How Do Tourists Use Metaheuristics for Decision-Making Mediated by Smartphones in a Destination?

Abstract

The use of smartphones has empowered tourists to make travel decisions while at a destination. The purpose of this paper is to explore how tourists use metaheuristics to achieve a near-optimal solution for onsite decisions mediated by smartphones. An event-based narrative inquiry technique with semi-structured interviews was used to collect the data. The findings identify two types of decision contexts based on temporal and geographic distance to direct experience/consumption. The findings also reveal that tourists use serial heuristics for a near-optimal solution under different decision contexts, and this solution is achieved through three steps of a tourist decision journey including the initialization solution, acceptance and selection, and final decision. These heuristics can be consciously deployed or unconsciously triggered. The findings offer marketing managers direction regarding what to emphasize when delivering marketing stimuli in onsite destination decision-making contexts.

Keywords

smartphones metaheuristics heuristics social influence onsite decision-making

Introduction

Advances in smartphone technologies have significantly influenced tourism (H. H. Kim & Law, 2015) and transformed decision-making (Buhalis & Sinarta, 2019; Liu et al., 2022). Tourists increasingly rely on their smartphones to make onsite decisions because they provide ubiquitous access not only to the Internet but also to location-based services (Liu et al., 2022). Smartphones allow travelers to instantly search through a wealth of information and afford greater flexibility during the journey (H. H. Kim & Law, 2015). This change can help tourists make decisions based on specific contexts (Dorcic et al., 2019). Such decisions can range from a “fundamental concern with the route taken and overall itinerary, through choices of accommodation, transport, and activity, to the daily purchases made onsite” (Moore et al., 2012, p. 635). Additionally, developments in technology have resulted in a shift in the spatial and temporal dimensions of many tourist decisions (e.g., which attractions to visit, which restaurant to dine at, and where to rent a car) from the pre-trip stage to onsite decision-making (Minazzi & Mauri, 2015). Tourists can use smartphones to continuously evaluate their trip plan or alternatives and make decisions onsite at the destination (Kang et al., 2020; H. H. Kim & Law, 2015). Accordingly, they can develop a new plan and re-examine or cancel their pre-trip plans (Liu et al., 2022). These changes have led to a need for tourism providers to better understand onsite tourist decision-making to inform appropriate marketing strategies.

Further, tourists are exposed to a phygital (physical + digital)-social context where the physical world has multiple dimensions and tourists are ubiquitously connected with social actors in the mediation of smartphones (Liu et al., 2022).The physical context consists of position and relative distance (e.g., the relative distance between places) and passive context-awareness (e.g., push alerts), while the social context refers to connected social networks (e.g., local social networks, remote social networks, and social media users; Liu et al., 2022). Accordingly, tourists are ubiquitously connected and are likely to get information from various online platforms from their social networks and passive awareness contexts. The influence of social networks on individuals’ actions (Pan et al., 2021) has been broadened through the use of smartphones and this, in turn, has heightened its impact on tourists’ decision-making.

Much of the recent literature on smartphone use during travel has focused on the consequences of smartphone use (e.g., positive and negative effects on tourist experiences). Research on how tourists use smartphones to make onsite decisions remains in its infancy (Liu et al., 2022). Although recent studies (García-Milon et al., 2021; Kang et al., 2020; Liu et al., 2022; Mieli, 2022) have offered valuable insights into understanding decisions mediated by smartphones, these studies have not specifically focused on the onsite decision-making process. For example, Kang et al. (2020) uncovered demographic differences in smartphone-based information search behavior as well as identifying differences in pre-trip and onsite information search behavior. Further, Liu et al. (2022) explored the decision contexts that trigger and support the use of smartphones in flexible travel planning in a destination. Finally, Mieli (2022) found that smartphones help tourists to find information that is location-specific and personalized, allowing for an optimized and flexible information search. However, few studies have explored how tourists use smartphones to achieve an optimal solution (the best possible decision) in onsite decision making. Accordingly, tourism and local business marketers lack theoretical guidance for formulating marketing strategies targeted at onsite tourists. Thus, there is a gap in our understanding of onsite decision-making processes mediated by smartphones. It is known that the use of smartphones can lead to information overload (highlighting the various attributes of the extensive list of alternatives; Park & Jang, 2013) and choice overload (emphasizing choice size, choice complexity, and choice variety; Sthapit et al., 2019). Due to limited time and cognitive capability, it is difficult for tourists to process all of the available information or choices so they often resort to heuristic approaches (Y. Kim et al., 2019). A heuristic approach is defined as “a strategy that ignores part of the information, to make decisions more quickly, frugally, and accurately than more complex methods” (Gigerenzer & Gaissmaier, 2011, p. 454). Hence, heuristics may play a role in helping users to cope with the overwhelming amount of digital information readily available through smartphones (Wattanacharoensil & La-ornual, 2019). Extant studies have not fully explored how heuristics are used to assist onsite decision-making mediated by smartphones in a destination (Wattanacharoensil & La-ornual, 2019).

Further, although the terms heuristic and metaheuristic are sometimes used interchangeably, they have a different focus (Gandomi et al., 2013). Metaheuristics highlight an overall optimal solution while heuristics are problem specific (Hussain et al., 2019). Specific problem-dependent heuristics (Hussain et al., 2019) are not well suited to providing an overall optimal solution in the context of information and choice overload. Thus, the notion of metaheuristics, which can be described as “very good solutions [. . .] that hybridize heuristics for many real-world problems” (Hussain et al., 2019), will be employed in this paper to explore how tourists make optimal decisions mediated by smartphones in a destination. The findings have practical relevance, as tourism providers can better tailor their marketing communications if they better understand how tourists use information from different sources in this context (Gursoy, 2019).

Against this background, this study focused on exploring how tourists use metaheuristics to make onsite decisions mediated by smartphones. We aim to provide a holistic understanding of how metaheuristics are used in different types of smartphone-mediated decision contexts in a destination. Aside from making a conceptual contribution, this work also seeks to make a practical contribution by helping destinations to develop effective onsite marketing strategies based on different decision contexts.

