Consumer resistance and inertia of retail investors 2020 Journal of Retail

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Journal of Retailing and Consumer Services 55 (2020) 102071

Available online 19 February 2020

Consumer resistance and inertia of retail investors: Development of the resistance adoption inertia continuance (RAIC) framework

Himanshu Seth

^a

, Shalini Talwar

^b

, Anuj Bhatia

^c

, Akanksha Saxena

^d

, Amandeep Dhir

^e^,^f^,^*

aBirla Institute of Technology and Science, Pilani, Pilani Campus, Rajasthan, India

bK J Somaiya Institute of Management Studies and Research, Mumbai, India

cInstitute of Rural Management Anand, India

dICFAI Business School, Hyderabad, India

eSchool of Business and Management, Lappeenranta University of Technology, Lappeenranta, Finland

fOptentia Research Focus Area, North-West University, Vanderbijlpark, South Africa

A R T I C L E I N F O Keywords:

Behavioral finance Consumer behavior Retail investor Inertia Resistance

Systematic literature review (SLR)

A B S T R A C T

Consumer resistance and inertia related behaviors are as important as adoption behaviors. Resistance can lead to unwillingness on the part of the investors to invest in a particular financial product. On the other hand, inertia can potentially lead to loyalty, despite dissatisfaction with a financial product. Consequently, an understanding of the antecedents and outcomes of retail investors’ resistance and inertia toward investments is valuable for firms selling investment products. Although the literature on resistance and inertia is around three decades old, empirical research related to retail investment decision making has only recently gained momentum, resulting in limited but interesting findings. The current study utilizes a systematic literature review (SLR) methodology to review prior studies in this domain. The SLR presents research profiling and an extensive content analysis of the studies selected by applying a robust search protocol. The study findings highlight numerous aspects of retail investment behavior, underscore research gaps in the prior literature, and offer recommendations for future research. Furthermore, a comprehensive framework, labelled resistance adoption inertia continuance (RAIC), is proposed to investigate the behavior of retail investors. The study concludes with meaningful theoretical and practical implications that can help counter resistance and inertia toward different financial products.

1. Introduction

The recent decades have witnessed a sharp growth in the retail financial services available for retail investors. Per a recent report by the International Trade Administration, retail financial services contributed

$1.5 trillion (7.4%) toward the United States’ gross domestic product (SelectUSA, 2019). Retail investors, as opposed to wealthy individual investors or institutional investors, hold modest investment portfolios (Graham and Kumar, 2006); they have considerably lower investment acumen and receive little personalized consideration from professional advisors (Bhattacharya et al., 2012). For these reasons, they tend to doubt the loyalty and impartiality of securities professionals and securities markets (Black, 2007). Despite the availability of a large number of retail investment products (e.g., equity and debt instruments; Das et al., 2014), retail investors are not generally aware of what to choose

(Bhattacharya et al., 2012). Such lack of awareness is likely to hamper their reach toward investments. Furthermore, their investment choices are driven by numerous rational factors (Cuong and Jian, 2015) that come under the domain of traditional finance (Thakral et al., 2013).

Interestingly, the choice behavior of retail investors is not always a result of a rational evaluation (Kannadhasan, 2015). Rather, at times, their investment decisions are driven by various irrational consider- ations, leading to some biased or uncalculated financial choices (Bal- tussen and Post, 2011). Such absurd manifestations fall in the behavioral finance domain. These irrational manifestations act as barriers that impede the investors’ decision to invest. Furthermore, these barriers constitute retail consumer’s (investor’s) resistance to adoption (investment).

The dilemma in the investors’ decision, continues post investment as well, especially if the outcome of the investment deviates from the

* Corresponding author. School of Business and Management, Lappeenranta University of Technology, Lappeenranta, Finland.

E-mail addresses: [email protected] (H. Seth), [email protected] (S. Talwar), [email protected] (A. Bhatia), [email protected] (A. Saxena), [email protected] (A. Dhir).

Contents lists available at ScienceDirect

Journal of Retailing and Consumer Services

journal homepage: http://www.elsevier.com/locate/jretconser

https://doi.org/10.1016/j.jretconser.2020.102071

Received 3 August 2019; Received in revised form 23 January 2020; Accepted 6 February 2020

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investors’ expectations. This results in a sense of dissatisfaction with their investment choices as well as the higher aversion to risk when choosing future investments (e.g., Anderson et al., 2018). This dissatisfaction can result in defections or investor inertness and inactivity. On the other hand, a substantial body of research has also argued in favor of the opposite perspective, stating that investors may continue to remain loyal and persist with the said financial investment even on experiencing dissatisfaction (Ranaweera and Neely, 2003). Supporting this conten- tion, Jones et al. (2000) suggested that despite dissatisfaction, investors’

resistance and inertia are the main reasons behind loyalty and repeat purchase. This multifaced argument provides an exciting dimension of the behavioral changes that investors may exhibit upon experiencing dissatisfaction. However, this complexity does not stop here. Adding more perplexity to the situation, inertia could also be an outcome of deterrents such as interpersonal relationships, the attractiveness of alternatives, the costs of switching, and service recovery (White and Yanamandram, 2007).

The evidence becomes even more convoluted with the argument that resistance and inertia result in consumer (investor) loyalty because habitual behavior and routine strengthen their existing choices (Jones et al., 2000). However, resistance and inertia are not stable conditions because the industry structure and competition level determine the impact of change in inertia on investor retention (Ranaweera and Neely, 2003). In this context, scholars argued that these factors result in spurious loyalty because consumers’ decisions shift from having high repeated patronage buying to low repeated patronage buying mainly due to the change in attitude toward the service firm (Dick and Basu, 1994).

The prior literature has discussed the various factors that influence retail investors’ decision making and increase their resistance and inertia. Some of these factors are changing preferences (Afik and Lahav, 2015), the availability and variety of investment choices (Agnew and Szykman, 2008; Baumann et al., 2011), financial sophistication and aptitude (Byrne et al., 2010; Da Silva et al., 2018), the complexity of the financial product (Tse et al., 2016), information asymmetry (Anderson et al., 2018), and emotions (Blanchett, Finke, & Guillemette, 2018).

However, the literature is fragmented and less organized. It is quite challenging to draw inferences because of the diverse nature of the findings. Furthermore, in addition to fragmented literature, there is also a paucity of studies examining investors’ resistance and inertia directly stemming from dissatisfaction or other factors (Kim et al., 2016). All of these aspects support the need for a more in-depth inquiry into understanding retail investors’ behavior, including continued loyalty or the exhibition of inertia despite disappointment from investments.

