Effect of hotel overall service quality on customers’ attitudinal and behavioural loyalty: perspectives from Zimbabwe

Brighton Nyagadza (Department of Marketing, Marondera University of Agricultural Sciences and Technology, Marondera, Zimbabwe)
Gideon Mazuruse (Institute of Teaching and Learning, Marondera University of Agricultural Sciences and Technology, Marondera, Zimbabwe)
Asphat Muposhi (Department of Information and Marketing Sciences, Midlands State University, Gweru, Zimbabwe)
Farai Chigora (College of Business, Peace Leadership and Governance, Africa University, Mutare, Zimbabwe)

Tourism Critiques

ISSN: 2633-1225

Article publication date: 12 May 2022

Issue publication date: 26 August 2022

5918

Abstract

Purpose

This study aims to examine the influence of service quality, satisfaction, trust, value and commitment on hotel customers’ attitudinal and behavioural loyalty.

Keywords

Citation

Nyagadza, B., Mazuruse, G., Muposhi, A. and Chigora, F. (2022), "Effect of hotel overall service quality on customers’ attitudinal and behavioural loyalty: perspectives from Zimbabwe", Tourism Critiques, Vol. 3 No. 1, pp. 42-71. https://doi.org/10.1108/TRC-12-2021-0026

Publisher

:

Emerald Publishing Limited

Copyright © 2022, Brighton Nyagadza, Gideon Mazuruse, Asphat Muposhi and Farai Chigora.

License

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode


1. Introduction and contextualisation

With the intense competition characterising the hospitality and leisure sector, understanding the antecedents of overall service quality and their effect on customers’ attitudinal and behavioural loyalty is very important (Chikazhe et al., 2021). This is particularly important in the hospitality service sector due to low customer switching costs (Sardana and Bajpai, 2020). Switching costs are the costs hotel customers pay as a result of switching brands or products, which can be monetary, psychological, effort-based and time-based (Lam et al., 2004). In the hospitality sector, there are lower switching costs due to the high overall service quality offered by hotels (Matzler et al., 2015), which reduces the probability of customers to switch from one hotel to the other rampantly (Yang and Peterson, 2004). The factors which affect customers’ attitudinal and behavioural loyalty, such as service quality, satisfaction, trust, value and commitment, are expected to enhance service excellence and customer retention (Ashraf et al., 2018). Under normal operating conditions, hotels following a customer-centric approach are expected to be able to attract, retain and build long-term relationships with customers (Ngo and Nguyen, 2016), which, in turn, translate into sustainable competitive advantage (Hosseini and Saravi-Moghadam, 2017). Engendering customer loyalty is critical for all businesses including hotels, due to the high cost associated with attracting new customers as opposed to retaining the existing ones (Thaichon and Quach, 2015).

The Zimbabwean hospitality service sector, with specific reference to Harare as the capital city (Harare City, 2022), is operating with a philosophy that mirrors the other developing countries. It aims to create memorable customer experiences by offering and incubating competitive hospitality products and services which enhance customers’ attitudinal and behavioural loyalty. Towards this end, variation of hospitality services is very key due to the heterogeneity of customer needs and service level expectations. Zimbabwe, as a tourism destination, has experienced numerous operating challenges induced by its socio-economic and political state of affairs (Klimek, 2013). Despite the cutthroat competition in the marketplace and position, Harare in Mashonaland East province of Zimbabwe remains a tourist destination of choice for sterling hospitality overall service quality.

Although prior research studies (Chikazhe et al., 2021; Dabholkar et al., 2000) have investigated service quality effect in hospitality and tourism, they were not conclusive on key constructs that influence attitudinal and behavioural loyalty. Uniqueness of the current study is that it contributes to efforts to bridge that knowledge gap by tbe examining influence of overall service quality on customers’ attitudinal and behavioural loyalty in the hotel service sector in Zimbabwe. In particular, this study sets out to address the following main objectives:

  • to explore customers’ evaluation of overall service quality in hotels’ context; and

  • to examine its (overall service quality) influence on hotel customers’ attitudinal and behavioural loyalty.

