (2023), 11: 2266659 https://doi.org/10.1080/23322039.2023.2266659 Page 20 of 36 when users minimize their effort to enhance internal financial knowledge because of the presence of external information (Ward et al., 2022). The third hypothesis (H3) proposes that higher perceived risk positively influences involvement and is not supported. This finding contradicts certain previous studies (Dholakia, 1997; Michaelidou & Dibb, 2008; Seabra et al., 2014) but aligns with Bettman et al. (2008), who believe first-time purchasers underestimate risk. The underestimation diminishes involvement in the purchase decision. Low housing and mortgage insurance penetration (1%) in India shows customers’ disregard for risk (Khanna, 2017; Tiwari, 2001). Instead of managing risk cognitively, consistent with Bauer (2001), customers may emotionally manage perceived risk by making hasty choices to escape the traumatic experience (Bauer, 2001; Bettman et al., 2008; Conchar et al., 2004). Consistent with prospect theory, research by Markett et al. (2016) and Mellers et al. (2021) has shown that loss aversion can cause customers to experience emotional pain, and the fear of the consequences of a poorly purchased mortgage can further increase consumer anxiety. McKinsey & Company finds that mortgage customers rate reassurance as the most important factor in the mortgage buying process (Bhattacharya et al., 2021). The fourth hypothesis (H4) proposes that higher perceived search behavior positively influences the number of Internet sources used. The hypothesis is rejected, and there is no relationship as proposed in existing literature (Srinivasan & Ratchford, 1991). The results support the view that customers interested in searching for mortgage information seek sources other than the Internet. Prior studies highlight the importance of gathering mortgage information from professionals—real estate agents, bank employees (Devlin, 2002), family, and friends (Lee & Hogarth, 2000). The complex nature of mortgages forces customers to seek trustworthy or professional information sources. The personalized nature of interactions with experts or trusted family and friends may play a more significant role in enhancing mortgage knowledge than the impersonal Internet. Deloitte (2016) emphasizes how customers use mortgage agents as “navigators” through the mortgage buying process. The fifth hypothesis (H5a and H5b) proposed that higher perceived search benefits and the number of internet sources positively influence customer knowledge. While perceived search benefits significantly influence knowledge, increasing internet source usage does not result in increased knowledge. These findings support the view that information, instead of empowering customers, must be overloading them—exposure to internet sources did not convert into adequate knowledge. Customers’ knowledge seems to expand as they search from multiple sourcesbank, real estate, and family sources. This result fits into the quantity-quality debate of consumer information (Fürstenau et al., 2016; Yoo et al., 2019), where the quality of the information is superior to the quantity/number of sources consulted. This finding shows that banks and regulators should prioritize providing customers with quality information over quantity (Nicholson et al., 2019). There continues to exist a trust gap between customers and financial service providers (Sapienza & Zingales, 2021). In this context, mortgage providers can increase trust and confidence by providing quality information to reduce customers’ perceived risk. Further, well-informed customers would make on-time payments and pose fewer compliance risks for banks. The sixth hypothesis (H6), proposing that higher involvement positively influences customer mortgage knowledge, is supported. This result confirms that involved customers are more knowledgeable (Celsi & Olson, 1988). This is due to increased elaboration because of the exertion of cognitive resources and willingness to attend to market information (Celsi & Olson, 1988; Petty & Cacioppo, 1986). As predicted by uncertainty reduction