Because scholars could find it challenging to take the existing evidence forward in its current state, the present study performs a systematic literature review (SLR) of the selected articles in the area of investors’ resistance and inertia as a way to catalyze future research. The SLR approach is effective because it enhances knowledge in the field by providing a comprehensive understanding of the literature (Aznoli and Navimipour, 2017). Furthermore, the findings of an SLR can shift the direction of future research by pointing out gaps in the literature (Corbet et al., 2019). An insight into the behavior of retail investors can help firms compete successfully for their limited investible surplus. Conse- quently, the present study’s key objective is to make use of the SLR approach for developing insights into the reasonably underexplored segment of the literature on investment behavior and to highlight the potential for future investigation.

To conduct this review, 41 relevant studies were selected by searching popular digital databases through a robust search protocol and applying prespecified quality criteria. A critical synthesis of the studies revealed specific gaps in the literature that enabled the study to make recommendations for advancing the theory and practice in this domain. The novelty of the current SLR comes from the proposed comprehensive framework, which identifies multiple barriers representing the resistance and inertia of investors. Two foundational

theories—the innovation resistance theory (IRT) by Ram and Sheth (1989) and status quo bias (SQB) by Samuelson and Zeckhauser (1988)—were utilized as the theoretical lenses for investigating investment purchase and continuation intentions. The proposed comprehensive framework addresses the gap that emerged in the selected literature, where most of the existing studies have remained limited to resistance to change, demographic profiles, psychological attributes, and behavioral characteristics (Da Silva et al., 2018; Gathergood, 2012;

Kirk et al., 2015).

2. Conceptualizing resistance and inertia

A review of the prior literature suggests that scholars have utilized multiple operational descriptions of resistance and inertia. In addition, a clear understanding of the possible similarities and differences between these two important concepts is also missing. Because of this, the current study examines the prior literature on behavioral finance, consumer behavior, and marketing to present a clear conceptualization of these terms, with an emphasis on investment decision making in the context of retail. This conceptualization is anticipated to provide more in-depth insights into the phenomena and bring out the existing ambiguities in their interpretation.

Resistance and inertia are manifestations of consumer behavior. Of the two, consumer resistance is conceptualized more widely. Notably, academics have discussed consumer resistance largely from the perspective of resistance to innovations, where some have described it as the lack of willingness to accept innovation (Tansuhaj et al., 1991). In one of the earliest elucidations, Ram and Sheth (1989) described consumer resistance as the resistance toward innovation arising from a perceived threat to the status quo. Also, IRT, as proposed by Ram and Sheth (1989), posits that functional and psychological barriers cause resistance to innovation. Building upon this seminal contribution, many prior studies have bifurcated resistance into active and passive (e.g., Heidenreich and Kraemer, 2015). Here, active resistance represents the negative attitude of consumers toward new products at the evaluation stage, while passive resistance represents a predisposition to resist a product even before evaluating it (Heidenreich and Spieth, 2013).

Furthermore, active resistance is caused by psychological and functional barriers against innovation (Heidenreich and Kraemer, 2015), while passive resistance is the tendency of resisting change and maintaining the status quo (Heidenreich and Kraemer, 2015). In the context of investments, the factors representing internal behavioral predispositions, such as risk-taking, variety seeking, and tradition, are the sources of investors’ resistance, which cause them to have low investment intentions (Baumann et al., 2011). Consequently, these factors are levers for improving the adoption of financial services or investments (Chem- ingui and Lallouna, 2013).

The conceptualization of inertia originates in the SQB theory. Inertia is the tendency of consumers to adhere to their existing habits or actions even when presented with a superior alternative (Samuelson and Zeckhauser, 1988). Other popular descriptions of inertia are as follows:

(i) maintenance of the status quo (De Guinea and Markus, 2009), (ii) associated with resistance to change (de Mesquita and Urdan, 2019) and resulting in the choice of path of least resistance (Samuelson and Zeckhauser, 1988), (iii) a driver of repeated purchases (Ranaweera and Neely, 2003) and loyalty (Wu and Lo, 2012), and (iv) spurious loyalty which is defined as the situation when a consumer purchases the same brand every time but no thought or commitment guides the purchase (Huang and Yu, 1999).

Multiple factors can propel inertia, such as uncertainty, convenience, habituated decision making, and the loss aversion (Huang and Yu, 1999;

Lee and Joshi, 2017). Furthermore, it can be bifurcated into two parts:

cognitive and affective inertia (Polites and Karahanna, 2012). Cognitive inertia represents conscious adherence to the status quo, despite knowing that it might not be the best alternative (Amoroso and Lim, 2017). In comparison, affective inertia represents adherence to the

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status quo because change is perceived as being stressful (Amoroso and Lim, 2017). The marketing literature has invoked inertia to examine intentions to continue with the same product and loyalty toward the brand (Greenfield, 2005; Polites and Karahanna, 2012). Compared with this, studies on investment behavior have utilized inertia to explain the adherence of investors to certain risk levels while investing (Auger et al., 2016). There exists strong empirical evidence of the persistence of inertia in investment decision making (Auger et al., 2016).

Although several studies have been published since the 1990s, the existing understandings are deficient in clearly explaining the concept of inertia (Khedhaouria et al., 2016). Recent literature has also argued that little is known about the possible antecedents of inertia (Gray et al., 2017). To address this, the present study provides a clear overview of the conceptualization of resistance and inertia (see Fig. 1).

Prior studies have identified resistance and inertia as the factors causing investor stickiness toward financial investment, even upon experiencing dissatisfaction (Jones et al., 2000; Ranaweera and Neely, 2003). Consequently, we argue that both resistance and inertia represent barriers to change or adherence to the status quo in the case of financial decision making. To this extent, resistance and inertia appear to be overlapping concepts. However, a more in-depth examination suggests that if similar to inertia, resistance is interpreted only as the tendency to resist change and adhere to the status quo, then it presents an incomplete picture. Such interpretation confines resistance to its passive form only. .