The remainder of this research article is structured as follows: literature review (Section 2), hypotheses and research conceptual model development (Section 2.3), this is followed by on methodological delineations (Section 3), then analysis of results (Section 4), the discussion (section 5), conclusion (Section 6), theoretical implications (Section 7), practical implications (Section 8), and finally study limitations and future research implications (Section 9) are presented.

2. Literature review

The organisation approach of literature in the current study follows a critical review, where the literature has been extensively researched, and its quality is critically evaluated (Grant and Booth, 2009). The aim of using this literature organisation approach is to produce the highest degree of thorough analysis, hypotheses development, conceptual model innovation and subsequent testing.

2.1 Model grounding the study

Parasuraman et al. (1988) managed to develop the SERVQUAL model, with the most widely used scales that have been applied for the measurement of service quality in the past recent years by many scholars. SERVQUAL model has five dimensions, namely, tangibles, reliability, assurance, responsiveness and empathy (Parasuraman et al., 1988). With regard to the tourism and hospitality industry, many scholars have modified the model and adapted it to the qualities of services provided (Blesic et al., 2011). Due to this, overall service quality concept has been used interchangeably (Reeves and Bednar, 1994), extremely endured pursuit by academics and has come to be a major business organisation issue of concern. However, the criticisms laid against SERVQUAL model are found in Buttle (1996) and Ladhari (2008). Its usage is still pervasive in nature (Adetunji et al., 2013; Attallah, 2015), and there is a general consensus that SERVQUAL items are the best predictors of overall service quality as applied in the current research study. In complement to the criticisms raised in the prior research upon application of SERVQUAL model, Cronin and Taylor (1992) have developed the SERVPERF performance based model, which proved to be better after its application in four different service industries. However, the current research applied SERVQUAL model due to its alignment to the variables developed in the conceptual framework. The scale has been extensively used in many studies and has formed the base for specific industries such as LODGSERV, DINESERV, SITEQUAL and SELEB (Rajeswari et al., 2017). On the other hand, there is no concurrence over on set of universal service quality dimensions that could be generalised across all industries (Dabholkar et al., 2000; Duggal and Verma, 2018).

The purpose of the research study feeds into the exploration of the theoretical and/or conceptual structure in line with overall service quality and its influence on hotel customers’ behavioural and attitudinal loyalty. Prior research studies have depicted that there are many factors which influence behavioural and attitudinal loyalty of customers and overall service quality remains the dominant one. Empirically, there are several research studies that have indicated that greater degree of customer satisfaction as a result of positive overall service quality can drive customer satisfaction (Dabholkar et al., 2000; Kushwana et al., 2013; Singh and Sirdeshmukh, 2000; Seto-Pamies, 2012).

In addition to this, other inquiries have depicted that there is an existence of a nexus between overall service quality and behavioural and attitudinal loyalty of customers (Brady and Cronin, 2001). A direct effect in this relationship has been unearthed (Boohene and Agyap, 2010). However, other studies have depicted that overall service quality and behavioural and attitudinal loyalty of customers need not to always be through a hierarchical relationship and can be moderating factors (Roberts et al., 2003; Zeithamal et al., 1996). The five dimensions of overall service quality of SERVQUAL define the basic concepts which make overall service quality (Groonros, 1984; Roest and Pieters, 1997; Parasuman et al., 1988). However, the importance is the development of an understanding of what specific containment in each of the elements is if the excellence of service is to be achieved (Brady and Cronin, 2001; Boulding et al., 1993).