theory, higher involvement leads to higher customer knowledge. Highly involved customers pay focused attention to the information presented, thereby enhancing their knowledge of mortgages (Amarasinghe Arachchige et al., 2022). Further, by interacting closely with mortgage personnel, involved customers can gather more details about the features and process of mortgage loans than less involved customers. The seventh hypothesis (H7) proposes that higher customer knowledge positively influences customer satisfaction and is supported (β = 0.105). These results suggest customer satisfaction Reddy & Thanigan, Cogent Economics & Finance (2023), 11: 2266659 https://doi.org/10.1080/23322039.2023.2266659 Page 21 of 36 Table 5. Hypothesis testing S No Hypothesis Path Path Coefficients Standard error t-value p-value Results 1 H1 Complexity -> Perceived Risk 0.51 0.51 0.04 13.77 0 Supported 2 H2a Perceived Risk -> Perceived Search Benefits −0.17 −0.17 0.05 −3.21 0.0 Not supported 3 H2b Perceived Risk -> Internet Sources 0.12 0.13 0.05 2.45 0.01 Supported 4 H3 Perceived Risk -> Involvement −0.11 −0.11 0.05 −2.14 0.02 Not supported 5 H4 Perceived Search Benefits -> Internet Sources 0.04 0.04 0.04 1.04 0.15 Not supported 6 H5a Perceived Search Benefits -> Knowledge 0.13 0.13 0.05 2.81 0.0 Supported 7 H5b Internet Sources -> Knowledge −0.08 −0.08 0.04 −2.36 0.01 Not Supported 8 H6 Involvement -> Knowledge 0.56 0.56 0.06 10.06 0 Supported 9 H7 Knowledge -> Satisfaction 0.1 0.1 0.05 2.18 0.01 Supported 10 H8 Satisfaction -> Purchase Intention 0.75 0.75 0.03 22.12 0 Supported Reddy & Thanigan, Cogent Economics & Finance (2023), 11: 2266659 https://doi.org/10.1080/23322039.2023.2266659 Page 22 of 36 results from mortgage knowledge, consistent with recent studies (Reddy & Thanigan, 2022). Customers with a more robust knowledge of mortgages may be able to negotiate favorable terms with mortgage providers while understanding the implications of those terms on their welfare. Contrarily, customers with less knowledge may be more vulnerable to predatory lending practices or may make decisions that are not in their best interest (Agarwal et al., 2014). Overall, having a good knowledge of mortgages would help customers to make better choices and to be more satisfied with the service they receive from their mortgage provider. The eighth hypothesis (H8) proposes that higher customer satisfaction positively influences purchase intention and is supported (β = 0.753). These results align with recent studies on home buying satisfaction and purchase intention (Dash et al., 2021). When customers have a satisfying buying experience when purchasing a mortgage, they believe it is because of the mortgage provider’s competence and brand reputation. This leads customers to trust the brand and develop loyalty (Bhattacharya et al., 2021). The authors examined the mediating relationships to gain insight into the underlying risk reduction mechanism. The authors find that perceived search benefits and involvement competitively mediate perceived risk. This suggests that higher perceived search benefits and involvement suppress perceived risk (Zhao et al., 2010). So, involvement and perceived search benefits are the parallel mediators of perceived risk and knowledge. The outcome of the parallel mediation test foregrounds the various approaches to help households reduce their perceived risk. These findings suggest that perceived risk reduces when customers are involved in the purchase decision. Mortgage counseling is an excellent approach to increasing purchase decision involvement (Argento et al., 2019). Also, increasing customers’ perception of search benefits can reduce customer perceived risk. This is consistent with Singh and Jang (2022), who show that each information channel has associated search benefits that enhance customer purchase intention and satisfaction. This is consistent with the conceptualizations of both the uncertainty reduction theory and customer empowerment theory, which offer complementary and parallel theoretical explanations of the process of risk reduction by customers. 