A complete understanding of resistance requires an inclusive description that spans various factors preventing retail investors from investing in a particular product. In comparison, inertia includes various factors that motivate retail investors to remain loyal to a particular product (Ranaweera and Neely, 2003). In light of the preceding discussion, we posit that resistance and inertia are two distinct manifestations, where resistance represents the barriers to adoption, while inertia drives loyalty and continuation intentions. This operationaliza- tion helps in capturing active resistance in the pre-investment (preadoption) stage, which can be measured through functional and psychological barriers. It also provides the basis for capturing inertia, a behavior related to the post-investment (adoption) stage and that can be

measured through affective and cognitive barriers.

3. Methodology 3.1. Research objectives

The current SLR study attempts to achieve four research objectives (ROs). These are as follows:

RO1. To undertake thorough research profiling of the selected studies in terms of descriptive statistics and study characteristics.

RO2. To distill the main perspectives examined by the selected studies and explore any additional aspects to uncover the factors related to retail investment decisions.

RO3. To assess the findings and inferences of the selected studies to provide better insights for practitioners and scholars.

RO4. To develop a comprehensive framework based on the research gaps and future scope uncovered through a critical appraisal of the selected studies.

3.2. Search protocol

The current study utilized eight different databases for choosing relevant studies: Web of Science, Scopus, Taylor & Francis, Science- Direct, Emerald, Springer, Wiley Blackwell Publishing, and EBSCO. A funneling procedure was utilized for selecting the studies to be reviewed by searching “Inertia” AND “Financial,” “Resistance” AND “Financial,”

“Inertia” AND “Retail” and “Resistance” AND “Retail” in the article title, abstract, and keywords. Fig. 2 shows the selection process followed.

Predefined inclusion and exclusion criteria were used to shortlist the relevant studies. The forward and backward citation chaining search was also used to ensure that all the relevant studies are included (see Fig. 2). The selected studies were evaluated by calculating quality scores, as suggested by Behera et al. (2019). The quality scores are essential for obtaining suitable and impartial results (Ouhbi et al., 2015). In the current study, these scores were calculated using different quality evaluation (QE) questions, as suggested by Idri et al. (2015; see Fig. 2). The quality scores generated through the QEs were used as one of

Fig. 1. Conceptualization of resistance and inertia.

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the exclusion criteria in the current SLR. The studies that obtained at least 4.5 QE scores out of 9 were selected. After the evaluation of the quality scores of each of the identified studies, 41 studies were considered for further synthesis. Independently, three authors carried out the study selection process to ensure robustness and the selection of an

unbiased sample (Mehta and Pandit, 2018).

3.3. Research profiling: Descriptive statistics

The statistics related to the year-wise and journal-wise publications, Fig. 2. Study selection process.

Fig. 3. Year-wise publications.

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international collaborations, most productive authors, and average total citations are presented as these are useful for a better understanding of the research milieu of the domain. The review suggests that the number of publications has increased in recent years (see Fig. 3). This indicates the growing significance of studying behavioral factors in retail investments. Fig. 4 shows the journal-wise publications. Out of 41 selected studies for the current SLR, eight are in The Journal of Behavioral Finance, highlighting growing importance for behavioral finance among the research fraternity. Fig. 5 presents the country collaboration of the authors of the selected studies.

3.4. Research profiling: Study characteristics

The study characteristics of the selected studies, including the geographic scope, samples used, respondent profiles, research methods, data analysis techniques, and theories are presented here. The review suggests that most of the studies are based in developed countries, representing a gap in terms of need for more research inputs from developing countries (see Fig. 6). The respondents chosen by the selected studies for data collection represent diverse groups, such as coffee farmers, business school undergraduates, and pension benefi- ciaries. Furthermore, almost all the selected studies had a balanced representation of male and female participants, implying that the prior studies have given both the genders an adequate representation (n ¼ 39;

see Fig. 7). In terms of participant distribution, the samples were mainly drawn from the general population (i.e., 88% of the selected studies), and the remaining 12% focused on student samples (see Fig. 7). The respondents’ ages varied from 18 to 67 years.

Similarly, the selected studies have utilized diverse research methods, ranging from experiments to cross-sectional surveys to literature reviews (see Fig. 8). In terms of data analysis techniques, most studies have utilized different types of regression analyses, comprising 56% of the studies selected (see Fig. 9). In terms of theoretical underpinnings, the selected studies invoked more than 20 theories. The pool of theories presents a spectrum of classical theories, with a gradual shift toward modern behavioral theories (see Table 1). This suggests the evolution and growing importance of understanding the factors related to human behavior in financial decision making.

4. Discussion

The current study utilized the content analysis technique to synthe- size, evaluate, interpret, and diagnose the selected articles (Hsieh and Shannon, 2005). The aim of content analysis was to offer an outline of

the current knowledge on retail investors’ investment behavior with respect to resistance and inertia. In the current study, three researchers performed the analysis independently and uncovered varying perspectives of financial decision making by retail investors. The emergent perspectives are presented as: (a) outcome variables that are impacted by resistance and inertia manifestations and (b) explanatory variables (antecedents) that drive the resistance and inertia of retail investors.

4.1. Outcome variables

There are several outcome variables that can be utilized as a proxy for understanding the investment behavior of retail investors. These variables are impacted by investors’ behavior driven by resistance and inertia. The varied variables utilized by prior scholars can be broadly grouped into three categories: (a) investment in contribution plans, pension plans, retirement plans, or other schemes (e.g., Byrne et al., 2010; Galliera, 2018; Tse et al., 2016). For example, Byrne et al. (2010) examined the contribution rates and investment choices of members of a large UK-based contribution plan; (b) active or passive portfolio management, investments in socially responsible portfolio and investment in equity stocks (e.g., Apostolakis et al., 2018; Kim et al., 2016; Phan et al., 2018). For example, Phan et al. (2018) examined the behavioral patterns and characteristics of investors impacting under-diversification, equity holdings, and overtrading; (c) test scores, such as cognitive reflective test (CRT) scores, risk tolerance scores, cross-sectional abso- lute deviation and risk aversion scores (e.g., Blanchett et al., 2018; Da Silva et al., 2018; Frederick, 2005). For example, Da Silva et al. (2018) calculated CRT scores to reveal that individuals with some form of debt have noticeably poorer cognitive performance when compared with individuals having no debt.

4.2. Explanatory variables (antecedents)

Each selected study investigated an interesting but diverse array of explanatory variables (antecedents) that influence retail investors’ investment decisions. The drivers of investment decision making also represent the potential sources of their resistance and inertia manifestations. In this context, the key driving factors exacerbating investors’

resistance and inertia toward financial products can be grouped under four broad categories: investor-side, product-side, firm-side, and market-side factors.