Literature Review

Metaheuristics

A metaheuristic is defined as “an iterative generation process which guides a subordinate heuristic by combining intelligently different concepts for exploring and exploiting the search space, learning strategies are used to structure information in order to find efficiently near-optimal solutions” (Osman & Laporte, 1996, pp. 513, 514). This approach is used to provide possible solutions in a shorter amount of time or acceptable computation cost (Hansen et al., 2010; Hussain et al., 2019). A review of the literature indicates that metaheuristics have been used to understand optimization problems across the physical and social sciences (Hansen et al., 2010), particularly in the field of computer science and mathematical optimization (Hussain et al., 2019). The two essential features of an optimization solution are exploration and exploitation (Črepinšek et al., 2013; Hussain et al., 2019). Exploration is the process of visiting a broader space for diversity value, while exploitation is visiting those regions of a search through targeted approaches (Morales-Castañeda et al., 2020).

In order to generate overall optimal solutions (metaheuristics), subordinate heuristics are employed. A heuristic (mental shortcut) is defined as “a strategy that ignores part of the information, to make decisions more quickly, frugally, and/or accurately than more complex methods” (Gigerenzer & Gaissmaier, 2011, p. 454). Heuristics can improve the efficiency of evaluation or decision making under uncertain circumstances (Tversky & Kahneman, 1974). Heuristics are typically problem-dependent (Hussain et al., 2019). Accordingly, consumers make decisions that are “good enough” (satisficing decisions), rather than optimal decisions, by using problem-specific heuristics to reduce cognitive effort (Buchanan & Kock, 2001; Mittal, 2017). For example, consumers can rely on some simple and non-content related information, such as brand type, expert, and ranking information, to make a decision (Tan et al., 2021).

A heuristic can be used unconsciously or consciously (Gigerenzer & Gaissmaier, 2011). On the one hand, heuristics have been described as an “unconscious, associative and error-prone process” in the two-system model of reasoning (Gigerenzer & Gaissmaier, 2011). The dual system model proposes that there are two distinct modes of a cognitive thinking process, including system 1 (heuristic) and system 2 (systematic; Chen & Chaiken, 1999). The heuristic process is intuitive and effortless (McCabe et al., 2016). On the other hand, heuristics have also been defined as rules that inform conscious thinking (Gigerenzer & Gaissmaier, 2011). Gigerenzer and Todd (1999) proposed the adaptive toolbox as a theoretical framework consisting of building blocks and core mental capabilities. Specifically, the building blocks include search rules (“what direction the search extends in the search space, stopping rules” (“when the search is stopped”) and decision rules (“how the final decision is reached”) while the core mental capability includes “recognition memory, frequency monitoring, object tracking, and the ability to imitate” (Gigerenzer & Gaissmaier, 2011).

There are six main classes of heuristics (see Table 1 for descriptions of each), including recognition-based heuristics, representativeness heuristics, lexicographic heuristics, social influence heuristics (depending on social information), availability heuristics, and trade-off heuristics (e.g., tallying heuristics and compensatory heuristics; Gigerenzer & Gaissmaier, 2011).

Table 1.

Heuristic Classes.

Heuristic classes	Description	Source
Recognition-based heuristics	Decision-making based on recognition	Goldstein and Gigerenzer (2002)
Representativeness heuristics	People make predictions based on similarities (e.g., brand and tourist sites that they have known)	Kardes et al. (2004)
Social heuristics	Learning from others	Goldstein and Gigerenzer (2002)
Lexicographic rules (e.g., elimination-by-aspect)	Using cues to exclude alternatives or unattractive options	Kurz-Milcke and Gigerenzer (2007)
Availability heuristics	The ease with which information can be recalled and the perception of the consequences of that action	Tversky and Kahneman, (1974)
Trade-off heuristics	Compensating some aspects of an offering with other aspects	Gigerenzer and Gaissmaier (2011)

Heuristics have been widely used in research on tourist decisions; however, extant studies have mainly focused on the use of problem-specific heuristics (e.g., how to solve a problem such as destination choice or tourism product choice, or how to choose between differently rated tourism services; Wattanacharoensil & La-ornual, 2019). Additionally, previous research using heuristics has focused on decision-making at the pre-trip stage, and the application of heuristics to onsite decision-making in tourism contexts have not yet been extensively researched (Wattanacharoensil & La-ornual, 2019). In the tourism literature, metaheuristics have been used as algorithms for a tourist trip design problem (TTDP), such as designing personalized routes (Zheng et al., 2020, 2022) and travel mode (Van Middelkoop et al., 2003), and have used quantitative methods to solve optimized itinerary or hotel selection problems. It is argued that metaheuristics are used for generating near-optimal solutions by tourists in their onsite decision-making to cope with the significant amount of information available and the resulting choice overload this brings. Yet, our knowledge of metaheuristics in this specific context is in its infancy.

Social Influence

The importance of social influence in consumer decisions has been widely recognized (Hamilton et al., 2021; Salazar et al., 2013; White et al., 2019). Social influence is defined as “an action or actions that are taken by an individual not actively engaged in selling the product or service, and that impacts others’ expected utility for that product or service” (Godes et al., 2005, pp. 416, 417). This implies that consumers’ attitudes and behaviors are influenced by observing or imitating others (Godes et al., 2005).

Two crucial social factors impacting the social influence effect are embeddedness and tie strength (Aral & Walker, 2014). Embeddedness is described as “the number of friends that two individuals in a relationship share in common” (Easley & Kleinberg, 2010, p. 5). Tie strength refers to the significance or intensity of interpersonal relationships, including closeness, intimacy, support, and association (Shen et al., 2016). The strength of the tie ranges from strong (e.g., close friends) to weak (e.g., online strangers), depending on the frequency of communication, the social context of the relationship, the recency of the relationship, the overlap of common interests, and the frequency of physical interaction (Aral & Walker, 2014). Haythornthwaite (2001) proposed that people in a strong tie condition are more likely to communicate more frequently and impact communication effectiveness.