2.2 Overall service quality

The idea behind overall service quality is that it depicts the gap that exists between customer expectations and actual service performance (Liu et al., 2020; Parasuraman et al., 1988). Overall service quality needs to match customers’ expectations (Lovelock and Wright, 2002). It results from a comparison that is between service perception and the expectations of the customers (Cronin and Taylor, 1992; Groonros, 2010). Overall service quality represents a good source of competitive advantage within the service industry like the hotel and hospitality (Rahman et al., 2020; Woratschek et al., 2020). Furthermore, overall service quality and reliability of products of an organisation create a competitive advantage (Bahadur et al., 2018; Iqbal et al., 2018). Customers’ attitudinal and behavioural loyalty, as well as satisfaction, are yields of continuous improvement (Chongsanguan et al., 2016; Junior and de Aquino Guimarães, 2012). Repeat purchases of service probability by hotel customers are as a result of overall service quality satisfaction (Liu et al., 2020). Hotel competitors are differentiated among other players as a result of superior service quality (El Essawi and El Aziz, 2012).

2.3 Hypotheses development and conceptual modelling

2.3.1 Overall service quality, customers’ trust, satisfaction, value and commitment.

Trust levels have been operationalised in prior research as the customers’ integrity, benevolence and ability in relation to the perception of hotel’s overall service quality. Intention to try hotel overall service can be defined as the customers’ subjective probability that they will perform an actual behaviour (Bae, 2018; Cheng and Jiang, 2020). The intention to try hotel service is highly related to the trust that customers place on them (Alalwan et al., 2018). Further to this, trust and intention to try hotel service is connected to the level of customers’ loyalty to a given corporate brand of the hotel (Nyagadza et al., 2021) and associated satisfaction levels (Papacharissi and Mendelson, 2011). This is so because the intention to try overall hotel service is positively related with hotel customers’ satisfaction and trust (Oliver, 1997). Bold associations exist between using new hotel technology such as smart mobile phone service, chatbots, micro-vlogs, microblogs (Nyagadza, 2020) and user trust.

In line with the above, customer satisfaction is a consequence of the comparison between the expected hotel service or product performance, product brand performance (Nyagadza et al., 2021) and comparison of standard type of customer expectations (Smith et al., 1999). For customers and other stakeholders to be able to evaluate the level of satisfaction in the overall hotel service offered via various platforms including digital ones (Nyagadza et al., 2021) and emotions play an important role (Bagozzi et al., 1999). However, only a few studies have managed to consider hotel service failure and hotel service recovery in connection with attitudinal and behavioural loyalty (Menon and Dube, 2000; Zeelenberg and Pieters, 2004). When hotel customers experience negative effects, they are exposed to lesser satisfaction than those who have little to no emotions (Oliver, 1997). This is due to the fact that customer satisfaction in this context has two psychological components, which are cognitive and affective.

For this current research study, customer value can be defined as the preferences of customers related to their assessment of the hotel products’ qualities and their performance (Neupane et al., 2021). Customer satisfaction is shaped by the creation of customer value proposition, which is meant for the proposition of value (Saarijarvi, 2012; Murphy, 2017). Customer purchase intention of hotel services or products is necessitated through customer value proposition (Kim and Mauborgne, 2015). The key values and/or factors which influence the customer value proposition are important elements which enhance overall hotel’s service quality (Alalwan et al., 2018). Most hotels and hospitality organisations make more investments in these key values to enhance their space in staying ahead of the competitors (Thaichon and Quach, 2015; Pew Research Center, 2019). Customer commitment and trust levels have been operationalised in prior research (Alalwan et al., 2018) as the customers’ integrity, benevolence and ability in relation to the perception of overall hotel’s service quality. Customer commitment can be viewed as the person’s subjective probability state or quality of being dedicated to the overall service quality of a hotel (Bae, 2018; Cheng and Jiang, 2020; CGS, 2019). The intention to enjoy offered hotel service is highly related to the trust that customers place on them in transacting and receiving envisaged hospitality services. Further to this, commitment and trust in overall hotel service quality are both connected to the level of loyalty to a given corporate brand of a hotel and associated customer satisfaction levels (Papacharissi and Mendelson, 2011; Arnold, 2018). This is so because the intention to enjoy hotel service is positively related to satisfaction and trust (Bae, 2018). Bold associations exist between using hotel services and products and customer commitment. Based on this research evidence in literature, we hypothesise that:

H0.