6. Contributions, Limitations, and Conclusion 6.1. Theoretical contributions Using the theoretical lens of consumer empowerment (Hu & Krishen, 2019) and uncertainty reduction (Knobloch, 2015; Kramer, 1999; Santos & Gonçalves, 2019), the present study examines the mechanism of consumer risk reduction and satisfaction enhancement during mortgage buying. The study makes three contributions to the literature. First, by examining the parallel mediating effects of involvement, perceived search benefits, and the number of internet sources, this study integrates the information search literature presented in customer empowerment theory and uncertainty reduction theory (Hu & Krishen, 2019; Santos & Gonçalves, 2019). While Hu and Krishen (2019) employed involvement as the only mediator, the current work investigates three mediators. By assessing the effect of three mediating variables in parallel, this study provides a robust profile of the underlying mechanisms of risk reductions in mortgage purchases. Second, it enumerates the role of knowledge in increasing satisfaction and purchase intent and is consistent with recent studies highlighting the importance of assessing consumer satisfaction (Dash et al., 2021; Hu & Krishen, 2019; Reddy & Thanigan, 2022; Xiao & Porto, 2017). Third, the antecedents of consumer knowledge, including the influence of customer involvement and search, are presented (Celsi & Olson, 1988; Damen & Buyst, 2017; Petty & Cacioppo, 1986; Woodward & Hall, 2012). 6.2. Managerial and policy implications This study highlights the critical components of reducing perceived risk and increasing purchase intent from an applied perspective. Most mortgage company managers want to boost customer satisfaction and purchase intent. The results imply that customers who decide based on adequate information and knowledge are more inclined to have a strong and satisfying relationship with the lender and are less likely to refinance with a competitor (Reddy & Thanigan, 2022). The findings Reddy & Thanigan, Cogent Economics & Finance (2023), 11: 2266659 https://doi.org/10.1080/23322039.2023.2266659 Page 23 of 36 Kock, N. (2022). Using causality assessment indices in PLS-SEM. Data Analysis Perspectives Journal, 3(5), 1–6. Kramer, M. W. (1999). Motivation to reduce uncertainty: A reconceptualization of uncertainty reduction theory. Management Communication Quarterly, 13 (2), 305–316. https://doi.org/10.1177/ 0893318999132007 Lacko, J. M., & Pappalardo, J. K. (2010). The failure and promise of mandated consumer mortgage disclosures: Evidence from qualitative interviews and a controlled experiment with mortgage borrowers. American Economic Review, 100(2), 516–521. https:// doi.org/10.1257/aer.100.2.516 Laffey, D., & Gandy, A. (2009). Comparison websites in U. K. retail financial services. Journal of Financial Services Marketing, 14(2), 173–186. https://doi.org/10. 1057/fsm.2009.15 Laroche, M., Yang, Z., McDougall, G. H. G., & Bergeron, J. (2005). Internet versus bricks-and-mortar retailers: An investigation into intangibility and its consequences. Journal of Retailing, 81(4), 251–267. https://doi.org/10.1016/j.jretai.2004.11.002 Laurent, G., & Kapferer, J.-N. (1985). Measuring consumer involvement profiles. Journal of Marketing Research, 22(1), 41. https://doi.org/10.2307/3151549 LeDoux, J. E. (1994). Emotion, memory and the brain. Scientific American, 270(6), 50–57. https://doi.org/10. 1038/scientificamerican0694-50 Lee, J., & Hogarth, J. M. (2000). Consumer information search for home mortgages: Who, what, how much, and what else? Financial Services Review, 9(3), 277–293. https:// doi.org/10.1016/S1057-0810(01)00071-3 Linciano, N., Lucarelli, C., Gentile, M., & Soccorso, P. (2018). How financial information disclosure affects risk perception. Evidence from Italian investors’ behaviour. European Journal of Finance, 24(15), 1311–1332. https://doi.org/10.1080/1351847x.2017. 