In tourism, social influence has been extensively studied in the context of word-of-mouth (Tanford & Montgomery, 2015). Online social influence (e.g., e-WOM) has a significant effect on travel product purchase (Filieri & McLeay, 2014; Tanford & Montgomery, 2015). Currently, there are two lines of research on social influence in tourism. The first research line focuses on the strength of social influence on tourism product choice. Litvin et al. (2008) proposed that source evaluation, brand familiarity, and memory greatly influence the strength of social influence. Tanford and Montgomery (2015) examined the role of majority (e.g., a large number of positive reviews) and minority influence (e.g., one or several negative reviews) in green travel product choice and found that tourists seek out more favorable reviews. Narangajavana Kaosiri et al. (2019) examined the impacts of tie strength on tourist satisfaction at the post-stage. Pan et al. (2021) found that tourists are prone to adapt to the destination image of social networks by using a sequential stated adaption choice experiment.

The second line of research has focused on factors influencing perceptions of the helpfulness and usefulness of online reviews. Ayeh et al. (2013) found that perceived expertise and trustworthiness significantly impacts the adoption of online reviews for trip planning. Filieri et al. (2018) identified several factors influencing the credibility and usefulness of online reviews, including normative cues (e.g., rankings of products/services, or customer ratings of elements of the product/service) and informational cues (e.g., reputation of reviewers, or subjective comments on the characteristics of the product/service, timelines, and information accuracy).

In summary, social networks have a significant impact on tourist decision-making. Hamilton et al. (2021) suggested that serial heuristics may be deployed to deal with information overload from others to make a satisficing decision. However, few studies have focused on how tourists deal with the overwhelming amount of information from social networks while at a destination.

Methodology

Event-Based Narrative Inquiry Technique (EBNIT)

This study adopted a qualitative approach, namely the event-based narrative inquiry technique (EBNIT; Helkkula & Minna, 2010), which refers to telling a lived experiences focused on a specific event. This approach combines narrative inquiry and critical incidents. Narrative inquiry enables narrators to convey their own stories in a relatively informal and unstructured way and allows individuals to contextualize their experience (Riessman, 2008). This technique can provide insights into how people tell a story using a temporally logical sequence that includes a beginning, middle, and end (Denzin, 1989). The critical incidents technique is used as a complementary approach, which can help “promote or detract from the adequate performance of some activity or the experience of a specific situation or event” (Butterfield et al., 2005, p. 483). This technique can help emphasize relevant issues and make the research more efficient by spending less time and effort on interviews and data analysis (Helkkula & Minna, 2010). Thus, the EBNIT approach was deployed to convey the tourists’ decision-making process by focusing on a specific decision (see Supplemental Appendix II: Detailed Methodology).

Research Design and Sample

The data were collected in Shanghai, China. According to the China Internet Network Information Centre (CNNIC, 2022), 99.7% of Chinese Internet users used their mobile phones to access the Internet in 2021. Furthermore, most digital consumers are users of smartphones (McKinsey, 2019). These statistics highlight the importance of understanding onsite decisions mediated by smartphones in China. This study was conducted in Shanghai where tourists regularly encounter high levels of information density, with a wide range of choices and vast amounts of virtual and physical information. Respondents were Chinese tourists. This is primarily because ongoing accessibility issues for international visitors mean that international social media sites (e.g., Twitter, Facebook, and Google Maps) are not available; thus international visitors are likely to follow very different decision-making processes. This is acknowledged as a potential limitation to the generalization of the results of this study.

The data were collected at several public tourist attractions, including the Shanghai Bund, Tianzi Lane, Nanjing Road, City God Temple, and Shanghai Happy Valley from March to April 2018. These locations were selected because they have a large number of visitors. The participants were interviewed in several locations, including cafes, restaurants, and hotel lobbies, in order to avoid noise and distraction. Every third person was approached and then asked the following screening question: “Are you visiting Shanghai?” and “Have you made any purchases or decisions using your smartphone since you arrived in Shanghai?” Participants who provided an affirmative response were invited to participate in an interview. These decisions included choices such as overnight accommodation, tourist attractions, transportation, tourist activities, and daily purchases (e.g., food stop and shopping stop; Lew & McKercher, 2006). The interview duration ranged between 30 and 60 minutes (see Supplemental Appendix I: Narrative interview guide).

In total, 36 participants were interviewed and their demographic and social characteristics are shown in Table 2. The characteristics of the sample was consistent with the findings of Tussyadiah (2016), which showed that users with high smartphone use tended to be younger. There are four further factors that explain why interviewees tended to be younger. First, according to the Statistical Report on Internet Development in China, Internet users in the 10- to 49-year age group accounted for 70.8% of all users (CNNIC, 2018). Second, based on responses to screening questions, many older tourists who were approached were not eligible to participate in the study because they had followed a pre-planned itinerary and did not make many onsite decisions. Third, when older tourists made changes to their itinerary, they often relied on traditional information sources (e.g., asking the local people or other tourists) rather than using their smartphones. Fourth, according to the statistics on the age distribution of domestic tourists in China in 2018 (Statista, 2022), more than 70% of domestic tourists were between 19 and 45 years old, thus the sample for this study reflects the population under study.

Table 2.

Tourists’ Social Characteristics.

No.	Gender	Age	Current location	Social class	No.	Gender	Age	Current location	Social class
#1	Female	25–34	Anhui	8\4	#19	Female	25–34	Nei Mongol	7\4
#2	Male	25–34	Jiangsu	4\3	#20	Male	25–34	Hubei	4\4
#3	Male	25–34	Taiwan	4\4	#21	Female	18–24	Hubei	6\4
#4	Female	18–24	Henan	5\5	#22	Female	18–24	Shandong	6\6
#5	Female	18–24	Henan	4\6	#23	Male	25–34	Anhui	6\6
#6	Male	18–24	Henan	7\7	#24	Female	25–34	Tianjin	6\6
#7	Male	35–44	Guangxi	6\3	#25	Female	18–24	Zhejiang	7\4
#8	Male	35–44	Shanxi	5\5	#26	Female	18–24	Heilongjiang	5\4
#9	Male	25–34	Guangdong	4\3	#27	Female	18–24	Jiangsu	8\7
#10	Female	18–24	Heilongjiang	7\7	#28	Female	25–34	Jiangsu	4\3
#11	Female	25–34	Beijing	6\7	#29	Male	18–24	Nei Mongol	5\5
#12	Female	18–24	Jiangsu	4\4	#30	Male	18–24	Nei Mongol	9\8
#13	Male	25–34	Jiangsu	6\6	#31	Female	18–24	Zhejiang	6\6
#14	Female	25–34	Guangdong	4\4	#32	Male	18–24	Jilin	8\6
#15	Male	25–34	Shandong	7\8	#33	Female	25–34	Anhui	7\6
#16	Female	18–24	Hunan	8\6	#34	Female	25–34	Beijing	8\9
#17	Male	18–24	Shanxi	5\6	#35	Male	25–34	Tianjin	5\5
#18	Female	18–24	Guangdong	6\6	#36	Female	18–24	Sichuan	6\6