Overall hotel’s service quality negatively affects (a) customers’ trust, (b) customers’ satisfaction, (c) customers’ value and (d) customers’ commitment.

H1.

Overall hotel’s service quality positively affects (a) customers’ trust, (b) customers’ satisfaction, (c) customers’ value and (d) customers’ commitment.

2.3.2 Customers’ trust, satisfaction, value and attitudinal loyalty.

Attitudinal loyalty is directly related to hotel customers’ attachment to inner thoughts, as well as word of mouth, which is positive and constructive recommendations (Zeithaml et al., 1996). This is due to the fact that trust plays a crucial role in determining attitudinal loyalty (Alalwan et al., 2018). On the other hand, behavioural loyalty is built through evaluating whether customers remain with the same hotel in the future (Worthington et al., 2010). This argument, however, requires further empirical support in Zimbabwe, a country is known for actively promoting gender equality, and traditional beliefs conservatism is linked to age and gender. However, it is not known whether this translates to the customers’ attitudinal and behavioural loyalty to hotel’s overall service quality (Murphy, 2017).

Attitudinal loyalty focuses on the cognitive basis of loyalty and isolates the purchase of services motivated by a strong attitude from other purchases due to constraints within a situation. Further to this, attitudinal loyalty can be seen as the extent of customer’s psychological attachments and attitudinal advocacy towards the organisation (Jaiswal and Niraj, 2011). The probability of switching for these hotel customers can be low if the level of attitudinal loyalty of customers is also high (Dube and Maute, 1996). Furthermore, the perception of the customers in terms of overall hotel service quality is a major driver of attitudinal loyalty (Zeithaml and Bitner, 2000) and one of its major constructs (Taylor et al., 1992; Fornell, 1992). For customers and other stakeholders to be able to evaluate the level of satisfaction in the overall hotel service offered via various platforms including digital ones (Nyagadza et al., 2021), emotions and attitudinal loyalty play an important role (Bagozzi et al., 1999). However, only a few studies have managed to consider hotel service failure and hotel service recovery in connection with emotions (Menon and Dube, 2000; Zeelenberg and Pieters, 2004).

Commitment and trust in overall hotel service quality are both connected to the level of loyalty to a given hotel corporate brand (Alalwan et al., 2018) and associated customer satisfaction levels. Whenever customers are satisfied with the quality of the service of any given hotel, it leads to the development high customer attitudinal and behavioural loyalty (Fornell, 1992; Sivadas and Baker-Prewitt, 2000). This is supported by Oliver (1997) who proposed that if an organisation is able to satisfy the basic wants of its customers such as the ones for hotels, it is very easy task to yield customer’s positive behavioural and attitudinal loyalty to the overall service offered to them. This is so because the intention to enjoy hotel service is positively related with satisfaction and trust (Bae, 2018). Bold associations exist between using hotel services and products (Alalwan et al., 2018) and customer commitment. Therefore, we propose:

H0.

(a) Customers’ trust, (b) customers’ satisfaction, (c) customers’ value and (d) customers’ commitment negatively affect attitudinal loyalty.

H2.

(a) Customers’ trust, (b) customers’ satisfaction, (c) customers’ value and (d) customers’ commitment positively affect attitudinal loyalty.

