Effect of a perceived threat of informal actors on the business performance of formal actors: inbound tour operators’ perspective

ABSTRACT

This paper develops and tests a conceptual model to examine the effect of a perceived threat of informal actors on the business performance of formal actors. Drawing on a sample of 130 inbound tour operators, structural path analysis shows that the perceived threat of informal actors is positively associated with the formal actors’ service quality and customer acquisition efforts, which subsequently are positively associated with an increase in business performance. The perceived threat of informal actors appears to increase business performance only through its influence on service quality and customer acquisition efforts. Based on the findings, actionable recommendations are provided.

KEYWORDS:

• Tourism
• informal actors
• formal actors
• tour operators
• service quality
• customer acquisition efforts
• business performance

Introduction

The informal economy, also known as the hidden, underground, black, grey, shadow, irregular, or unobserved economy, is increasingly gaining traction among researchers (Khuong et al., 2020). Partly, this is due to the significant economic role played by informal businesses (Y. C. Chen & Chen, 2020; Lv, 2020). For instance, the International Labour Organization [ILO] (2020) noted that two billion people, equivalent to 61.2% of the world’s employed individuals, worked in the informal economy. These numbers point to the significant role of the informal sector in creating jobs. The informal economy exists in all countries around the globe, but its magnitude is higher in developing countries than developed ones (Omri, 2020). According to ILO, nations with the highest incidence of the informal sector in Africa include Egypt, Ivory Coast, and Namibia. In Asia, the highest incidence is registered in Mongolia, Pakistan and Vietnam. Bolivia, Colombia, Ecuador, and Honduras are reported to have the highest numbers of individuals employed in the informal economy in Latin America. Despite its positive contribution to job creation, the informal economy is, on the other hand, associated with negative impacts such as the creation of unfair competition and limited tax revenue opportunities for governments, which in turn negatively affects national economic growth (Dell’Anno, 2016).

Tourism is one of the industries that attract considerable numbers of informal actors, and for that reason, the subject has garnered significant scholarly attention (e.g., Çakmak, et al., 2019; Dahles, 1998; Henderson & Smith, 2009; Biggs et al., 2012; Rogerson & Letsie, 2013; Damayanti et al., 2017; Kedir et al., 2018; Pécot et al., 2018; Truong, 2018). For instance, Dahles (1998) conducted a qualitative study in two cities of Bali and Yogyakarta in Indonesia and found that small-scale enterprises were characterised by features that could neither be classified as elements of the formal nor informal economy. Likewise, Biggs et al. (2012) studied the factors associated with resilience as experienced by formal and informal tourism enterprises and their response during the tsunami in 2004 and the political crisis of 2008 in Phuket, Thailand. They found that informal actors tended to have better financial conditions during the crisis than formal actors did. Finally, Çakmak (2020) examined the interaction between the shadow tourism economy and employment in Thailand. The study found that informal actors had a sizable contribution to the nation’s economic growth.

Overall, the extant body of tourism research related to informal actors has mainly focused on issues such as the effect of tourism on the level of the informal economy (Lv, 2020), the magnitude of the shadow economy (Çakmak, 2020; Smith & Henderson, 2008), and the link between informal tourism actors and pro-poor tourism (Rogerson, 2014). While these studies provide valuable insights, several aspects related to the informal sector in the tourism industry remain unexplored. One of such aspects is the impact of informal actors on the performance of formal actors. Furthermore, it is important to note that the co-existence of informal actors with formal ones has been characterised by adversarial relationships (Çakmak, 2020). In addition, the informal economy is often associated with negativity, as exemplified by labels such as black, shadow, unofficial, underground, and hidden economy (Huynh & Nguyen, 2020; Lv, 2020). As such, an empirical examination of the impact of informal actors on formal actors warrants further research. Against this backdrop, this paper examines the effect of informal actors on the business performance of formal actors in tourism. To the best of our knowledge, the threat of informal actors on formal actors has received minimal attention in the business field in general, particularly in travel and tourism marketing and management. The present study develops and tests a conceptual model and thus contributes insights into the mechanism through which informal actors affect the business performance of formal actors. Such insights are valuable because they provide a basis for actionable managerial and policy recommendations that can contribute to the development of the tourism industry, especially in developing countries where the informal sector is prominent. Besides, since many aspects related to the informal sector are still unexplored (Lv, 2020), the results of this study may provide insights and foundation for the research of informal actors in other industries. As such, our study contributes to extending knowledge in a domain that has not been extensively explored.

As value chains worldwide have suffered a significant blow from the COVID-19 pandemic, an intriguing question for tourism businesses is how to navigate through the current and post-pandemic business landscape. Considering that the demand for tourism products is much more responsive to changes in discretionary income (Crouch et al., 2007; Dolnicar et al., 2008), formal tourism actors should be prepared for intense competition from informal actors who often tend to offer cheaper offerings due to lower operating costs. Therefore, this study provides insights that will be relevant even after the pandemic. The remainder of this paper is structured as follows. The next section presents a literature review and hypothesis development, followed by the methodology section. The subsequent section presents the analysis and results of the study. Finally, the discussion section places the results in perspective, followed by closing remarks that identify limitations and opportunities for future research.