Data Analysis

Each tourist narrative (story) was voice-recorded, transcribed, and saved as a separate word document by the researcher to allow for a rigorous coding and analysis process (Rubin & Rubin, 2011). A narrative analysis which can reorganize the interviewer’s story and contextualize data by highlighting the temporal sequence and narrative structure (Riessman, 2008) was performed using NVivo version 11. The researcher established a chronology to reconstruct the story with a clear beginning, middle, and end, because, according to Beritelli et al. (2019), understanding the sequence of decision-making allows researchers to learn the context of specific moments of choice (Beritelli et al., 2019). Initially, the data were open-coded. Following this, Labov’s (1972) structural “evaluation model” was employed for the narrative analysis to understand the major events in the narrative and the impacts on the individual. This model includes an abstract (what was this about?), an orientation (Who? What? When? Where?), a complication (Then what happened?), an evaluation (So what?), a result (What finally happened?), and a coda (the finished narrative). Accordingly, narrative analysis was used to reorganize and code the data.

Following this, thematic analysis was used to code the data. By combining thematic analysis, the researcher can “identify narrative components across the accounts” (Riessman, 2008, p. 91). This process included initial coding, developing basic themes, consolidating into organized themes, deriving global themes and networks, and describing, exploring, and analyzing networks (Wikan, 1984). Overall, the combination of structural analysis and thematic analysis was employed to reinforce one another (Riessman, 2008).

To ensure trustworthiness and validity; triangulation, member checking, and peer debriefing were applied (Shenton, 2004). Member checking is used to ensure the credibility of the data (Birt et al., 2016) and was employed by the researcher by restating and summarizing information and then checking the accuracy with interviewees. Peer debriefing is used to ensure the trustworthiness of qualitative analysis (Denzin & LincoIn, 1994). Before conducting interviews, the survey and interview guidance was reviewed by several peers. After the interviews, several peers engaged in data analysis to review and assess transcripts, emerging themes, and the final findings. Other Chinese scholars were co-opted to check transcriptions and thematic coding to ensure consistency in interpretation.

Findings

The data analysis showed that being overloaded with choice was common among participants. The following section will discuss the tourists’ decision contexts and the impacts of these contexts on how tourists use metaheuristics, including initialization solution, acceptance and selection strategies, and decision rules. The conceptual framework is mapped out by integrating metaheuristics and decision contexts (see Figure 1).

Figure 1.

Conceptual framework of tourists’ onsite decision mediated by smartphones through metaheuristics and decision contexts lens.

Temporal and Geographic Decision-Making Contexts

This section describes how temporal and geographic distance influences tourists’ mental representations when tourists make a decision mediated by smartphones in a destination (see Figure 2).

Figure 2.

Tourists’ decision contexts mediated by smartphones in a destination.

Firstly, the findings revealed that some participants made a decision or planned for activities which were temporally and geographically distant from the direct experience. In situ, they paid more attention to the abstract, superordinary, and decontextualized features of products and services in a broader area. For example, participants #4, #7, and #9 described how they use smartphones to begin with their new trip plans or re-examine their trip plans. Participants #7 reported that “I did not make any plan until arriving in Shanghai. I decided to hang out and began to plan the day’s trip in the hotel. I’d like to visit some interesting places.” In addition, participants also reported that they would use smartphones to make decisions for a restaurant which was far away from their current position in advance. As stated by interviewees # 12, #18, and #21, also reported that they would make decisions for a restaurant in advance by using smartphones. For example, participants #21 reported that “We had decided which restaurant we could go to for lunch in advance before visiting this site. And I’d like to find something popular and delicious in the area.”

Secondly, some interviewees reported that they made decisions about a subsequent activity (e.g., tourism attraction and restaurant), which was geographically closer and occurring sooner. In these situations, participants placed emphasis on more concrete, contextualized, tangible, and incidental features. For example, participant # 8 described how the context influenced his requirements for the food, as reported that “It was time for dinner. I was too tired to walk so that I only wanted to find a nearby restaurant and to eat myself full.” Similarly, as stated by interviewee #10, “I did not book a hotel until I arrived in Shanghai. After I got off the bus, I booked the hotel on the side of the road. I wanted to find a hotel which is close, cheap, secure and cozy.”

In conclusion, the findings illustrated that there are two main types of decision-making contexts based on the temporal distance and geographic scope. Firstly, making or reorganizing a plan for future activities (e.g., the day’s trip) in a broader area of the destination, and secondly, immediately experiencing an activity in a nearby neighborhood. Thus, in this study, decision-making contexts are mainly classified according to how soon an activity is happening and how proximate or distal the activity was.

Impacts of Decision-Making Contexts on Metaheuristics

The initialization solution: Consciously using social heuristics

Data analysis revealed that participants use smartphones to seek information from others, including their social networks (online and offline), social media users, and residents (see Figure 3). The findings indicate that social heuristics are consciously employed to initiate information search rules. Specifically, in relation to how soon and how close the planned activity is, participants attend to different types of information, different information processing, and different judgment on alternatives. There are two types of default information search processes (exploring abstract and diverse travel information in a broader space and exploiting concrete and intensive information in a smaller space; see Figure 4).

Figure 3.

Consciously deploying social heuristics for information seeking.

Figure 4.