1414069 Lin, Y., Zhou, X., & Fan, W. (2021). How do customers respond to robotic service? A scenario-based study from the perspective of uncertainty reduction theory. Proceedings of the 54th Hawaii International Conference on System Sciences. http://hdl.handle.net/ 10125/70681 Li, Z., Sha, Y., Song, X., Yang, K., Zhao, K., Jiang, Z., & Zhang, Q. (2020). Impact of risk perception on customer purchase behavior: A meta-analysis. Journal of Business & Industrial Marketing, 35(1), 76–96. https:// doi.org/10.1108/Jbim-12-2018-0381 Liu, S., & Su, Y. (2021). The impact of the COVID-19 pandemic on the demand for density: Evidence from the U.S. housing market. Economics Letters, 207, 110010. https://doi.org/10.1016/j.econlet.2021.110010 Llewellyn, N., Price, L. L., & Arsel, Z. (2021). The embodiment of consumer knowledge. Journal of Consumer Research, 48(2), 212–234. https://doi.org/10.1093/jcr/ ucab003 Loibl, C., Haurin, D. R., Brown, J. K., & Moulton, S. (2020). The relationship between reverse mortgage borrowing, domain and life satisfaction. The Journals of Gerontology: Series B, 75(4), 869–878. https://doi.org/ 10.1093/geronb/gby096 Lu, B., & Chen, Z. (2021). Live streaming commerce and consumers’ purchase intention: An uncertainty reduction perspective. Information & Management, 58(7), 103509. https://doi.org/10.1016/j.im.2021.103509 Malliaris, S., Rettl, D. A., & Singh, R. (2022). Is competition a cure for confusion? Evidence from the residential mortgage market. Real Estate Economics, 50(1), 206–246. https://doi.org/10.1111/1540-6229.12340 Marafon, D. L., Basso, K., Espartel, L. B., de Barcellos, M. D., & Rech, E. (2018). Perceived risk and intention to use internet banking: The effects of self-confidence and risk acceptance. International Journal of Bank Marketing, 36(2), 277–289. https://doi.org/10.1108/ ijbm-11-2016-0166 Markett, S., Heeren, G., Montag, C., Weber, B., & Reuter, M. (2016). Loss aversion is associated with bilateral insula volume. A voxel based morphometry study. Neuroscience letters, 619, 172–176. https://doi.org/ 10.1016/j.neulet.2016.03.029 Mellers, B. A., Yin, S., & Berman, J. Z. (2021). Reconciling loss aversion and gain seeking in judged emotions. Current Directions in Psychological Science, 30(2), 95– 102. https://doi.org/10.1177/0963721421992043 Mesly, O. (2021). Spinning: Zooming in an atypical consumer behavior. Journal of Macromarketing, 41(2), 232–250. https://doi.org/10.1177/ 0276146720931909 Michaelidou, N., & Dibb, S. (2008). Consumer involvement: A new perspective. The Marketing Review, 8 (1), 83–99. https://doi.org/10.1362/ 146934708X290403 Mick, D. G., Broniarczyk, S. M., & Haidt, J. (2004). Choose, choose, choose, choose, choose, choose, choose: Emerging and prospective research on the deleterious effects of living in consumer hyperchoice. Journal of Business Ethics, 52(2), 207–211. https://doi. org/10.1023/B:BUSI.0000035906.74034.d4 Mitchell, V. W. (1999). Consumer perceived risk: Conceptualisations and models. European Journal of Marketing, 33(1/2), 163–195. https://doi.org/10.1108/ 03090569910249229 Mitchell, V.-W., & McGoldrick, P. J. (1996). Consumer’s riskreduction strategies: A review and synthesis. The International Review of Retail, Distribution & Consumer Research, 6(1), 1–33. https://doi.org/10. 1080/09593969600000001 Mitra, K. K., Reiss, M. C., & Capella, L. M. (1999). An examination of perceived risk, information search and behavioral intentions in search, experience and credence services. Journal of Services Marketing, 13 (3), 208–228. https://doi.org/10.1108/ 08876049910273763 Mulia, D., Usman, H., & Parwanto, N. B. (2020). The role of customer intimacy in increasing Islamic bank customer loyalty in using e-banking and m-banking. Journal of Islamic Marketing, 12(6), 1097–1123. https://doi.org/10.1108/jima-09-2019-0190 Musa, H., Musova, Z., Natorin, V., Lazaroiu, G., & Boďa, M. (2021). Comparison of factors influencing liquidity of European Islamic and conventional banks. Oeconomia Copernicana, 12(2), 375–398. https://doi. org/10.24136/oc.2021.013 Mützel, L. M. A., & Kilian, T. (2016). Product complexity in consumer research: Literature review and implications for future research. Rediscovering the Essentiality of Marketing, 645–661. https://doi.org/10. 