Literature review and hypothesis development

Informal actors

One of the characteristics of emerging economies is their sizable informal economy (Omri, 2020; Xu et al., 2020). The informal economy is defined by Roberts (1989, p. 41) as “the set of economic activities, often, but not exclusively, carried out in small firms or by the self-employed, which elude government requirements such as registration, tax and social security obligations, and health and safety rules.” Thus, informal actors refer to individuals and firms that engage in such activities. As Williams and Martinez (2014) note, informal actors do not register themselves or their businesses to the tax authorities. In the context of tourism, informal actors, also known as informal tourism entrepreneurs, refer to the individuals or enterprises that engage in offering tourism products and services informally (Çakmak et al., 2019). These include beach boys, taxi drivers, individual owners of private hire vehicles, hotel staffs, freelance tour guides, tour operators’ staffs such as guides and drivers who provide tourism services informally.

Due to their limited resources and the pressure to increase profit margins, small enterprises may be tempted to keep their operations off the tax records. However, in their study on the effect of tax registration on profitability, McKenzie and Seynabou Sakho (2010) found that while tax registration does increase profit for medium-sized companies, it reduces gains for small and big enterprises. Views on informal actors are two-fold: some consider them unacceptable while others support them (Williams & Martinez, 2014). Understandably, those who disapprove of informal actors base their argument on the negative impact of the informal economy on national economic growth. In contrast, those who support informal actors view doing business informally as being in an “incubation period” in which actors test the feasibility of their business before deciding to become formal (Williams et al., 2013). In other words, the proponents of informal actors consider operating informally as a transitional phase towards formalisation.

Interestingly, the spread of digital technologies appears to empower informal actors in developing countries. Due to their limited resources, individuals and firms operating in the informal economy are often unable to implement marketing campaigns to promote their businesses. As a result, their access to markets is limited. However,the spread of digital platforms, such as Uber, Airbnb, and local varieties, offers new opportunities for informal entrepreneurs to interact with many potential customers. Furthermore, these platforms allow informal actors to build “reputational capital” through positive reviews left by previous customers, which plays a crucial role in attracting prospective clients. As digital platforms continue to empower actors in the informal sector, the question regarding the impact of these actors on the formal actors becomes even more relevant. In the subsequent subsections, we present arguments and hypotheses related to the effect of informal actors on the service quality, customer acquisition efforts and business performance of the formal actors. To set the scene, we will first introduce the concepts of service quality and customer acquisition efforts.

Service quality

Service quality can be defined as ‘customer’s perception of service performance, based on evaluations of service dimensions and their associated service attributes’ (Ahrholdt et al., 2017). Overall, previous studies have shown that a significant relationship exists between service quality and customer satisfaction in the sense that as service quality increases, and so does customer satisfaction (e.g., Pandža Bajs, 2015). Furthermore, previous studies (e.g., Chan et al., 2015; Su et al., 2015) have shown that the effect of service quality on satisfaction can eventually translate into positive behavioural intentions such as willingness to join higher priced tours organised by the same operator. In addition to confirming this relationship, Ahrholdt et al. (2017) established two mechanisms that link service quality and customer loyalty. In the first mechanism, customer delight mediates the link between service quality and loyalty. As for the second mechanism, service quality and loyalty are mediated by customer satisfaction. Also, a recent study by Lin et al. (2021) has concluded that service quality significantly enhances brand image. With such potential positive outcomes, the importance of service quality in creating and maintaining a competitive advantage cannot be overemphasised. As W. J. Chen and Chen (2014) emphasise, service quality is among the critical elements needed for attaining and sustaining a competitive advantage in a marketplace characterised by fierce competition.

In terms of service quality indicators, the SERVQUAL model, a multi-item scale for measuring consumer perceptions of service quality, has been widely adopted by previous studies to gauge various services. Recent examples include its application in evaluating: the quality of airline baggage handling systems (Rezaei et al., 2018), public services (Ocampo et al., 2019), and healthcare services (Mehrotra & Bhartiya, 2020). Developed by Parasuraman et al. (1988), the original SERVQUAL model had ten dimensions, but after modifications, it remained with five dimensions: assurance, empathy, reliability, responsiveness, and tangibles.This model recognises that a firm’s service proposition is twofold, involving tangible and intangible service components.All indicators related to the firm’s physical facilities, such as marketing and promotional and personnel, are referred to as the tangible component. Conversely, the intangible element generally refers to worker service quality, in other words, the remaining four dimensions of service quality: assurance, empathy, reliability, responsiveness (Parasuraman, Zeithaml, & Berry, 1988).