(a) Investor-side factors: The prior literature has identified several individual barriers or hindrances that impact investment decisions by enhancing resistance and inertia. For the ease of understanding, the

Fig. 4. Journal-wise publications.

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investor-side factors discussed by the prior studies can be classified into three distinct heads: psychological factors, behavioral factors, and individual characteristics. Scholars have identified varied psychological factors that affect investors’ decision making. The identified factors are largely related to risk-profile and psychological reaction to equities. The risk-profile-related factors include risk preference (Blanchett et al., 2018; Galliera, 2018), risk aversion (Anderson et al., 2018; Blanchett et al., 2018), risk attitude (Lee and Andrade, 2011), risk tolerance (Hallahan et al., 2004; Jonsson, S€oderberg, & Wilhelmsson, 2017), and time preference (Frederick, 2005). On the other hand, psychological reaction to equities includes the factors that impact equity investment decisions to an appreciable extent. Investors are reluctant to buy equity when they fear incidental loss (Lee and Andrade, 2011). They also exhibit emotional reactions such as disappointment and regret for the equities that have caused loss earlier (Strahilevitz et al., 2011). Simi- larly, some investors exhibit variety-seeking trait while others don’t (Tse et al., 2016; Wu et al., 2018). For instance, some investors tend to invest in familiar stocks (Huberman, 2001) and are resistant to change (Bau- mann et al., 2011).

Apart from the risk profile-related factors and factors driven by psychological reaction to equities, past research has also discussed other factors that represent the psychological aspects of investors. These include ego-defensiveness (Galliera, 2018), heuristics (Lam et al., 2012), experiences and emotions (Odean et al., 2011), and sentiments (Sturm, 2014). For instance, the use of financial services is triggered by the perceived enjoyment derived from using them (e.g., Chemingui and Lallouna, 2013). In addition to the psychological factors, the prior literature has extensively examined the importance of numerous behavioral factors in an individual’s investment decisions. The key behavioral manifestations discussed by prior studies are: asymmetric response (Anderson et al., 2018); cognitive reflection (Frederick, 2005);

intentions, motivation and trust (Chemingui and Lallouna, 2013); and loyalty and familiarity (e.g., Apostolakis et al., 2016).

Coming to the third group of factors representing the investor-side antecedents of investment decision making, scholars have argued that individual characteristics drive investment decision making. Individual characteristics discussed by the past studies include financial characteristics such as financial literacy (e.g., Agnew and Szykman, 2008;

Banks et al., 2015), financial aptitude (e.g., Banks et al., 2015; Gath- ergood, 2012), and financial sophistication (e.g., Dahlquist and Marti- nez, 2015; Anderson et al., 2018), all of which determine an individual’s ability to understand and process financial information while making financial decisions. For instance, financial education and literacy determine over indebtedness (Agnew and Szykman, 2008; Banks et al., 2015). Similarly, the ability to self-manage a portfolio (Kim et al., 2016) and experience in financial investments (Gaurav et al., 2011) also impact investment decisions. For example, financial knowledge and the background of investors impact asset allocation decisions and their ability to deal with information overload (e.g., Agnew and Szykman, 2008). Similarly, low financial literacy can lead to the sluggish accep- tance of new financial products (Gaurav et al., 2011).

The prior literature has also discussed individual’s characteristics in terms of his demographic profile, which includes age (e.g., Blanchett et al., 2018), gender (e.g., Frederick, 2005), income, marital status and number of dependents (e.g., Hallahan et al., 2004), and wealth (Blan- chett et al., 2018). These factors have been found to influence individual’s investment decisions. For example, age and value of equity influence the risk preferences of investors (Blanchett et al., 2018). In addition to the factors discussed above, individual characteristics also include financial and nonfinancial goals (Kirk et al., 2015; Wilcox et al., Fig. 5. The country collaboration of the authors.

Fig. 6. The geographical focus of the selected studies.

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2011), level of debt (Da Silva et al., 2018; Gathergood, 2012), and unique style of investing (Taylor & Taylor, 2016>). All of these factors have a significant impact on investors’ decision making.

(b) Product-side factors: The key product-side factors that have been discussed by the prior scholars are: complexity of the financial product, which may influence the retail investors’ intentions to invest in a particular product (Byrne et al., 2010; Tse et al., 2016); compatibility, trialability and system quality of financial service, which have a

significant effect on the adoption of the concerned financial service (Chemingui and Lallouna, 2013); and fee, where the frequency and magnitude of the account or service fees influence the investment decisions (Tse et al., 2016).

(c) Firm-side factors: The level of responsiveness of firms offering financial products is also a significant driver of investment decision making. These factors also have the potential to drive the resistance and inertia of retail investors. One of the key factors related to the responsiveness of firms is communication and the role of financial advisors (Byrne et al., 2010; Kim et al., 2016). In this context, a lack of communication (Byrne et al., 2010) can result in resistance and inertia in investment decision making by retail investors. Availability and Fig. 7. Sample distribution in terms of gender and type of participants.

Note: The first box exhibits the gender distribution of the respondents, and the other box highlights the sample profile.

Fig. 8. Research methods used in the selected studies.

Fig. 9. Methods of data analysis used in the selected studies.

Table 1

Theoretical underpinnings of the prior literature.

Theory Studies

Path of Least Resistance Agnew and Szykman (2008)

Expected Utility Theory Allen and Evans (2005); Baumann et al. (2011);

Frederick (2005) The Annuity Puzzle Anderson et al. (2018)

Theory of Planned Behavior Apostolakis et al. (2016); Apostolakis et al. (2018) Standard Economic Theory Banks et al. (2015)

Theory of Risk Tolerance Blanchett et al. (2018)

Prospect Theory Baumann et al. (2011); Murphy et al. (2016);

Talpsepp et al. (2014); Taylor & Taylor (2016);

Braga and F�avero (2017); Wu et al. (2018); Phan et al. (2018)

Theory of Decision Making Frederick (2005); Huberman (2001); Medhioub and Chaffai (2018)

Cognitive Dissonance Theory Galliera (2018) Theory of Self Control Gathergood (2012) Roger’s Theory of

Innovations Diffusion Chemingui & Lallouna (2013) Portfolio Theory Huberman (2001); Talpsepp et al. (2014) Behavioral Finance Theory Medhioub and Chaffai (2018)

Goal Theory Kirk et al. (2015)

Engagement Theory Kirk et al. (2015) Asset-Pricing Theory Lam et al. (2012) Regret Theory Strahilevitz et al. (2011) Disappointment Theory Strahilevitz et al. (2011) Classic Finance Theory Sturm (2014) Micro-Economic Theory Wilcox et al. (2011)

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variety of investment choices (Agnew and Szykman, 2008; Baumann et al., 2011) is another important firm-side factor. In this context, too few or too many options may confuse the investors, causing the manifestation of resistance and inertia. Researchers have also identified the amount of focus on the less sophisticated investors (Byrne et al., 2010) as a firm-side factor having the potential to influence investment decisions of retail investors. A limited focus on the less sophisticated investors (Byrne et al., 2010) is likely to exacerbate their resistance and inertia.