Search rules in different decision contexts mediated by smartphones (using social heuristics).

The analysis revealed that when a proposed activity is not immediate, interviewees tended to explore more abstract and diverse travel information when using smartphones in the destination. First, interviewees tended to actively and consciously see what other people do and imitate observed behaviors by using keywords to search for travel products in the destination on social media apps, review website apps, and search engine apps. For example, participants #4 described how they planned the day’s trip at a hotel by using smartphones: “I searched the travel information to look for other tourists’ travel experiences by using the keywords (e.g., the interesting places in Shanghai) in Baidu (a search engine) at the hotel.” Similarly, participants also reported that they would select a restaurant in advance by using smartphones. As stated by participant #21, “We had decided which restaurant we could go to for lunch in advance before visiting this site. And I searched restaurants in Little Red Book app (a social e-commerce website) by using keywords, such as the Shanghai’s best restaurants.”

Second, interviewees tended to ask for suggestions on tourism products and services from their social networks who had prior experience or were familiar with the destination. For example, interviewee #12 described how she asked for suggestions on restaurant selection from the local friends on WeChat: “I will have dinner with a foreign visitor this evening, and I’d like to introduce Chinese food to them. I asked my local friends on WeChat to use my smartphone to recommend some good restaurants that can represent Chinese culture in Shanghai.” What’s more, after interviewees got suggestions from their remote and local social networks, they continued to search for travel information by using smartphones for further evaluation. For example, interviewee #33 said, “I asked my local friends to recommend some interesting places and good restaurants. Also, I used my smartphone apps to search travel information in detail.”

Data analysis found that when a proposed activity was nearby or immediate, interviewees paid more attention to the feasibility of their decision and transaction cost, such as the geographic distance and their specific needs. Most of the interviewees tended to search for concrete and specific information about activities and products nearby by using review website apps or digital maps on their smartphones and then using online filters to exclude unwanted choices. For example, interviewee #6 described how they searched for hotel information after arriving in Shanghai: “I got off from the bus when arriving in Shanghai. I used my smartphone to search for information about hotels on the side of the road. I paid more attention to the distance, price, and security. I used the location feature of Meituan (an e-commerce website) app on my smartphone to search the nearby economic hotel.”

Interviewees used social cues by using online filters to rank alternatives and evaluate several top-ranked alternatives. Interviewees spoke of using rankings based on various aspects of the product/service (e.g., popularity, sales, and amounts of consumer reviews), and the number of positive consumer reviews, as stated by interviewees # 15, “I select the restaurant by using the filters (such as popularity ranking) on Meituan app.” Also, review ratings can help tourists to exclude certain alternatives. For example, one interview (#16) reported that “After excluding unattractive choices, I will first consider the restaurant with higher rating.”

Besides directly searching information from remote social networks on social media review websites and digital maps, participants also searched for specific tourism attractions online, based on recommendations from their social networks. For example, Interviewee #5 noted, “Due to time pressure, we decided to visit a nearby tourism attraction. Tianzi Lane came into my mind because my teacher and classmates mentioned it before. Then I searched the detailed information on Baidu (the search engine) on my smartphone.” Also, interviewees can reduce the number of possible alternatives by seeking inspiration from their online social networks and then searching for more detailed information through their smartphones. Some interviewees sought to re-enact the successful travel experiences of friends. For example, “I visited Tianzi Lane because I saw my friend’s travel post about a site on WeChat. Then I searched detailed information by using smartphones.” (Interviewee #6).

Furthermore, participants tended to be unconsciously influenced by the physical context during travel. For example, interviewee #3 reported that the physical context impacted his decision: “I canceled the original plan of visiting the Film theme park due to time constraints. Then, I found the bus stop named Drunken Bai Pond where I was at that time. Thus, I searched for detailed information about it on Google.”

Acceptance and selection strategies triggered by unconscious heuristics

The data analysis revealed that interviewees continued to weigh and value the alternatives after adopting social influence to narrow down their choices. The analysis identified the heuristics that were unconsciously triggered or employed to process information. These heuristics can help tourists to decide whether an alternative is excluded, evaluated further, or purchased.

Recognition

When presented with alternative offerings on a smartphone app, interviewees unconsciously gave a higher value to an offering or brand they recognized. Analysis of the data suggested that recognition-based heuristics were used to recognize or identify possible alternatives quickly. In other words, interviewees stopped searching for more information and, in turn, began with the recognized alternative.

This stopping rule was reported among interviewees with both proximal and distal distance. For example, interview #8 reported that she stopped searching when she saw a familiar tourism attraction: “I know this site because I heard of it many times and also learned it from TV and films. The high frequency of the name in my life makes me think that it must be special or unique. When I saw it in the ranking list of Shanghai travel attractions on the digital map, I decided to visit it.” Similarly, one interviewee (#9) reported: “I searched accommodation that was close to the subway stations by using the filters on Xiechen. I found the Jinjiang Inn, a hotel that we are familiar with, and briefly read the reviews of the latest week.”

Similarity

Analysis of the data showed that representativeness heuristics were unconsciously triggered among interviewees when they found a certain alternative similar to a product or service they knew. However, similarity works differently among interviewees. The similarity heuristic works as a stopping rule when tourists have neither a pre-trip plan nor a multi-destination trip. Interviewees reported making quick judgments based on the perceived similarity of an option with something that they had already experienced. One interviewee (#29) reported how the similarity of an attraction to a site that she visited previously influenced her decision: “Online consumer reviews described Tianzi Lane as similar to South Luogu Lane that I visited. I was in a hurry when I visited South Luogu Lane because of time pressure. Thus, I’d like to visit Tianzi Lane to remedy this.”

On the other hand, the findings revealed that similarity works as a continuing rule rather than a stopping rule for some tourists. Interviewees reported that they excluded alternatives perceived to be too similar to products or services they had experienced or planned to experience during this trip. For example: “I search information about must-see places in Shanghai by using smartphone apps such as Baidu and Mafengwo (a review website) this morning because I’d like to re-examine my travel plans. I found Yu Park, which I planned to visit, similar to several parks I just visited in Nanjing and Yangzhou two days ago. I decided not to visit it and continued to evaluate other alternatives” (Interviewee #9).