Like scholars in other disciplines, tourism scholars have also used the SERVQUAL model extensively. Typically, researchers tend to modify and apply service quality assessment scales to suit their research contexts. For instance, Ding et al. (2020) used a modified SERVQUAL scale to explore perceived service quality attributes in Airbnb accommodation, while Su et al. (2016) applied a SERVQUAL scale specifically designed to provide accurate customer feedback in a lodging context. The application of modified scales is plausible because, as Dolnicar et al. (2015) recommend, researchers should consider including relevant indicators for specific contexts. Such indicators allow researchers to generate insights that have meaningful implications for practice. In a similar vein, Lin et al. (2021) note that due to a cognitive process, ‘the perceived service quality is not only subjective but also context-dependent and will likely vary according to several factors. This notion is plausible as it echoes an analysis in Ban and Ramsaran (2016) that applied a performance-based measure of service quality (SERVPERF) and found three additional dimensions specific to the ecolodge sector: eco-friendly practices, eco-activities, and eco-learning.

Customer acquisition efforts

While service quality is crucial for retaining customers, firms must continuously engage in customer acquisition efforts to identify and acquire prospects. This is crucial for growing and rejuvenating their customer base. Customer acquisition efforts can be conducted both online and offline. Examples of online customer acquisition efforts include search engine optimisation (gaining traffic through “organic” search results on search engines), search engine marketing (gaining website traffic by purchasing ads on search engines), social media campaigns, e-mail marketing, content marketing, and affiliate marketing. Examples of offline efforts include participation in trade fairs, roadshows, and distribution of product brochures. Although customer acquisition efforts are vital for enhancing business performance, their implementation is costly (Tillmanns et al., 2017).

Nevertheless, customer acquisition efforts can significantly impact overall firm performance (Arnold et al. 2011; Denizci & Li, 2009; Narayanan et al., 2004; Pauwels, Silva-Rosso). Therefore, to maximise customer profitability, firms must balance the costs and benefits of their customer acquisition efforts. Like businesses in other industries, tourism businesses need to grow their customer bases just as much as they need to retain their current customers. Considering the intense competition prevailing in the tourism industry, firms must continuously engage in customer acquisition efforts. As Christy and Penn (1994) noted, a much more active marketing stance becomes essential in the face of increasing competition and falling demand.

Effect of informal actors on service quality and customer acquisition efforts of formal actors

Since the demand for tourism products is discretionary, there is pressure on all tourism actors to simultaneously lower their prices and increase the quality of their services (Mwesiumo, 2019). More so, as noted in the introduction section, unlike other discretionary expenditures, demand for tourism products is much more responsive to changes in discretionary income (Crouch et al., 2007; Dolnicar et al., 2008). Therefore, as informal actors often tend to have small-scale operations, and do not pay taxes and other charges that formal actors in destinations incur, they are more likely to charge lower prices for their offerings. Consequently, they can attract the attention of price-sensitive customers. Hence, informal actors can partly be viewed as a disruptive force in a destination. Disruptive services are associated with a cost leadership approach, simplicity, low quality, and appeal to the low-end market segments (Guttentag, 2015). In contrast, well-established firms whose focus is on profit maximisation and maintaining reputation may not be interested in offering such services. However, in the long run, disruptive services can draw customers’ attention and eventually attract the mainstream clientele (Guttentag, 2015). Thus, although informal actors do not offer superb quality, they may gradually attract mainstream clientele.

To maintain competitive advantage and growth in a changing environment, firms must continuously develop new and upgrade their existing capabilities (Pettus et al., 2018). Given that the increasing number of informal actors in a destination presents a significant shift in the competitive environment, formal actors would be compelled to unleash appropriate strategic responses. Such responses may involve the deployment of strategies that will ensure their survival. For instance, in the context of the telecommunication industry, Majumdar (2019) found that a changing competitive landscape due to the entry of new competitors resulted in significant responses by incumbents in the form of increased advertising spending and reduced selling and overhead costs. Due to the importance of service quality and customer acquisition efforts, it is plausible to assume that quality improvement and customer acquisition efforts are likely to be among strategic responses that firms may choose to unleash in the face of growing competition. Accordingly, we argue that the perception of an increased presence of informal tourism actors in a destination will compel formal actors to respond strategically by improving the quality of their services and expanding their customer acquisition efforts. Formally, we propose the following hypotheses.

H1: A perceived threat of informal actors is positively associated with the formal actors’ efforts to improve service quality.

H2: A perceived threat of informal actors is positively associated with the formal actors’ customer acquisition efforts.

Service quality, customer acquisition efforts and business performance

Ocampo et al., (2019) define business performance as the firm’s handling capacity of external elements which are characterised by the company’s market position, increasing consumer satisfaction, market development and profitability. However, other researchers view business performance through the lens of internal factors. For instance, Abdullah et al. (2009) define business performance in terms of the production capacity of a firm and customer satisfaction. They argue that a firm that can produce multiple goods and services is likely to increase the satisfaction of its customers. Hence, a firm’s business performance increases proportionately with its internal capability to adjust to the market changes (Abdallah & Matsui, 2008).