(d) Market-side factors: Market-related factors cannot be ignored when making investment decision because investments are subject to the risks posed by market conditions. Scholars have discussed varied market-related factors, such as expectations regarding future prices, perceived return, and changes in the market, all of which can enhance the manifestation of resistance and inertia (e.g., Kim et al., 2016).

Typically, the market-related factors are related to two main categories—information and uncertainty—and their influence on investors’

decision making are well documented. Information related factors include the investors’ decision making being contingent upon the type and quality of information they possess and their ability to use the information (Anderson et al., 2018). Both past and present information play a vital part in shaping the investment decision. For instance, traders (Afik and Lahav, 2015) and investors (Talpsepp et al., 2014) rely on historical information about financial assets to make an investment decision, but when the current events and prices change, investors shift their focus to current events (Afik and Lahav, 2015; Talpsepp et al., 2014). Furthermore, information asymmetry (Anderson et al., 2018) also influences investors’ decisions. In comparison, uncertainty related factors imply that the investors are disinclined to invest in uncertain markets (Taylor & Taylor, 2016). Furthermore, intentions to invest are impacted by future prices and changes in the market (Kim et al., 2016).

4.3. Key antecedents that drive resistance and inertia

The key emphasis of the present SLR is on the role of resistance and inertia in investment decision making. Although a multitude of factors can be anticipated to drive the resistance and inertia of retail investors as discussed above, a deeper look at the prior studies highlights the criti- cality of certain factors when compared with others. The current study has synthesized these significant factors to develop an overview of the key drivers of resistance and inertia of retail investors (see Fig. 10). The overview brings together four different sets of factors: (a) market-related factors: these comprise of past historical experiences, prevailing prices, current trends, and information asymmetry (Anderson et al., 2018;

Sturm, 2014, Talpsepp et al., 2014; Taylor & Taylor, 2016); (b) psychological factors: these contribute to investors’ resistance, and here, inertia includes belief, moods, emotions, and attitude (Allen and Evans, 2005; Banks et al., 2015; Huberman, 2001; Murphy et al., 2016); (c) individual factors: these include financial sophistication, self-ability to manage portfolio, self-control, risk appetite, and demographics (Afik and Lahav, 2015; Allen and Evans, 2005; Byrne et al., 2010; Gathergood, 2012) and constitute the individual factors that can aggravate resistance and inertia in retail investment decision making; and (d) product-related factors: these include service availability, quality, complexity, trialability, and frequency and the magnitude of service fees (Chemingui and Lallouna, 2013; Tse et al., 2016), all of which can magnify the resistance and inertia of retail investors.

5. Beyond the review: Directions for future research

The present SLR amalgamated and critically synthesized the findings of the selected studies on retail investors’ investment decisions, emphasizing resistance and inertia. This review resulted in the identification of different open research gaps that form the basis for the formulation of a comprehensive framework on resistance and inertia in the context of investment decision making by retail investors. The developed comprehensive framework provides the necessary theoretical foundation for future research.

5.1. Research gaps

The selected studies have covered interesting issues and aspects of resistance and inertia, revealing deep insights into the retail investors’

investment decision making. However, certain limitations and gaps persist in the accumulated findings. Our SLR suggests four different research gaps organized under four discrete heads, as discussed below.

(a) Limited geographical scope: The geographical scope of the selected studies has been limited, both in terms of the countries examined and international collaborations while undertaking the research. To elabo- rate, the geographical coverage of the selected studies is confined to only developed countries. This limited geographical coverage severely con- strains the generalization of the study findings to other contexts.

Because of this, the findings are less useful in multiple contexts and settings. Furthermore, this gap suggests a limited understanding of investment decision making by investors in developing countries and a clear lack of cross-cultural findings in this domain. Therefore, this skew in the geographical focus indicates a need for more studies based in

Fig. 10. Factors affecting resistance and inertia in investment decision making.

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countries that have remained unexplored so far (e.g., developing countries). Furthermore, it is evident that majority of the prior scholars did not seek international collaborations, which, with its global back- drop, could have enabled a broader and more universal understanding of the phenomena of resistance and inertia. This severely restricts comparative work across several countries for a better understanding of investor decision making (e.g., Banks et al., 2015; Baumann et al., 2011;

Blanchett et al., 2018).

(b) Methodological limitations: Prior literature has two major meth- odological limitations related to sampling and data collection. Most of the selected studies have utilized a small sample size to represent a comparatively larger population (e.g., Banks et al., 2015; Tse et al., 2016). Statistically, small samples are not considered true representa- tives of a larger population. Therefore, the generalizability of the findings is questionable. Large study samples create more possibilities for a new and better understanding of behavioral aspects, including resistance and inertia (Da Silva et al., 2018). Additionally, there is a lack of heterogeneity in the methods utilized for the data collection. Nearly 71% of the selected studies utilized experiments to investigate investor decision making (e.g., Wu et al., 2018; Apostolakis et al., 2018). It is argued that experimental studies may not be able to explain the actual investment decision making, wherein the complexity, stakes, and costs are higher, because scholars simplify experiments to make them more manageable (Fisch and Wilkinson-Ryan, 2013). Consequently, the differences between an experimental setting and real-life setting raise the question of the validity of the findings in a larger context and present an open gap for future researchers.

(c) A limited set of variables: Most prior studies utilized a limited set of variables for examining the individual characteristics of the investors and their decision making. This includes financial background and knowledge, perceptions, age, income level, expectations, past experiences, and risk preferences (e.g., Banks et al., 2015; Afik and Lahav, 2015; Kim et al., 2016). Thus, the selected studies are confined to a limited number of demographic, financial, and psychological factors. In addition to this, most prior studies did not consider industry-related and other barriers that may potentially be contributing to investors’ resistance and inertia (e.g., Lukas, 2019).