1007/978-3-319-29877-1_125 Nicholson, G., Skelton, R., & Tarr, J.-A. (2019). An exploratory study of Regulatory Failure in the Australian Home mortgage market. Journal of Consumer Affairs, 53(1), 126–166. https://doi.org/10. 1111/joca.12200 Nitzl, C., Roldan, J. L., & Cepeda, G. (2016). Mediation analysis in partial least squares path modeling: Helping researchers discuss more sophisticated models. Industrial Management & Data Systems, 116 (9), 1849–1864. https://doi.org/10.1108/Imds-072015-0302 Reddy & Thanigan, Cogent Economics & Finance (2023), 11: 2266659 https://doi.org/10.1080/23322039.2023.2266659 Page 30 of 36 Oliver, R. L. (2014). Satisfaction: A behavioral perspective on the consumer: A behavioral perspective on the consumer. Routledge. Park, C. W., & Moon, B. J. (2003). The relationship between product involvement and product knowledge: Moderating roles of product type and product knowledge type. Psychology & Marketing, 20(11), 977–997. https://doi.org/10.1002/mar.10105 Pavlicek, J., & Krištoufek, L. (2019), “Modeling U. K. mortgage demand using online searches”, IES Working Paper. http://hdl.handle.net/10419/203237 Peng, M., Xu, Z., & Huang, H. (2021). How does information overload affect consumers’ online decision process? An event-related potentials study. Frontiers in Neuroscience, 15, 695852. Pernagallo, G., & Torrisi, B. (2022). A theory of information overload applied to perfectly efficient financial markets. Review of Behavioral Finance, 14(2), 223–236. https://doi.org/10.1108/RBF-07-2019-0088 Perry, V. G., & Lee, J. D. (2012). Shopping for a home vs. A loan: The role of cognitive resource depletion. International Journal of Consumer Studies, 36(5), 580–587. https://doi.org/10.1111/j.1470-6431.2012. 01124.x Peterson, R. A., & Kim, Y. (2013). On the relationship between coefficient alpha and composite reliability. Journal of Applied Psychology, 98(1), 194. https://doi. org/10.1037/a0030767 Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion communication and persuasion. Springer New York. https://doi.org/10.1007/ 978-1-4612-4964-1_1 Phillips-Wren, G., & Adya, M. (2020). Decision making under stress: The role of information overload, time pressure, complexity, and uncertainty. Journal of Decision Systems, 29(sup1), 213–225. https://doi.org/ 10.1080/12460125.2020.1768680 Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/ 0021-9010.88.5.879 Pop, R.-A., Săplăcan, Z., Dabija, D.-C., & Alt, M.-A. (2022). The impact of social media influencers on travel decisions: The role of trust in consumer decision journey. Current Issues in Tourism, 25(5), 823–843. https://doi.org/10.1080/13683500.2021.1895729 Power, J. D. (2021). Record Mortgage Origination Volume Causes Customer Satisfaction to Erode, J.D. Power Finds. https://www.jdpower.com/business/pressreleases/2021-us-primary-mortgage-originationsatisfaction-study Power, J. D. (2022a). Mortgage Lenders Struggle to Stand Out as Demand Plummets and Rates Spike, J.D. Power Finds. https://www.jdpower.com/business/press-releases /2022-us-mortgage-origination-satisfaction-study Power, J. D. (2022b). Trust is Crucial in Determining Satisfaction with Mortgage Servicers, J.D. Power Finds. https://www.jdpower.com/business/press-releases /2022-us-mortgage-servicer-satisfaction-study Ravitch, S. M., & Riggan, M. (2016). Reason & rigor: How conceptual frameworks guide research. Sage Publications. Reddy, N. S., & Thanigan, J. (2022). Antecedents of customer mortgage shopping satisfaction: The mediating role of search intensity, evidence from the NSMO survey. International Journal of Housing Markets and Analysis. https://doi.org/10.1108/IJHMA-06-2022-0094 Santos, S., & Gonçalves, H. M. (2019). Multichannel consumer behaviors in the mobile environment: Using fsQCA and discriminant analysis to understand webrooming motivations. Journal of Business Research, 101, 757–766. https://doi.org/10.1016/j.jbusres.2018.12.069 Sapienza, & Zingales. (2021). Chicago Booth/Kellogg School Financial Trust Index reveals highest level of anger about the economy since 2013. The University of Chicago Booth School of