Stereotyping

The data analysis revealed that when interviewees engaged in information processing activities, stereotyping could be unconsciously triggered to process target-relevant information. First, the findings revealed that gender and age stereotypes could be passively triggered when interviewees evaluated the possible offerings. Interviewees reported that gender stereotyping was employed when they processed information. For example, when an offering mentions that a product or service is popular among females, female interviewees and their partners may be persuaded to prioritize these offerings. This was reported in many interviews: “The reviews described many cuisines and street foods there and many girls prefer these delicious snacks” (Interviewee #5). Similarly, one interviewee described that he visited one site with his girlfriend because that site was popular with female travelers: “I do not like to hang out in this kind of place because I do not want to walk too much. But my girlfriend likes it. Girls like this kind of place” (Interviewee #20).

In addition, interviewees explained that age stereotypes influenced some decisions. One interviewee (#17) reported how the age of the target market influenced their decision: “This museum seems different. We are the young generation, so we are not interested in the traditional and changeless museums.”

Availability heuristics

The data analysis revealed that interviewees were unconsciously influenced by the primacy rule. The primacy rule suggests that individuals are easily influenced by information that comes first in a list (Lund, 1925). Interviewees discussed how they made onsite decisions by integrating primacy cues from the online recommendations of providers. For example, one interviewee #29 reported how recommendations from a third-party platform influenced his decision: “I was automatically located by using a smartphone app – Xiecheng (a review website). While searching for information about accommodation nearby, I got a recommendation from this app, which described the accommodation as the top choice in that area. I found it met my requirements, and I decided to inspect the hotel.” In the context of onsite decisions, location-based online recommendations that match travelers’ requirements can help individuals to exclude other choices quickly. This suggests that the information presented to individuals first often has a stronger influence on decisions, and therefore that the sequence of these recommendations can influence real-time decision making.

Stopping and decision rules triggered by conscious heuristics

Social circle

The analysis revealed that the final decision was mainly influenced by a social-circle heuristic. The social-circle heuristic implies that people give a higher value to an alternative when the number of instances within a social circle exceeds others (Pachur et al., 2013). This study found that participants were more likely to choose products or services that featured most frequently within a hybrid of online social media users and personal social networks. For example, interviewee #7 reported how she made a final decision depending on the number of instances in her social circle: “I knew these places well because I learned them from TV in daily life. Meantime, these places were on the list of recommendations from my colleague. Last, when I searched travel tips in Shanghai on search engines (e.g., Baidu), these places were also recommended on travel blogs. Thus, I decided to visit these places.”

Trade-off

Interviewees in this study reported paying more attention to favored attributes of products or services they prioritize to help them decide. Provided the favored attributes met their requirements, participants were open to overlooking other less favorable attributes.

The analysis revealed that interviewees considered a decision further away geographically, or in time, depending on the geographic distribution of tourism products, as stated by interviewee #30 that “I marked the tourism attractions that I’d like to visit on Baidu digital map app. I planned my itinerary that tourism attractions are close to each other.”

In contrast, participants planning a nearby or immediate experience mainly focused on the tangible (or secondary) attributes of the product offering (e.g., geographic distance, price, product quality, environment, sanitation, security, diversity of activities, and taking a good photo), rather than intangible attributes (e.g., service attitude). For example, a promotion can trigger the trade-off heuristic, as reported by one interviewee (#25): “I found a Groupon was available at that time so that I choose this KTV for our entertainment.” Alternatively, another interviewee (#22) evaluated both the positive and negative attributes of the reviews and ended up selecting an option that was “good enough”: “My requirements for the accommodation were security, location, and price. The reviews on the facility, space, and environment were negative, but my friend stayed in this hotel previously, and she confirmed that the security was fine. Considering the location, security, and price, I think it is OK for me.”

However, data analysis revealed that when participants made real-time consumption decisions for others (e.g., their companions or their remote social networks) they evaluated both the intangible and tangible attributes. For example, interviewee #16 reported that when he made a final decision, the main factors influencing evaluation are intangible and tangible attributes: “I learned from online reviews that the restaurant manager is very nice and kind. The manager would come to chat with customers and give away free snacks. It seems the restaurant provides a friendly environment. In addition, the environment and food flavor are also good.”

Discussion and Conclusion

In conclusion, this study attempts to conceptualize tourists’ onsite decision-making mediated by smartphones. The findings provide empirical evidence that when faced with information and choice overload, tourists use a range of metaheuristics to achieve a near-optimal solution for onsite decisions mediated by smartphones. Psychological distance refers to the “subjective experience that something is close or far away from the self, here, and now” (Trope & Liberman, 2010, p. 440). For example, when an event or action is not experienced directly, it is psychologically distant (Liberman et al., 2007). Liberman et al. (2007) stated that psychological distance mainly consists of four dimensions, including temporal, spatial, social, and hypothetical distance, “anchored on a single starting pointing, which is my direct experience of the here and now” (p.353). While processing incoming information, people who were temporally and geographically distal focused on outcomes as well as abstract and decontextualized features, while those who were proximal focused on the process as well as concrete and contextualized features (Trope et al., 2007). Thus, the concept of psychological distance can be used to explain tourists’ behaviors under different decision contexts mediated by smartphones. As illustrated in Figure 1, there are four categories—steps of metaheuristics, heuristics, decision contexts, and practical implications (see Table 3).

Table 3.

Metaheuristics and heuristics used in onsite tourist decision-making mediated by smartphones.