(d) Lack of theoretical advancement: The prior literature suggests that the prospect theory (Kahneman and Tversky, 1979) is the most utilized theory in the selected studies when it comes to understanding investment decision making (e.g., Duxbury, 2015a; Murphy et al., 2016;

Taylor & Taylor, 2016). However, the dynamic operating environment and changing investor profile are likely to result in the evolution of their preferences, thereby underscoring the need for invoking consumer behavior theories and utilizing a more integrated outlook that can better explain the complexities of the contemporary investment decision-making process.

5.2. Framework on resistance and inertia

The insights obtained through research profiling, research perspectives, and identification of the research gaps were utilized to develop a comprehensive framework for better understanding investors’ decision making regarding resistance and inertia (see Fig. 11). The proposed resistance adoption inertia continuance (RAIC) framework can provide scholars and practitioners with the necessary theoretical basis for pro- gressing the research and practice on resistance and inertia in the context of retail investors. Two foundational consumer behavior theories—the IRT (Ram and Sheth, 1989) and SQB (Samuelson and Zeck- hauser, 1988) —were used to propose the antecedents and consequents of interest. The proposed framework comprises two phases. The preadoption phase invokes the IRT to identify various barriers that could impede the investment decision of retail investors. The model posits that these barriers will have a negative association with purchase intentions.

When the firms offering the financial products succeed in lowering these barriers through multiple strategies, investors’ are likely to develop positive investment intentions. Next, the framework posits that these positive intentions can be anticipated as resulting in actual investment behavior, that is, adoption. In line with the prior studies (e.g., Wu and Lo, 2012; Mikolon et al., 2015), the RAIC framework also suggests a post-adoption phase. In this second phase, investors may experience dissatisfaction or satisfaction based on the performance of the chosen product. At this stage, the framework captures the effect of inertia, where, despite dissatisfaction, the investors exhibit continuation

Fig. 11. Proposed resistance adoption inertia continuance (RAIC) framework.

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intentions. We hypothesize this loyalty or stickiness toward the financial service and product using the SQB.

Originally, the IRT posited that there are five types of barriers for measuring resistance, here classified under two broad types: functional barriers and psychological barriers. The functional barriers further comprise usage, risk, and value barriers, and the psychological barriers comprise tradition and image barriers. Together, these barriers represent the factors that adversely affect the purchase intentions of individuals across a variety of financial products and services. Compared with this, the SQB accounts for disparate economic phenomena, such as the availability of choices, complexity level, loss aversion, convenience, and habituated decision making. These are the consequences (or outcomes) arising out of the SQB, which not only motivate the consumers to resist change but also significantly influence their decisions. The SQB manifests as cognitive and affective inertia. Cognitive inertia leads to conscious maintenance of the status quo despite the option of superior alternatives, while affective inertia causes adherence to the status quo to avoid stress (Amoroso and Lim, 2017).

To summarize, the IRT explains the preadoption phase of the proposed RAIC framework, and the SQB explains its postadoption phase, as discussed below.

5.2.1. Preadoption phase

The preadoption phase is modeled by invoking the IRT to identify various barriers that could impede the investment decision of retail

investors. The RAIC extends the IRT in the context of financial investments by introducing additional barriers relevant to the financial decision making of retail investors. The proposed new barriers are individual and industry barriers.

The RAIC framework utilizes the original functional barriers as proposed by the IRT. In line with the IRT, the functional barriers can be divided into three sub-barriers: usage, risk, and value barriers. One of the proposed new barriers, namely, individual barriers, is further sub- divided into financial, behavioral, risk profile, psychological, and social barriers. The division of the individual barriers into five sub-barriers provides clarity and enhanced understanding. The original psychological barrier proposed by the IRT is not included in RAIC because tradition and image barriers are not discussed in the selected studies. However, because the review revealed that psychological factors such as moods and sentiments are key determinants of investor behavior, we have included psychological barriers as sub-barriers constituting individual barriers. Table 2 tabulates the factors representing these barriers. In- dustry barrier is not sub-divided further because the analysis around this theme is limited in the selected studies. These three broad barriers represent product-side (functional barrier), investor-side (individual barriers), and firm-side (industry barriers) issues that can increase investor resistance toward adopting available financial products.

(a) Functional barriers: Three types of functional barriers are usage barriers, comprising prevailing market anomalies, perceived complexity of the financial products, and trialability (e.g., Lam et al., 2012; Tse

Table 2

Mapping different barriers to IRT and SQB theories.

Theory Barriers Sub-barriers Variables References

Innovation Resistance

Theory (IRT) Functional

barriers Usage barriers Prevailing market anomalies Lam et al. (2012) The perceived complexity of the financial

products Tse et al. (2016)

Trialability Chemingui and Lallouna (2013)

Risk barriers Credit availability to the consumers Wilcox et al. (2011)

Value barrier Perceived price Talpsepp et al. (2014), Ihli et al. (2018) Individual

barriers Financial barriers Financial literacy Gaurav et al. (2011), Gathergood (2012), Banks et al. (2015), Agnew & Szykman (2008)

Background Agnew & Szykman (2008)

Indebtedness Gathergood (2012), Da Silva et al. (2018)

Wealth Kim et al. (2016), Ihli et al. (2018), Hallahan et al. (2004)

Behavioural

barriers Overconfidence among the investors

regarding the investment returns Phan et al. (2018), Allen and Evans (2005) Willingness to buy or reject any investment Apostolakis et al. (2016)

Loyalty for the financial product Baumann et al. (2011) Past experiences of investment in terms of

gains or losses Allen and Evans (2005), Galliera (2018)

Spending patterns of investors Wilcox et al. (2011) Risk-profile

barriers Risk preferences Anderson et al. (2018), Galliera (2018), Gaurav et al. (2011)

Risk tolerance Hallahan et al. (2004)

Risk attitude Lee and Andrade (2011), Talpsepp et al. (2014)

Psychological

barriers Perception Afik & Lahav (2015)

Emotion Talpsepp et al. (2014), Strahilevitz et al. (2011), Lee and

Andrade (2011), Duxbury (2015b), Wilcox et al. (2011)

Mood Duxbury (2015b)

Sentiments Lam et al. (2012), Sturm (2014)

Social barriers Ethics Medhioub and Chaffai (2018)

Social projection Lee and Andrade (2011)