Similarly, Akhtar et al. (2019) measure business performance using four dimensions: operational performance, business development, financial performance, and environmental performance. In contrast, Inman et al. (2011) employed economic and market understanding to measure the business performance, while Nawanir et al. (2013) relied on profitability, sales (share and growth and customer satisfaction. Overall, the approaches used by these studies is in line with the notion that business performance is a multidimensional construct, and therefore the use of composite measures is deemed appropriate (Shah et al., 2015).

Regardless of how one defines business performance, two questions have always been important in the business and strategic management literature. The first question is: what are the sources of high performance? The second is: why some firms can maintain their high performance while others fail?. These two questions can be traced back to the seminal works of Jay Barney (Barney, 1991) and Michael Porter (Porter, 1985, 1991, 1994, 1996, 1998, 2008). While Barney explains variation in performance due to valuable, rare, non-imitable and organised resources, Porter argues for the variation in performance due to unique, well-aligned and valuable activities. In other words, Barney argues that it is resources that distinguish top performers from the rest, while Porter argues activities is what makes the difference. To that end, Porter argues that firms can achieve high performance by designing and implementing a unique set of primary and support activities. Generally, scholars agree that both perspectives provide a plausible explanation for the variation in performance (Gaya & Struwig, 2016).

This paper embraces the activity-based view and argues that service quality and customer acquisition efforts are among key activities for tourism firms that can cause a significant difference in business performance. Porter distinguishes between primary activities (logistics, production, marketing, sales, and service) and support activities (human resource management, technology development, procurement and firm infrastructure). Service quality and customer acquisition efforts fall under the category of primary activities. Viewing business performance in terms of dominance in the marketplace, customer satisfaction, and financial profitability, we argue that activities geared towards service quality improvement and customer acquisition will translate into increased customer satisfaction and an expanded customer base. Subsequently, such efforts can contribute to higher performance. Formally, we propose the following hypotheses:

H3: Formal actors’ efforts to improve service quality are positively associated with their increased business performance.

H4: Formal actors’ customer acquisition efforts are positively associated with their increased business performance.

In sum, hypotheses 1–4 are summarised in Figure 1.

Figure 1. Conceptual model

Methodology

Description of the research setting

The conceptual model developed in this study was analysed based on data collected from 130 inbound tour operators in Zanzibar. Located 35 kilometres from mainland Tanzania, Zanzibar is one of the two countries forming the United Republic of Tanzania. As a semi-autonomous part of Tanzania, Zanzibar retains full autonomy over non-union matters, such as tourism, agriculture, health, economy, investment, legal affairs, primary and secondary education. Therefore, the tour operations in Zanzibar are exclusively reserved for the local Zanzibaris (The Zanzibar Tourism Act No. 6, 2009). In addition, section 19 (a) of the act restricts the hotels from conducting tour operations, and section 19(c) stipulates that one needs to have an operating license to run a tour operating business. The licence is not granted unless one meets all registration procedures from the Zanzibar registrar of companies and taxation clearance from the tax authorities. Thus, Zanzibar tourism regulations require all tourists’ transfers and excursions to be organised by the registered and licensed Tour operators (The Zanzibar Tourism Act No. 6, 2009). For instance, in their operating license, hotels are required to respect their functional boundaries. Thus they are strictly prohibited to directly organise transfers and excursions for the guests, as these activities should be arranged through the local tour operators. Likewise, as a condition for obtaining a tour guide’s identity card, tour guides must work under the registered tour operators (The Zanzibar Tourism Act No. 6 of 2009, 1996). Hence, the informal operators considered in this study are all individuals and entities that provide tour operating services in Zanzibar, contrary to the Zanzibar Tourism Act No. 6, 2009.

Sample and data collection

Data were collected through a self-administered questionnaire distributed from January to March 2020. The questionnaires were distributed to registered and licensed tour operators in Zanzibar. In total, the sampling frame for the study consisted of 233 operators, constituting inbound tour operators registered by the ZCT. Following Mwesiumo et al. (2019), data were collected using a drop-off and pick-up method whereby questionnaires in sealed envelopes were delivered to the tour operators’ premises and collected afterwards. The drop-off and pick-up method is one of the well-established data collection methods. It has been widely used by previous tourism and hospitality scholars (e.g., Mwesiumo & Halpern, 2018; Safavi & Karatepe, 2019; Said & Tanova, 2021). Applying the drop-off and pick-up method helped confirm tour operators’ eligibility and reduce the potential for non-response bias. The tour operating companies who responded to our questionnaire did so voluntarily. As a requirement, the questionnaire was to be completed by the manager who is most knowledgeable about the operations of the firm and their business environment in general. To ensure clarity, the introductory part of the survey stated that informal players refer to any person or business that engages in business informally (against the law). Eventually, 130 usable questionnaires were collected (56% of operators in the sampling frame). According to (Assaker, Huang, & Hallak, 2012), a minimum of 100 observations is required to be confident in using PLS-SEM. Likewise, based on Hair et al. (2017), a minimum threshold of 129 questionnaires is sufficient to meet the statistical power of 80%. To assess non-response bias, we followed Armstrong and Overton (1977) and conducted a paired sample test of the responses of the first 25% of respondents and the responses of the last 25%. Results showed that there was no significant difference between the two groups (p >0.05), which suggested that non-response bias was unlikely.