Metaheuristic	Heuristics		Construal level		Implications for business practice
Metaheuristic	Heuristics		High-level construal: outcome orientation	Low-level construal: process orientation	Implications for business practice
Initialization solution	Social heuristics	Consciously deploying	Excluding choice overload by conforming to others’ actions or imitating their social network choices by asking for suggestions or learning from their shared experiences on social media		1. When designing marketing messages (e.g., social media ads), marketers should take advantage of personal social resources or the residents of a destination. For example, marketing ads could be entitled with “your best friends’ favorites” or “top local spots.” 2. Marketers should tailor marketing campaigns according to tourists’ construal levels. 2.1 Marketers could distinguish between construal levels through search behaviors such as keywords used for information and location-based searches. Tourists with high-level construal should be targeted with messages which highlight the gains and diversity; while those with low-level construal should be targeted by messages which focus on feasibility and transaction cost.
		Search rule	Exploring abstract and diverse information	Exploiting concrete and intensive information
		Search space	A broader space: the destination or a district	A smaller space: nearby
		Concerns	Abstract gain/benefit, such as the best products and the most interesting places	Concrete transaction cost: personal energy, economic cost, and temporal cost
Selection strategies	Recognition heuristics	Unconsciously triggered	Unconsciously giving a higher value to a recognized offering or brand, such as a well-known brand or past experience		Marketers should take advantage of heuristics (e.g., recognition heuristics, representativeness heuristics, and availability heuristics) for message design.
	Representativeness heuristics		Similarity: triggered among interviewees when an alternative is similar to a product or service they knew		For example, when tourists search for restaurants in a destination, marketing managers can leverage big data to automatically recommend familiar or similar food as well as promoting the local food.
	Representativeness heuristics		Age/gender stereotyping: giving a higher value to an alternative based on age or gender stereotypes.
	Availability heuristics		Giving a higher value to an alternative which is communicated in an emotionally interesting, image evoking, or concrete manner
Decision rules	Social circle heuristics	Consciously deploying	Consciously giving a higher value to one alternative when the number of instances within a social circle exceeds others. For example, calculating the numbers of recommendations with their social circles (e.g., closer social networks and social media users).		When brands and businesses develop strategies for influencer marketing, they should pay attention to the number of influencers.
	Trade-off heuristics		Intangible attributes: service attitude	Tangible attributes: product quality/environment /sanitation\security /diversity of activities (e.g., shopping)/good photography spots	Marketing messages which target tourists with high-level construal should highlight intangible attributes (e.g., service attitude), while messages which target those with low-level construal should focus on tangible attributes.
	Trade-off heuristics		Intangible attributes: service attitude		For example, when tourists search for tourism sites nearby by using location-based features, the product description should pay attention to the environment, diversity of activities, and convenience.

First, the findings of data analysis extend the phygital-social contexts mediated by smartphones (Liu et al., 2022) by further classifying phygital-social contexts. Being motivated by conceptual and practical considerations, we classified decisions based on whether the activity was immediate or happening later and whether the activity was nearby or geographically more distant. This suggests that not all onsite decisions are equal. When the tourism activity is proximal, tourists focus on the feasibility and tangible attributes of tourism products. In contrast, when the tourism activities are distal, tourists emphasized the outcomes and intangible attributes of tourism products. The findings are consistent with previous studies in consumer behavior, which found that when consumers are making decisions about consumption that are relatively far away in time, they focus on the intangible and primary features of products/services while those planning more immediate consumption focus on the tangible and secondary features (Lee et al., 2014; Wan & Agrawal, 2011). These findings also resonate with the concept of psychological distance, the subjective experience that something is close or far away from the self, here, and now” (Trope & Liberman, 2010, p. 440). While processing incoming information, people who are considering activities that are distal (for example considering an activity that is happening in the future or far away) focus on outcomes and abstract and decontextualized features, while those who are proximal (such as considering an activity that is happening nearby or soon) focus on the process as well as concrete and contextualized features (Trope et al., 2007).

Second, the research findings contribute to understanding onsite decision-making processes mediated by smartphones through a metaheuristic lens. Based on a metaheuristic approach, the present study investigated how tourists make near-optimal decisions under different decision contexts in a destination by essentially using a set of heuristics as “a set of feasible solutions.” The contrasts with previous work, which has tended to focus on specific problem-dependent heuristics. The findings reveal a tourist decision journey mediated by smartphones, including the initialization solution of the information search rule (e.g., exploring abstract and desirable information in a broader area or exploiting the concrete and feasible information nearby by consciously deploying social heuristics), the selection strategies (e.g., heuristics were unconsciously triggered), and decision rules (e.g., some heuristics were consciously used).

Third, rather than focusing on the unconsciousness of heuristics, this study reveals that heuristics are both unconsciously and consciously used for onsite decisions mediated by smartphones. Most extant studies on heuristics highlight unconsciousness under the dual-system mode (McCabe et al., 2016; Wattanacharoensil & La-ornual, 2019). This study distinguishes the heuristics that are consciously used or unconsciously triggered for decision-making in a certain context. When tourists seek an initialization solution for information search, they consciously deploy social heuristics, such as imitating the majority, imitating others’ successful experience, and seeking social proof. Additionally, instead of examining specific heuristics, this study reveals how a decision is made by using serial heuristics.

Fourth, while existing studies on information and choice overload have tended to focus on measurements (e.g., satisfaction, confidence, and regret) and behavioral outcomes (e.g., choice deferral, switching likelihood, categories and assortment, choice option selection, and online filters; Guillet et al., 2020; Sthapit et al., 2019), the findings of this research provide evidence that when faced with information and choice overload, tourists use metaheuristics to help them make a near-optimal decision. The findings respond to the research call from Hamilton et al. (2021) who proposed that businesses should understand decision heuristics for increasingly information and opinions from social networks during consumer decision journeys.

Lastly, although the data for this study were collected before the COVID-19 pandemic, the use of heuristics is not expected to change in the post-pandemic era. Coker (2020) found that when consumers experience high arousal levels, they are even more likely to be influenced by the majority during consumption (such as popular choice). Accordingly, awareness of the risks associated with the COVID-19 pandemic (Galoni et al., 2020) may drive tourists to consciously use social heuristics. During information processing, heuristics (e.g., the recognition heuristic, representativeness heuristic, and the availability heuristics) are unconsciously triggered. For example, Galoni et al. (2020) found that the COVID-19 pandemic created uncertainty, leading consumers to prefer familiar products. Likewise, Rather (2021) revealed that tourists have a higher revisit intention in the pandemic era.

As well as theoretical contributions, the results have several practical implications. The findings of this study suggest that there are four main learnings for tourism providers and local businesses. As a starting point, the local business could explore tourist decision context for using social influence and effectively deploy the necessary resources to accommodate these decision contexts.