Morality Medhioub and Chaffai (2018)

Lack of communication Byrne et al. (2010)

Industry

barriers Role of financial advisors Kim et al. (2016)

Availability and variety of investment

choices Agnew & Szykman (2008), Baumann et al. (2011)

Inadequate or no focus on the less

sophisticated investors Byrne et al. (2010) Status Quo Bias (SQB) Cognitive

barriers The ability for processing information Allen and Evans (2005) Availability of alternatives/lack of

information Agnew & Szykman (2008)

Complexity level or uncertain returns Tse et al. (2016) Affective

barriers Loss aversion Anderson et al. (2018)

Engagement Kirk et al. (2015)

Familiarity Huberman (2001)

Psychological ownership Kirk et al. (2015)

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et al., 2016); value barriers comprising perceived price (e.g., Talpsepp et al., 2014; Ihli et al., 2018); and risk barriers, comprising credit availability to the consumers (Wilcox et al., 2011) and perceived security. Perceived security in using the financial product or making investments is proposed as another form of risk barrier, even though none of the selected studies examined it. The inclusion of perceived risk in RAIC is based on the findings of the studies on consumer resistance (e.g., Cheng et al., 2014).

(b) Individual barriers: Five types of individual barriers are financial barriers representing investors’ financial literacy, background, wealth, and indebtedness (e.g., Agnew and Szykman, 2008; Banks et al., 2015;

Da Silva et al., 2018); behavioral barriers comprising overconfidence among the investors regarding their investment returns, willingness to buy or reject any investment product, spending pattern of investors, loyalty toward a financial product, and past experiences of investment in terms of gains or losses (e.g., Phan et al., 2018; Galliera, 2018); risk profile barriers consisting of risk preferences in terms of whether the investor is a risk-seeker or risk-averse, risk tolerance level in terms of capacity to absorb losses from investments, and risk attitude toward any financial product (e.g., Anderson et al., 2018; Galliera, 2018; Talpsepp et al., 2014); psychological barriers including perception, emotion, mood, and sentiment (e.g., Afik and Lahav, 2015; Lee and Andrade, 2011; Sturm, 2014); and social barriers including ethics, morality, and social projection (e.g., Medhioub and Chaffai, 2018).

(c) Industry barriers: These barriers result in resistance to investment based on the issues related to the firms offering the financial products in the market; these include a lack of communication regarding the investment products from the seller (Byrne et al., 2010), the role of financial advisors in the investment decisions made by the individuals (Kim et al., 2016), the availability and variety of investment choices (Agnew and Szykman, 2008; Baumann et al., 2011), and inadequate or no focus on less-sophisticated investors (Byrne et al., 2010).

5.2.2. Postadoption phase

In the postadoption phase, the RAIC framework measures inertia through cognitive and affective barriers (Polites and Karahanna, 2012).

Table 2 tabulates the factors representing these barriers.

(a) Cognitive barriers: These consist of factors such as the ability to process the information regarding the investment products (Allen and Evans, 2005), availability of alternatives or limited information regarding the available choices (Agnew and Szykman, 2008), complexity of investments, and uncertainty of the return on investment (Tse et al., 2016). Investors usually prefer to spend less cognitive effort when making a decision, so they repeat past decisions that they can make quickly and easily (Auger et al., 2016).

(b) Affective barriers: These include factors such as psychological ownership for the ongoing investment (Kirk et al., 2015), engagement with the current investment plan (Kirk et al., 2015), familiarity with the investment or related products (Huberman, 2001), and loss aversion (Anderson et al., 2018). Psychological ownership prevents investors from switching to other investments because doing so would undermine their existing decisions. Furthermore, investors weigh gains substan- tially less than losses (Tversky and Kahneman, 1992). Hence, loss aversion makes them value what they own more than what they do not (Kahneman et al., 1991). This leads to unwillingness to change the current strategies and behavior (Samuelson and Zeckhauser, 1988).

5.3. Dependent variables

The RAIC framework suggests that a variety of dependent variables connect the preadoption with postadoption phases. Some of the popular choices are purchase intentions, adoption, satisfaction, and dissatisfaction. Purchase intentions represent the likelihood that a consumer will execute the purchase, and the actual execution of the purchase is adoption (Zhang et al., 2007). In the context of investments, purchase intentions imply the likelihood of an investment in a particular product,

such as insurance, and adoption implies the actual execution of the intention. Additionally, continuation intentions are the key outcome of the RAIC framework. The selected studies examined purchase intentions (e.g., Apostolakis et al., 2018; Auger et al., 2016) and adoption (investment decision; e.g., Anderson et al., 2018; Jonsson et al., 2017; Ihli et al., 2018) when looking at the investment decision making of retail investors. Similarly, the literature analyzes satisfaction and dissatisfaction in the context of the deviation of the outcome of investment from investors’ expectations (e.g., Anderson et al., 2018). Scholars have argued that investors may continue to remain loyal and persist with the said financial investment even on experiencing dissatisfaction (Rana- weera and Neely, 2003; Jones et al., 2000). This behavior represents inertia, which is a key aspect of the SQB. This persistence with an investment represents loyalty and continuation intentions (Greenfield, 2005; Polites and Karahanna, 2012).

5.4. Moderating variables

The RAIC framework provides a scope for accommodating different moderating variables, namely, demographic (e.g., marital status, age, gender, and culture) and economic (e.g., macro-economic policies, interest rates, inflation, and growth rate) factors. Many of the selected studies have discussed these factors while examining investment decision making (e.g., Tse et al., 2016; Strahilevitz et al., 2011). The inclusion of moderators makes the RAIC framework more versatile because the role of moderators in bringing forth individual differences in consumer behavior is a factor that the literature has discussed (e.g., Ye et al., 2019). Moderators can play a significant role in investment decisions because every investor has a unique style of investing (Taylor & Taylor, 2016). Furthermore, moderators can strengthen or diminish the relationship between investment decisions and their antecedents. For instance, although all individuals have a tendency to avoid risky decisions, there are differences in each individual’s extent of risk aversion (Weber et al., 2002).