Operationalisation of the variables

The questionnaire included questions that measured all the variables included in the conceptual model: the perceived threat of informal actors, customer acquisition efforts, service quality, and business performance. In addition, to assess the effect of potential alternative explanations for variation in business performance, we considered two variables, the age and size of the tour operators. We included these variables because we assumed that the age and size of the tour operator might affect the magnitude of the efforts in improving service quality, customer acquisition and subsequently, their business performance. This is because, as we noted earlier, service quality, customer acquisition efforts require resources. Hence large and mature tour operators may be in a better position to implement them.

All indicators were measured on a 5-point Likert scale ranging from 1 = strongly disagree to 5 = strongly agree. Five indicators developed based on Kwieciński (2017) were used to measure a perceived threat of informal actors (TIA). Service quality (SQ) was measured by using six indicators developed based on Rezaei et al. (2018) and Su et al. (2016). Measures for customer acquisition efforts (CAE) were created by considering the modern marketing communication complex (Chaffey, 2015). Business performance (BP) was measured using four items based on Espino-Rodríguez and Gil-Padilla (2015). Following Mwesiumo and Halpern (2018), the control variable age was operationalised as the number of years the operator has been in business. Operator size was measured reflectively using two indicators, the number of employees and departments. While the extant literature provided us with the basis for the measures, we followed Dolnicar et al. (2015) and crafted our indicators to suit the research context. To ensure relevance, four experts assessed the face validity of our final measures. Two of these are professionals with extensive experience in tour operations in Zanzibar, while the other two are scholars within tourism management. Finally, we conducted a pilot study involving 20 respondents to assess the clarity of the questions. Based on the feedback from the pilot study, the final version of the questionnaire was prepared. The responses collected in the pilot study were not included in the final analysis reported in this paper. Table 1 presents the focal constructs of the study and their corresponding measures.

Table 1. Measures of constructs and control variables

Constructs	Abbreviations	Measures
The perceived threat of informal actors (TIA)	TIA1	Informal players seriously threaten the existence of our company
	TIA2	Anything we can offer; informal players can match easily
	TIA3	Informal players force our company to pay our staff lower salaries
	TIA4	Informal players force us to lower the prices of our services
	TIA5	Informal players do whatever it takes to steal our clients
Service quality efforts (SQ)	SQ1	We always adhere to the schedule promised to the clients
	SQ2	We always use competent staff (e.g., tour guides, drivers …)
	SQ3	We always deliver agreed service standards to the clients
	SQ4	We always use the agreed facilities (e.g., hotels. Lodges, etc.)
	SQ5	We comply with international and local consumer protection acts
	SQ6	We abide by the local tour operators’ standards
Customer acquisition efforts (AE)	CAE1	We actively use social media to promote our company
	CAE2	We actively use e-mail marketing to promote our business to potential clients
	CAE3	We regularly use search engine optimisation to promote our company
	CAE4	We frequently update our website
	CAE5	We attend trade fairs regularly
Business performance (BP)	BP1	Our organisation market share has increased during the past three years
	BP2	Our organisation market share growth has exceeded that of informal players
	BP3	Profit has increased during the last three years
	BP4	Customer satisfaction has increased during the last three years

1. We used the phrase “informal players” in the questionnaire because it turned out from the pilot study that our target respondents mostly used it when talking about “informal actors”. 2. TIA3 was eventually removed for further analysis because we realised that the phrase “lower salaries” is ambiguous due to the lack of a specific reference point.

Analytical approach

The conceptual model, including the two control variables, was estimated using partial least square structural equation modelling (PLS-SEM). This method is increasingly used in various research fields (e.g., Mwesiumo et al., 2021; Halpern et al., 2021; Mwesiumo et al., 2021). . Considering the composite nature of our latent variables, PLS-SEM is a suitable choice (Sarstedt et al., 2016). More so, the size of our sample (130) meets both the recommended size of 100 observations (Assaker et al., 2012) and the PLS-SEM sample size for a statistical power of 80% as recommended by Hair et al. (2017). The analysis was conducted using SmartPLS 3 (Ringle et al., 2015).