First, the research has shown that decision contexts resulting from the phygital-social context significantly influence the decision journey, including information search, information evaluation, and final decision. Based on tourists’ search behaviors under different decision contexts, the goal of a mobile strategy is to deliver valuable content to tourists. When managers make marketing messages/ social content ads for tourists with distal distance, such as travel tips (e.g., the best tourist attractions or the most interesting places), they should highlight the gain/benefits (e.g., social recognition) and primary and intangible features of the products and services (e.g., service attitude). Furthermore, when managers introduce the product or service itself, they should pay attention to transaction cost (e.g., economic value, personal energy, and transport convenience) and other tangible features (e.g., environment factors). Advertisements or social marketing content should be personalized for tourists with different decision contexts. Marketing managers should also consider decision contexts when designing messages and content for smartphone apps. For example, location-based advertisements should highlight the transaction cost and tangible features in a destination.

Second, previous research has identified a range of heuristics that can be used to help with decision-making (Wattanacharoensil & La-ornual, 2019). The key contribution of this study is the demonstration and mapping of exactly how the need to use heuristics is triggered in the first place, and how these heuristics are used in the decision-making process. The findings also revealed that it is better to use heuristic cues (such as recognition, similarity, and stereotyping) to make marketing messages/ads for onsite tourists while tourism managers design market messages. For example, they can highlight the similarity with the landmark which is physically far away and distinctiveness with the product or service which is physically near. Furthermore, because tourists are greatly influenced by age and gender stereotyping for onsite tourists, marketing providers can take advantage of the group stereotyping (e.g., local people) to design messages.

Thirdly, this study revealed that the primacy effect matters in real-time decision-making in a destination. This seems very promising for the application of AR for local businesses, which can help tourists quickly identify the situations around them and directly read the information about the product and services from the providers and social intelligence.

Fourthly, when it comes to recognizing heuristics and social circle heuristics, it is important to expose tourists to the product or service as often as possible. For example, marketing managers could cooperate with online influencers and develop strategies that encourage the sharing of their products and service on social media. Meanwhile, because tourists consciously seek and give a higher value to information from the local group, marketing providers can take advantage of group stereotyping (e.g., local people) to design messages.

Lastly, understanding how tourists use metaheuristics would be useful for trip design. Prior studies on tourist trip design problems mainly focused on the inspiration from animals, such as ant colony optimization metaheuristics. If we understand the metaheuristics travelers use onsite, some of these can be incorporated into algorithms for apps to help tourists to optimize their travel plans-further reducing cognitive load.

Limitations and Future Research

While this study contributed to a better understanding of the use of heuristics in onsite tourist decision-making mediated by smartphones, limitations remain, leading to important suggestions for future studies. Firstly, this study took a qualitative approach which means that attempts to generalize the conclusions from this study should be treated with caution. Secondly, the data were collected in Shanghai, a metropolis with high levels of information density. Arguably, if tourists visit sites with lower information density and fewer choices, the decision-making process may be different. Additionally, a different cultural context may result in different findings, and as acknowledged in the paper, this study mainly focused on domestic Chinese tourists. Finally, the sample for this study was primarily aged between 18 and 35, and while this is consistent with the average age of Chinese internet users, results may differ for tourists of different ages.

Future research could take a quantitative approach to improve the generalizability of results and incorporate data collected in other cities, countries, and contexts to help understand the role of location and culture in onsite decision-making. Additionally, the current study independently explored the heuristics used in specific contexts. Future research could usefully consider if and how tourists might combine various heuristics with helping them make decisions.

Second, it would be important to understand how augmented reality (AR) mobile app advertising facilitates tourists to make an onsite decision. AR technology, which can offer sensory marketing elements (e.g., audio, visual graphics, and touchpoints) through a smartphone touch screen, can enhance purchase intention (Sung, 2021). The findings of our study indicate that the physical signs are crucial for tourists to make onsite decisions. Besides the traditional physical advertising, augmented reality (AR) mobile app advertising may be effective for tourists making onsite decisions.

Supplemental Material

sj-docx-1-jtr-10.1177_00472875221140905 – Supplemental material for How Do Tourists Use Metaheuristics for Decision-Making Mediated by Smartphones in a Destination?

Supplemental material, sj-docx-1-jtr-10.1177_00472875221140905 for How Do Tourists Use Metaheuristics for Decision-Making Mediated by Smartphones in a Destination? by Shasha Liu, Pierre Benckendorff and Judith Mair in Journal of Travel Research

Supplemental Material

sj-docx-2-jtr-10.1177_00472875221140905 – Supplemental material for How Do Tourists Use Metaheuristics for Decision-Making Mediated by Smartphones in a Destination?

Supplemental material, sj-docx-2-jtr-10.1177_00472875221140905 for How Do Tourists Use Metaheuristics for Decision-Making Mediated by Smartphones in a Destination? by Shasha Liu, Pierre Benckendorff and Judith Mair in Journal of Travel Research

Footnotes

Acknowledgements

The authors would like to acknowledge the editors and the anonymous reviewers for their insightful and crucial comments that helped improve the article.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Shasha Liu

Judith Mair

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Shasha Liu is a Postdoctoral Research Fellow in Antai College of Economics and Management, Shanghai Jiao Tong University, China. Her research interests include decision-making, marketing, technology enhanced tourism, and tourist experiences. Email address: ligaotangbo@163.com; s.liu@sjtu.edu.cn.

Pierre Benckendorff is Deputy Dean of the University of Queensland (UQ) Graduate School and Associate Professor in the UQ Business School. His research interests include visitor behavior, tourism information technologies, and tourism education and training. Email address: p.benckendorff@uq.edu.au.

Judith Mair is Discipline Leader of the Tourism Discipline Group and Associate Professor in the UQ Business School, University of Queensland, Australia. Her research interests include pro-environmental behavior and resilience both in tourism and events; the impacts of events on community and society; consumer behavior in events and tourism; the relationship between events and climate change; and business and major events. Email address: j.mair@business.uq.edu.au.

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