2.3.3 Customers’ trust, satisfaction, value, commitment and behavioural loyalty.

Long-term relationships with service providers in tourism and hospitality organisations such as hotels (Singh and Sirdeshmukh, 2000) hold the major key success factors meant for sustainability establishment (Seto-Pamies, 2012). There are several research studies with application of the SERVQUAL model that have indicated that greater degree of customer trust as a result of positive overall service quality can drive customer behavioural loyalty (Dabholkar et al., 2000; Kushwana et al., 2013). For customers and other stakeholders to be able to evaluate the level of satisfaction in the overall hotel service offered via various platforms including digital ones (Nyagadza et al., 2021), emotions play an important role (Bagozzi et al., 1999). Customer satisfaction is a consequence of the comparison between the expected hotel service or product performance, product brand performance (Nyagadza et al., 2021) and comparison standard type of customer expectations (Smith et al., 1999). Other research inquiries have indicated that greater degree of customer satisfaction as a result of positive overall service quality can drive customer satisfaction (Dabholkar et al., 2000; Kushwana et al., 2013). In addition to this, some have depicted that there is an existence of a nexus between overall service quality and behavioural and attitudinal loyalty of customers (Brady and Cronin, 2001).

Behavioural loyalty is built through evaluating whether customers remain with the same hotel in the future (Worthington et al., 2010). Commitment and trust in overall hotel service quality are both connected to the level of loyalty to a given corporate brand of hotel and associated customer satisfaction levels (Papacharissi and Rubin, 2010). When customers enjoy the overall service quality, they believe that they have made the correct decisions. This is because the overall service quality of hotels have been found to be with direct effect on customer trust, customer satisfaction and behavioural loyalty (Liu et al., 2020), as supported in the SERVQUAL model. Customer perceived value and trust levels have been operationalised in prior research (Alalwan et al., 2018) as the customers’ integrity, benevolence and ability in relation to the perception of overall hotel’s service quality. However, other studies have depicted that overall service quality and behavioural and attitudinal loyalty of customers need not to always be through a hierarchical relationship and can be moderating factors (Roberts et al., 2003; Zeithamal et al., 1996). Therefore, we propose:

H0.

(a) Customers’ trust, (b) customers’ satisfaction, (c) customers’ value and (d) customers’ commitment negatively affect behavioural loyalty.

H3.

(a) Customers’ trust, (b) customers’ satisfaction, (c) customers’ value and (d) customers’ commitment positively affect behavioural loyalty.

Based on the theoretical and literature review and posited hypotheses, the conceptual model supporting this study is illustrated in Figure 1.

3. Methodology

The research design, data sources and collection strategy, sample, design of the questionnaire and measures applied in the research are explained in this section.

3.1 Study design

The research followed a positivist research philosophy and quantitative methodology to examine the causal effect of hotel’s overall service quality on customers’ attitudinal and behavioural loyalty. Due to objective nature of the research study, deductive logic and approach were applied to test the SERVQUAL model’s theoretical application (Parasuraman et al., 1988) after practical statistical inferences. On the condition of nomothetic quantitative methodology, the researchers applied cross-sectional time horizon due to the fact that the research was limited to a specific time frame. Time horizons are needed for the research design independent of the research methodology used (Saunders et al., 2009).

3.2 Data sources

Data were collected primarily from the field through the distribution of self-administered structured questionnaires to the hotel customers. The survey method was used to collect data with an intention to validate the research model. All hotel customers (including non-customers) could access the questionnaire and respond to it. Customers who paid for services from the selected five hotels were qualified to participate in the study based on their most recent hotel service experiences. However, the customers who used other services of the hotels were excluded from the current study.

3.3 Study area characteristics

The geographical location of the study area was in the city of Harare, formerly Salisbury (now commonly known as “Sunshine City”), under Mashonaland East province of Zimbabwe, where five hotels were selected (Harare City, 2022). Justification for the five selected hotels in the city of Harare was due to the fact that it is the epicentre of business and administrative activities, with all year round good weather attractive to tourists (Harare City, 2022). Further to this, the selected five hotels include the top, middle and lower tiers in terms of service quality ranking so as to gather mean survey responses for conclusive research results.