Assessment of the measurement model

The focal variables in this study are measured using a reflective measurement model. Thus, their internal consistency reliability, convergent validity and discriminant validity must be assessed before estimating the structural model (Henseler et al., 2016). To assess internal consistency reliability, Dijkstra and Henseler (2015) proposed a novel and more robust metric, rho_A (ρA). Henseler et al. (2016) note that at the moment, this is the only consistent measure for assessing the reliability of PLS construct scores. The recommended threshold for this measure is ρA >0.7. As for convergent validity, the assessment is done by checking the value of the average variance extracted (AVE), and it is established when AVE is at least 0.5. In addition, Hair et al. (2011) recommend that the loading of each measure must be at least 0.7. However, Hair et al. (2017) noted that researchers should keep indicators that load between 0.4 and 0.7 if doing so helps retain the construct’s content validity. As shown by Table 2, internal consistency reliability is established for all multi-item constructs. In contrast, convergent validity is established for all except customer acquisition efforts whose AVE is slightly lower than 0.5. Since our measurement model is reflective, we can improve convergent validity by dropping indicators with lower loadings. Indeed, dropping CAE1 resulted in an increase in AVE to 0.528. However, we decided to follow Hair et al. (2017) and retain this indicator to maintain content/practical validity. This is because CAE1 measures the use of social media to promote tour operator’s services, and our descriptive statistics suggest that among all the indicators of customer acquisition efforts, social media and e-mail marketing are crucial, as they have the highest average scores (4.1 and 4.2, respectively). Moreover, previous studies (e.g., Senders et al., 2013) have shown that social media plays an important role in the marketing activities of tour operators because customers enjoy building online relationships with them. Thus, dropping it would compromise the practical relevance of the study. Besides, all the loadings that fall below 0.7 are above 0.4, therefore, according to Hair et al. (2017), acceptable for further analysis.

Table 2. Internal consistency reliability and convergent validity

	Mean	SD	Factor loadings	ρA	AVE
TIA1	3.923	1.225	0.778	0.758	0.572
TIA2	3.732	1.031	0.691
TIA4	4.023	1.167	0.789
TIA5	4.377	0.905	0.765
SQ1	4.354	0.840	0.757	0.853	0.567
SQ2	4.438	0.723	0.775
SQ3	4.406	0.833	0.679
SQ4	4.380	0.759	0.842
SQ5	4.154	0.881	0.704
SQ6	4.077	0.981	0.749
CAE1	4.054	0.987	0.588	0.727	0.470
CAE2	4.169	0.913	0.677
CAE3	3.738	1.034	0.699
CAE4	3.892	0.963	0.716
CAE5	3.754	0.977	0.739
BP1	3.364	0.940	0.774	0.840	0.578
BP2	3.333	1.044	0.783
BP3	3.124	0.980	0.643
BP4	3.985	0.992	0.829
OS1	2.969	1.381	0.932	0.800	0.729
OS2	10.846	15.740	0.767
OA	9.646	7.340	1.000	1.000	1.000

Discriminant validity is the extent to which each construct is different from other constructs in the estimated model (Hair et al., 2017). Traditionally, discriminant validity is assessed by using the Fornell–Larcker criterion and cross-loadings, whereby discriminant validity is established when the square root of each construct’s AVE is greater than its highest correlation with any other construct. However, Henseler et al. (2015) introduced the Heterotrait-monotrait (HTMT) ratio as a superior approach for assessing discriminant validity. With this approach, discriminant validity is established when the HTMT ratio is less than 0.85 (Voorhees et al., 2016). As shown in Figure 2, discriminant validity among the focal constructs in this study was established as all the HTMT ratios are below the threshold of 0.85.

Figure 2. HTMT ratios for the focal variables

Common method variance

As our study used self-reported survey questionnaires, we applied two remedies for mitigating the common method variance (Podsakoff et al., 2003). First, questionnaires were only given to respondents who were willing to participate in the study. Respondents were given enough time to fill out questionnaires, and they were assured of the anonymity of their identities. The introduction of the questionnaire included information like “please understand your participation is entirely voluntary, and you have the right to withdraw your consent or discontinue participation at any time.” There was also a psychological separation where the questionnaire did not mention the objective of the study. Second, we conducted Harman’s single factor test for assessing potential common method variance. A study is susceptible to common method variance if at least 50% of the variance is explained by a single factor (Messerschmidt & Hinz, 2013). The results of the test showed that only 34% of the variance is explained by a single factor, suggesting that there is no evidence of the common method bias in the data.

Structural model and hypothesis testing

The structural model (Figure 3) was estimated by bootstrapping the recommended 5000 re-samples and evaluated by checking path coefficients and the R² values. More so, the model’s predictive power was assessed by examining Stone Gaesser’s (Q²) value. For a model to have meaningful predictive power, Q² must be above zero (Hair et al., 2017). In addition to the hypothesised paths and potential alternative explanations, we followed Zhao et al. (2010), who suggested that researchers must assess both direct and indirect effects when an estimated model involves a mediation effect. This assessment allows researchers to identify the nature of mediation or non-mediation involved. For that reason, our structural model includes an additional path that estimates the direct effect of the perceived threat of informal actors on the business performance of formal actors. The results of model estimation and indirect effect analysis are presented in Tables 3–6.