Business and Kellogg School of Management. Sarstedt, M., Hair, J. F., Jr., Nitzl, C., Ringle, C. M., & Howard, M. C. (2020). Beyond a tandem analysis of SEM and PROCESS: Use of PLS-SEM for mediation analyses! International Journal of Market Research, 62(3), 288–299. https://doi.org/10.1177/ 1470785320915686 Savino, M. (2022). Will mortgage rates jump after another fed rate hike? National Mortgage Professional. https:// nationalmortgageprofessional.com/news/will-mort gage-rates-jump-after-another-fed-rate-hike Seabra, C., Abrantes, J. L., & Kastenholz, E. (2014). The influence of terrorism risk perception on purchase involvement and safety concern of international travellers. Journal of Marketing Management, 30(9– 10), 874–903. https://doi.org/10.1080/0267257X. 2014.934904 Sharma, K., Aswal, C., & Paul, J. (2022). Factors affecting green purchase behavior: A systematic literature review. Business Strategy and the Environment, 32(4), 2078–2092. https://doi.org/10.1002/bse.3237 Silic, M., & Ruf, C. (2018). The effects of the elaboration likelihood model on initial trust formation in financial advisory services [article]. International Journal of Bank Marketing, 36(3), 572–590. https://doi.org/10. 1108/ijbm-02-2017-0038 Simon, H. A. (2000). Bounded rationality in social science: Today and tomorrow. Mind & Society, 1(1), 25–39. https://doi.org/10.1007/BF02512227 Singh, S., & Jang, S. (2022). Search, purchase, and satisfaction in a multiple-channel environment: How have mobile devices changed consumer behaviors? Journal of Retailing and Consumer Services, 65, 102200. https:// doi.org/10.1016/j.jretconser.2020.102200 Song, C., Wang, T., Lee, H., & Hu, M. Y. (2020). The moderating role of perceived social risk in bank credit card referral programs. International Journal of Bank Marketing, 38(7), 1601–1616. https://doi.org/10.1108/ IJBM-05-2020-0291 Soto-Acosta, P., Molina-Castillo, F. J., Lopez-Nicolas, C., & Colomo-Palacios, R. (2014). The effect of information overload and disorganisation on intention to purchase online: The role of perceived risk and internet experience. Online Information Review, 38(4), 543–561. https://doi.org/10.1108/OIR-01-2014-0008 Srinivasan, N., & Ratchford, B. T. (1991). An empirical test of a model of external search for automobiles. Journal of Consumer Research, 18(2), 233–242. https://doi.org/10.1086/209255 Su, D. N., Nguyen-Phuoc, D. Q., & Johnson, L. W. (2021). Effects of perceived safety, involvement and perceived service quality on loyalty intention among ride-sourcing passengers. Transportation, 48(1), 369– 393. https://doi.org/10.1007/s11116-019-10058-y Sunstein, C. R. (2013). Deciding by default. U Pa L Rev, 162, 1. Sunstein, C. R. (2017). Default rules are better than active choosing (often). Trends in Cognitive Sciences, 21(8), 600–606. https://doi.org/10.1016/j.tics.2017.05.003 Szymanski, D. M., & Henard, D. H. (2001). Customer satisfaction: A meta-analysis of the empirical evidence. Journal of the Academy of Marketing Science, 29(1), 16–35. https://doi.org/10.1177/ 0092070301291002 Reddy & Thanigan, Cogent Economics & Finance (2023), 11: 2266659 https://doi.org/10.1080/23322039.2023.2266659 Page 31 of 36 Taylor, J. W. (1974). The role of risk in consumer behavior: A comprehensive and operational theory of risk taking in consumer behavior. Journal of Marketing, 38(2), 54–60. https://doi.org/10.1177/ 002224297403800211 Thaler, R. H., & Sunstein, C. R. (2009). Nudge: Improving decisions about health, wealth, and happiness. Penguin. Tharp, D. T., Seay, M., Stueve, C., & Anderson, S. (2020). Financial satisfaction and homeownership. Journal of Family and Economic Issues, 41(2), 255–280. https:// doi.org/10.1007/s10834-019-09652-0 Timmons, S., Barjaková, M., McElvaney, T. J., & Lunn, P. D. (2022). Official advice improves mortgage-holders’ perceptions of switching: Experimental evidence. Behavioural Public Policy, 6(4), 625–653. https://doi. org/10.1017/bpp.2019.38 Tiwari, P. (2001). Home mortgage risk: A case for insurance in India. International Real Estate Review, 4(1), 57–59. https://doi.org/10.53383/100029 