6. Conclusion

The current study aimed at providing a critical, detailed, and extensive synthesis of the prior relevant literature published over the last two decades on retail investor investment decision making. The present study provides scholars with a research profile (e.g., variables, methodology, and theories), emergent research perspectives, open research gaps, and the RAIC framework as a theoretical basis for extending prior research on resistance and inertia. To achieve the objectives of the study, a total of 41 studies were critically evaluated to understand the various facets of resistance and inertia in retail investors’

decision making. However, the SLR went beyond resistance and inertia to take into account other aspects of investors’ behavior, as supported by Anderson et al. (2018). The study findings highlight the fact that market-related factors, psychological factors, individual characteristics, and product features are vital for decision making by investors. These factors serve as the basis for examining the barriers that aggravate resistance and inertia. Going beyond offering a mere narrative on the methods and findings of the selected studies in the domain, the present SLR took a critical look at the existing state of research to propose a RAIC framework that set the agenda for future research. The framework utilized two popular consumer behavior theories—the IRT and SQB. It models both the preadoption barriers and the postadoption stickiness (continuation). The study provides some key theoretical and practitioner implications, as discussed below.

6.1. Theoretical implications

The current study has five theoretical implications. First, the present study attempted to make very fragmented literature organized. Despite extensive interest in investors’ decision making by academics and

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practitioners, there is a dearth of studies examining investors’ resistance and inertia. The current study, thus, fills this fundamental gap by providing insights into this underdeveloped segment of the literature on investment decision making, underscoring the potential for future investigation.

Second, the current study presented a comprehensive overview of the pertinent literature by highlighting the research profile, elucidation and discussion of dependent variables, theoretical underpinnings, emergent perspectives, identification of open research gaps, and a detailed overview of the findings. The present study offers a strong foundational basis for future research development by assisting in deepening the understanding of the potential influence and purpose of behavioral investment, its significance to society, and its future trajectory.

Third, the findings suggest that research on retail investors’ resistance and inertia has been noticeable since 2001, yet academics have focused on this only sporadically throughout the years. However, the momentum increased in 2018. This implies that the interest of editors and publishers in the domain is rising again, and future researchers may be able to find more opportunities to publish their research in this domain.

Fourth, the proposed RAIC framework has some key theoretical implications. Future researchers can utilize this framework to examine the preadoption resistance behavior of retail investors. Researchers can do this by measuring the proposed barriers as explanatory constructs that can predict purchase intentions. Researchers can look at the postadoption continuation intentions despite dissatisfaction by measuring the factors representing inertia. Furthermore, scholars can utilize select demographic and economic factors as moderators to capture the individual differences in retail investors’ resistance and inertia behavior.

Finally, scholars can use the pre- and postadoption framework proposed by the current study to study consumer behavior in areas other than financial products. This versatility of the RAIC framework comes from the fact that it is grounded in consumer behavior theories and builds upon behavioral constructs such as satisfaction and intentions.

6.2. Practitioner implications

The current study suggests five practical implications for practitioners and policymakers. These implications can help strengthen the strategies to counterbalance the factors that produce negative influences preventing investors from making investment decisions or causing them to continue with suboptimal investments.

First, the selected studies have suggested that the behavior of retail investors changes in response to several internal and external factors (e.

g., Galliera, 2018; Phan et al., 2018), resulting in resistance and/or inertia toward a financial product. Consequently, investors would appreciate better communication by the seller, and this might affect them positively in their purchasing process. It can even act as security to overcome human behavioral biases (e.g., Banks et al., 2015). Thus, we recommend that managers and financial advisors communicate the information regarding the financial products in a way that is easily un- derstandable by all types of retail investors, educated or not.

Second, managers can focus on the real drivers of the investment process, namely, financial knowledge and sophistication. Educating investors to take optimal financial decisions is important because the financial markets and financial decisions are causal, such that individuals’ decisions may affect the market outcomes (Asgarnezhad Nouri et al., 2017; Blanchett et al., 2018). In this regard, the govern- ment, firms, or financial advisors can conduct educational and financial literacy programs explaining various investment products and financial planning.

Third, because the prices of financial products fluctuate from time to time, asset markets need regulation to prevent crashes and bubbles (Agnew and Szykman, 2008; Anderson et al., 2018; Gathergood, 2012;

Afik and Lahav, 2015; Kim et al., 2016). Such regulations can help

mitigate the risk of investing and reduce shocks to the market structure, resulting in better trade methods (Anderson et al., 2018). Thus, policymakers should strengthen and revise the regulatory framework to keep it aligned with the contemporary environment.

Fourth, to better understand investors’ behavioral patterns related to resistance and inertia and develop financial products aligned with the investment objectives of the investors, firms should acquire knowledge about their demographic profiles, in addition to sectoral learnings and heuristics models (Gaurav et al., 2011).

Finally, all investors may not feel confident or informed enough to make investment decisions. In these situations, the research findings and a collective view of scholars examining investor behavior across the globe can provide inputs for managers regarding how to engage the potential and existing investors better. The findings of future studies employing the proposed RAIC framework can offer several useful inferences for practice. To begin with, empirical knowledge on the magnitude of the contribution of each barrier to retail investors’ resistance can assist managers in identifying the reasons behind the failure of a particular investment product and to develop strategies to overcome the same from occurring again. Similarly, rich information about the source of continuation intentions in terms of satisfaction or inertia can help managers better understand investor stickiness. For example, if stickiness or loyalty is based on the SQB, the product may face exit from investors in the near future. Furthermore, the RAIC framework provides a point of reference for managers to know in advance the type of product that should be offered to the target investors and how they can design or modify each investment product to lower the barriers that negatively impact pre- and post-investment behavior. This may also help in creating a better buyer–seller relationship and increase the chances of a repeat investment.

6.3. Limitations of the current study

The current study suffers from three limitations that need attention from future researchers. First, the search keywords were selected per the related literature and definitions of resistance and inertia. Despite best efforts, the chosen keywords might not be exhaustive. In all probability, expanding the keywords to provide a more exhaustive review of the domain would be helpful. Second, although the studies were selected for the current SLR after a diligent search based on a robust protocol, it is possible that some studies published on the related theme around the same review period may have been missed; the reason behind this may be the unavailability of these studies in the selected database or the absence of search terms in the title, abstract, and keywords of the study.

Third, the scope of the current study was confined to retail investors only, which can be further expanded to include institutional investors and financial advisors. Despite these limitations, the current study makes a first attempt toward examining retail investors’ investment decision making with respect to resistance and inertia, thereby providing a platform for taking forward the research and practice related to retail investors in particular and consumer behavior in general.

Acknowledgements

We acknowledge the support received from the Academy of Finland (Decision No 292448, 334595 and 326066).

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.

org/10.1016/j.jretconser.2020.102071.