Table 3. R squares, Adjusted R squared and Stone Gaesser’s (Q²) value

Dependent variable	R^2	Adjusted R^2	Q^2
BP	0.299	0.270	0.135
CAE	0.134	0.113	0.048
SQ	0.226	0.208	0.120

Table 4. Estimates of the path coefficients

Path	Coefficient	P Values	Significant	VIF	Hypotheses
TIA->SQ	0.427	0.000*	Yes	1.020	H1
TIA->CAE	0.315	0.006*	Yes	1.020	H2
SQ->BP	0.313	0.013**	Yes	1.737	H3
CAE->BP	0.242	0.021**	Yes	1.551	H4
TIA->BP	−0.080	0.426	No	1.267
OA->BP	−0.105	0.273	No	1.102
OA->CAE	−0.001	0.995	No	1.098
OA->SQ	0.043	0.637	No	1.098
OS->BP	0.219	0.015**	Yes	1.135
OS->CAE	0.188	0.064***	Yes	1.079
OS->SQ	0.185	0.022**	Yes	1.079

*Significant at p < 0.01; ** significant at p < 0.05; *** significant at p < 0.10

Table 5. Specific indirect effects

Path	Coefficient	Confidence interval bias corrected		P Values
		2.5%	97.5%		Significant
TIA->SQ->BP	0.133	0.021	0.305	0.074	Yes***
TIA->CAE->BP	0.076	0.005	0.187	0.099	Yes***
OA-> SQ->BP	0.014	−0.036	0.094	0.667	No
OA-> CAE->BP	−0.000	−0.047	0.048	0.995	No
OS-> SQ->BP	0.058	0.008	0.136	0.076	Yes***
OS-> CAE->BP	0.046	−0.003	0.124	0.159	No

***Significant at p < 0.10

Table 6. Total indirect effects

Path	Coefficient	Confidence interval bias corrected		P Values
		2.5%	97.5%		Significant
TIA->BP	0.210	0.060	0.387	0.016	Yes**
OA->BP	0.013	−0.064	0.120	0.771	No
OS->BP	0.103	0.012	0.857	0.019	Yes**

**Significant at p < 0.05

Figure 3. Structural model

As shown by Table 3, the values of R² and Q² of the target-dependent variable and the mediating factors suggest that the model provides an adequate explanation, and its predictive power is meaningful. The values of variance inflation factors (VIF) are well below 3, suggesting the absence of a multicollinearity problem (Hair et al., 2019). Each of the hypotheses was assessed by checking the significance of the corresponding path coefficient, as shown by Table 4. The first hypothesis stated that the perception of the threat of informal actors is positively associated with the formal actor’s efforts to improve service quality. This hypothesis is supported as the path between the constructs perceived threat of informal actors (TIA) and service quality efforts (SQ) is positive and significant at p < 0.01. The second hypothesis stated that the perception of the threat of informal actors is positively associated with the formal actor’s efforts to acquire customers. This hypothesis is supported as the path between the constructs perceived threat of informal actors and customer acquisition efforts (CAE) is positive and significant at p < 0.01. The third hypotheses stated that the service quality of the formal actors is positively associated with their business performance. This hypothesis is supported as the path between the constructs service quality efforts (SQ), and business performance (BP) is positive and significant at p < 0.01. The fourth hypothesis stated that customer acquisition efforts of the formal actors are positively associated with their business performance. This hypothesis is also supported as the path between the constructs customer acquisition efforts (CAE), and business performance (BP) is positive and significant at p < 0.05.

The analysis of the specific and total indirect effects showed that the perceived threat of informal actors has a significant indirect effect on formal actor’s business performance through its association with service quality and customer acquisition efforts. Interestingly, the direct effect of the perceived threat of informal actors on formal actors’ business performance is negative (TIA->BP, −0.080). However, since this path is not significant (p = 0.426), our results suggest the presence of indirect-only mediation effects between the perceived threat of informal actors and formal actor’s business performance. . Regarding potential alternative explanations, results in Table 4 show that only operator size has a significant association with service quality, customer acquisition efforts and business performance.

Discussion

This study set out to tests a conceptual model on the mechanism through which a perceived threat of informal actors in the tourism industry affects the business performance of formal actors. To the best of our knowledge, it is the first study to explore such a mechanism. The findings support our hypotheses and therefore confirm the relevance of theoretical insights that guided their formulation. Hence, the study has provided valuable insights that form the basis for actionable managerial and policy recommendations, as discussed below.