Torres-Moraga, E., Vásquez-Parraga, A. Z., & ZamoraGonzález, J. (2008). Customer satisfaction and loyalty: Start with the product, culminate with the brand. Journal of Consumer Marketing, 25(5), 302–313. https://doi.org/10.1108/ 07363760810890534 Tuu, H. H., Olsen, S. O., & Linh, P. T. T. (2011). The moderator effects of perceived risk, objective knowledge and certainty in the satisfaction-loyalty relationship. Journal of Consumer Marketing, 28(5), 363–375. https://doi.org/10.1108/07363761111150017 van Ooijen, R., & van Rooij, M. C. J. (2016). Mortgage risks, debt literacy and financial advice. Journal of Banking & Finance, 72, 201–217. https://doi.org/10.1016/j. jbankfin.2016.05.001 Van Raaij, W. F. (2016). Understanding consumer financial behavior: Money management in an age of financial illiteracy (1st ed.). Palgrave Macmillan. https://doi. org/10.1057/9781137544254 Vinhas, A. S., & Bowman, D. (2019). Online/Offline information search patterns and outcomes for services. Journal of Services Marketing. https://doi.org/10. 1108/jsm-07-2017-0222 Walsh, G., & Mitchell, V. W. (2010). The effect of consumer confusion proneness on word of mouth, trust, and customer satisfaction. European Journal of Marketing, 44(6), 838–859. https://doi.org/10.1108/ 03090561011032739 Wang, M., Keller, C., & Siegrist, M. (2011). The less you know, the more you are afraid of—A survey on risk perceptions of investment products. Journal of Behavioral Finance, 12(1), 9–19. https://doi.org/10. 1080/15427560.2011.548760 Ward, A., Grillo, T., & Fernbach, P. (2022). Confidence without competence: Online financial search and consumer financial decision-making. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4131784 Wolf, M., Albinsson, P. A., & Becker, C. (2015). Do-it-yourself projects as path toward female empowerment in a gendered market place. Psychology & Marketing, 32 (2), 133–143. https://doi.org/10.1002/mar.20768 Woodward, S. E., & Hall, R. E. (2012). Diagnosing consumer confusion and sub-optimal shopping effort: Theory and mortgage-market evidence. American Economic Review, 102(7), 3249–3276. https://doi.org/ 10.1257/aer.102.7.3249 Xiao, J. J., & Huang, J. (2021). Financial capability: A conceptual Review, extension, and synthesis. SSRN. https://doi.org/10.2139/ssrn.3943629 Xiao, J. J., & Porto, N. (2017). Financial education and financial satisfaction: Financial literacy, behavior, and capability as mediators. The International Journal of Bank Marketing, 35(5), 805–817. https://doi.org/10. 1108/IJBM-01-2016-0009 Yoo, B., Katsumata, S., & Ichikohji, T. (2019). The impact of customer orientation on the quantity and quality of user-generated content: A multi-country case study of mobile applications. Asia Pacific Journal of Marketing & Logistics, 31(2), 516–540. https://doi.org/ 10.1108/APJML-03-2018-0118 Zajonc, R. B., & Markus, H. (1982). Affective and cognitive factors in preferences. Journal of Consumer Research, 9(2), 123. https://doi.org/10.1086/208905 Zhang, Y.-W., Choi, J.-G., & Akhmedov, A. R. (2021). The impacts of perceived risks on information search and risk reduction strategies: A study of the hotel industry during the COVID-19 pandemic. Sustainability, 13(21), 12221. https://doi.org/10. 3390/su132112221 Zhang, Z., & Hou, Y. (2017). The effect of perceived risk on information search for innovative products and services: The moderating role of innate consumer innovativeness. Journal of Consumer Marketing, 34 (3), 241–254. https://doi.org/10.1108/JCM-04-20161768 Zhao, S., & Liu, Y. (2021). The more insufficient, the more avoidance? Cognitive and affective factors that relates to information behaviours in acute risks. Frontiers in Psychology, 12, 730068. https://doi.org/ 10.3389/fpsyg.2021.730068 Zhao, X. S., Lynch, J. G., & Chen, Q. M. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197–206. https://doi.org/10.1086/ 651257 Zuschke, N. (2020). The impact of task complexity and task motivation on in-store marketing effectiveness: An eye tracking analysis. Journal of Business Research, 116, 337–350. https://doi.org/10.1016/j. jbusres.2020.05.009 Reddy & Thanigan, Cogent Economics & Finance (2023), 11: 2266659 https://doi.org/10.1080/23322039.2023.2266659 Page 32 of 36 Appendix Table A1. Summary of related studies Theme Study Aim of paper Methodology Key Findings Contribution of our study Perceived Risk van Ooijen and van Rooij (2016) Empirical Poor understanding of risk causes households to own risky mortgage loans The present study examines complexity as an antecedent to risk and examines the mechanism of risk reduction. Fornero et al. (2011) Empirical Higher financial literacy allows the matching of household risk with product risk while also increasing risk diversification Bialowolski et al. (2022) Empirical Financially savvy customers understand riskreward mechanisms better Consumer empowerment Theory Hu and Krishen (2019) Empirical Studied the role of information overload on satisfaction and moderating role of involvement and knowledge and invites research in high-risk decisions such as mortgage This study seeks to examine the role of involvement in reducing risk. Denegri-Knott et al. (2006) Conceptual Higher consumer involvement leads to higher satisfaction when customers feel empowered (Continued) Reddy & Thanigan, Cogent Economics & Finance (2023), 11: 2266659 https://doi.org/10.1080/23322039.2023.2266659 Page 33 of 36 Table A1. (Continued) Theme Study Aim of paper Methodology Key Findings Contribution of our study Uncertainty reduction theory Flavián et al. (2016) Empirical Information obtained from the internet and offline searches reduces customer uncertainty This study demonstrates the role of information search in reducing perceived risk Santos and Gonçalves (2019) Empirical Consumers use online and offline sources for both information search as well as uncertainty reduction. Lin et al. (2021) Empirical Perceived risk mediates between customer traits and website revisit intention Information Search Dawes et al. (2009) Empirical 79% of bank customers are not actively engaged in information search This study examines the role of objective information search and perceived search benefits in risk reduction Fornero et al. (2011) Empirical Higher financial knowledge reduced delays in payments and increased search behavior. Satisfaction Reddy and Thanigan (2022) Empirical Financial satisfaction is influenced by customer’s subjective knowledge of mortgage product The present study examines knowledge as an antecedent to satisfaction and satisfaction as an antecedent to purchase intention Xiao and Porto (2017) Empirical Higher levels of financial knowledge and capabilities lead customers to higher levels of satisfaction Reddy & Thanigan, Cogent Economics & Finance (2023), 11: 2266659 https://doi.org/10.1080/23322039.2023.2266659 Page 34 of 36 Table A2. Descriptive statistics Indicator Minimum Maximum Mean Variance Perceived Risk 1 1 5 2.41 1.21 Perceived Risk 2 1 5 2.42 1.13 Perceived Risk 3 1 5 2.41 1.18 Perceived Risk 4 1 5 2.35 1.10 Perceived Risk 5 1 5 2.38 1.05 Involvement 1 1 5 3.93 0.87 Involvement 2 1 5 4.01 0.89 Involvement 3 1 5 3.97 0.83 Knowledge 1 1 5 3.83 0.87 Knowledge 2 1 5 3.94 0.86 Knowledge 3 1 5 3.90 0.93 Knowledge 4 1 5 3.91 0.95 Knowledge 5 1 5 3.93 0.86 Knowledge 6 1 5 3.80 0.90 Complexity 1 1 5 2.59 1.16 Complexity 2 1 5 2.57 1.17 Perceived Search Benefits1 1 5 3.89 0.85 Perceived Search Benefits 2 1 5 3.97 0.86 Perceived Search Benefits 3 1 5 3.79 0.82 Perceived Search Benefits 4 1 5 3.98 0.84 Internetsites 1 11 3.61 5.12 Purchase intention 1 1 5 4.01 0.91 Purchase intention 2 1 5 3.93 0.95 Purchase intention 3 1 5 3.92 0.83 Purchase intention 4 1 5 3.93 0.88 Purchase intention 5 1 5 3.92 0.86 Purchase intention 6 1 5 3.87 0.94 Satisfaction 1 1 5 3.88 0.83 Satisfaction 2 1 5 3.93 0.83 Satisfaction 3 1 5 3.92 0.87 Satisfaction 4 1 5 3.93 0.84 Satisfaction 5 1 5 3.95 0.82 Reddy & Thanigan, Cogent Economics & Finance (2023), 11: 2266659 https://doi.org/10.1080/23322039.2023.2266659 Page 35 of 36 Figure A1. Summary of the Research Methodology Reddy & Thanigan, Cogent Economics & Finance (2023), 11: 2266659 https://doi.org/10.1080/23322039.2023.2266659 Page 36 of 36