Theoretically, this study relied on activity-based view and perspectives related to strategic responses to competitive pressure. Two sets of activities, service quality and customer acquisition efforts, were considered to constitute the strategic response of the formal actors to the competitive threat of informal actors. Our analysis shows that the threat of informal actors is significantly associated with formal actors’ service quality and customer acquisition efforts, which are subsequently related to increased business performance. With these findings, it may be tempting to rush and conclude that the presence of informal actors is beneficial for formal actors. However, one must note that our further analysis revealed an indirect-only positive effect of the threat of informal actors on business performance. Therefore, the threat of informal actors is only positively associated with an increase in the business performance of formal actors through its positive association with service quality and customer acquisition efforts. The implication for managers is that informal actors can negatively affect formal actors who do not consider improving their service quality and customer acquisition efforts. A possible explanation is that ignoring these two aspects will make formal actors compete with their informal counterparts based on price, a battle that they are likely to lose because of their relatively higher operational costs. As such, managers of formalised tourism firms should focus on service improvement measures (such as hiring competent staff and offering unique value propositions) and customer acquisition efforts (such as using social media to reach out to existing and potential clients, search engine optimisation, attending trade fairs and roadshows). As expected, we found that operator size is significantly associated with service quality, customer acquisition efforts, and business performance. This finding is in line with our argument that larger operators are likely to have more resources to support service quality and customer acquisition efforts, which are subsequently associated with increased business performance. However, considering the significance of service quality and customer acquisition efforts, we recommend that smaller operators should find cheaper ways to engage in these activities. For instance, they may optimise their websites to increase visibility on search engines and be active on relevant social media platforms such as Instagram.

As digital technologies continue to lower entry barriers for various service industries, the threat of informal actors is becoming prominent. These technologies are the engine behind sharing economy platforms, numerous service applications and social networking sites. Indeed, due to the proliferation of digital technologies, it has never been easier for individuals to start and deliver small-scale service offerings. In the context of travel and tourism, an example can be an individual who is empowered by digital platforms to use their extra bedroom to offer accommodation service or use their private car to provide transfer and tour guiding service. While the incidence of informal actors is primarily prevalent in developing countries, digital platforms also empower individuals in developed countries. Since the penetration of digital technologies is far deeper in the developed countries, the potential for empowering individuals to offer services is even higher in these countries. It means that established firms within the tourism industry in the developed countries could face a similar threat that formal actors in developing countries face from informal actors. By interpolation, the results of our analysis suggest that established firms facing such a threat can defend themselves by focusing on offering unique and high-quality services, along with intensifying their customer acquisition efforts.

Our analysis also provides relevant insights in terms of policy and regulatory intervention. As the results suggest that the threat of informal actors is associated with an increase in formal actors’ service quality efforts, it is plausible to argue that their presence may subsequently contribute to the destination’s attractiveness due to the rise in service quality. However, it should be clear that we do not condone informal actors. Instead, we argue that regulators should strive to eliminate the barriers that discourage individuals and entities from formalising their operations. As noted in the introduction section, the informal sector is quite prevalent in developing countries. These countries also tend to have poor conditions for doing business. For instance, the research setting of this study – Tanzania – is ranked 141 in the latest Ease of Doing Business rankings of the World Bank, scoring relatively low on aspects such as easiness of starting a business, ease of registering property, protection of minority investors and the tax system (World Bank, 2020). Therefore, we argue that regulatory authorities in these countries should create a conducive atmosphere for informal actors to formalise their operations, which will increase competitiveness among service providers in the tourism destinations and eventually increase the reputation of the destination through improved service quality.

In addition, given that informal actors are more likely to prevail in places characterised by weak institutions, the informality is lesser in countries with fewer regulatory loopholes. Therefore, informal actors usually calculate the costs and rewards of operating informally (Williams & Bezeredi, 2020). For instance, an individual would not engage in the grey economy if the punishment for doing so is severe. As such, the regulatory authorities should introduce and enforce strict rules prohibiting the prevalence of informal activities. Presumably, strict rules and harsh punishments to restrict the grey economy are likely to discourage informal actors. Moreover, the regulatory authorities may prepare unique programmes to educate informal actors on the consequences of operating informally and guide them on how to formalise their businesses.

Concluding remarks

This study has provided valuable insights into the mechanism through which informal actors affect formal actors in the tourism industry contexts. Our findings are relevant for both regulators and managers of formalised tourism businesses. Although our data were collected right before the outbreak of the COVID-19 pandemic, the insights provided by our results will be relevant even for the post-pandemic business environment. Considering the economic consequences of the pandemic and the discretionary nature of tourism products, post-pandemic customers are likely to be even more sensitive to prices, quality, and safety measures. This means that formal actors will face increased difficulty balancing the costs of financing efforts related to these aspects and the return on the investment. Since these activities will still be critical for their business performance, managers of tourism businesses should systematically find out which specific service quality and customer acquisition interventions have a higher impact and prioritise them accordingly.

Finally, it is worth noting that our research has some limitations that provide avenues for future research. First, our analysis is based on data collected from operators in one destination. Future research can consider replicating this in other destinations. Second, this study did not investigate the costs and benefits of specific service quality and customer acquisition efforts. Future studies may consider such analysis and thus help managers of tourism businesses make optimal choices. Lastly, given the objective of our research, it was adequate to collect data only from the formal actors. However, to extend this stream of research, future research may collect data from informal actors in the tourism industry and explore other relevant issues such as their growth trajectories.

Disclosure statement

No potential conflict of interest was reported by the author(s).