3.4 Data collection strategy, population, sampling and sample size

The researchers divided the population of 600 potential respondents into more relevant and significant strata (Muposhi et al., 2021) based on subsets where a random sample was drawn from each of the strata (Saunders et al., 2009), such as the customers’ profiles (low, middle and high income earning capacities), as well as the geographical locations (local, regional and international) to which they belong to. Stratified random sampling technique was applied due to its accuracy and easy-to-use advantages (Saunders et al., 2009). To determine the sample size, Krejcie and Morgan 1970 formula was applied, necessary to construct a confidence interval (generally +5%) (Alalwan et al., 2018). A total of 234 questionnaires were distributed through the Web-based survey method to hotel customers in Harare, Mashonaland East province of Zimbabwe, where the five selected hotels are geographically located. The research study applied an online Web-based cross-sectional survey with the aid of 20 fieldworkers. Participation was voluntary, and the objectives of the study were explained to the participants in the research study before completing the questionnaire (Nyagadza, 2019). The researchers collected the data from April 2021 to June 2021. Stretching of the data collection period was as a result of COVID-19 restrictions, which caused some bottlenecks in the whole process. A total of 219 questionnaires were returned, and 205 valid responses were considered for analysis, translating to a response rate of 88%. Pilot study was conducted on the respondents using stratified random sampling from the five selected hotels. These respondents represented the recommended 5% of the research study sample. To complete the questionnaire, the respondents took about 20 min on average. Women dominated men in the survey. Majority of the respondents (69.2%) were aged between 20 and 39 years. Most of the respondents (67.2%) had already earned at least a bachelor’s degree. Majority of the respondents (84.4%) were earning less than US$1,500 per month. The sample constituted mixed nationalities due to the fact that hotels may include visitors from various countries and majority being locals. Hotels that participated in the study were mainly four- and five-star ranking due to accessibility issues. Questionnaires for the study were distributed via electronic means through Web-based survey means during the process of service delivery in the selected five hotels.

3.5 Research instrument

Study constructs were measured using item scales adapted from literature, specifically from prior research studies, which were in line with hotel overall service quality, behavioural and attitudinal loyalty. Likert scale used was with a range of Strongly Disagree (SD) = 1 to Strongly Agree (SA) = 5. The main importance of the Likert scale questions to statistical community is that they use a universal method of collecting data, which means it is easy to understand them and easy to draw conclusions, reports, results and graphs from the responses. The sources of constructs in the questionnaire (Table A1, Appendix 1) include Blesic et al. (2011), Reeves and Bednar (1994), Nelson et al. (2005), Buttle (1996), Ladhari (2008), Adetunji et al. (2013), Attallah (2015) and Cronin and Taylor (1992). Measurement instrument variables were subjected to examination via confirmatory factor analysis (CFA).

3.6 Reliability and validity

Reliability of each factor in the instrument was tested using Cronbach’s alpha (α) (Malhotra, 2010). Each value was required to be at least 0.5, as this is suggested to be a sufficient reliability score by Churchill (1979). Internal consistency was meant to measure the degree of interrelatedness of measurement items that were constructed to assess the uniformity (Thaichon and Quach, 2015). To assess validity, content, discriminant and predictive validities were tested (Nyagadza et al., 2021). The researchers used content validity to look into the fitness and link of the research subjects to the theoretical underpinnings (Malhotra, 2010). Furthermore, the researchers used pretesting and pilot approaches to enhance research instrument’s content validity. The concept of construct validity used was made to check on the connections between items that were assessed (Liu et al., 2020) and the concept under study (Malhotra, 2010). To assess construct validity, average inter-item correlations were computed using CFA (Adetunji et al., 2013; Attallah, 2015). To establish discriminant validity of the measurement model, the researchers used Fornell and Larcker’s (1981) measure of average variance extracted (AVE). All the factor loadings that were above 0.5 were considered (Fornell and Larcker, 1981).

3.7 Non-response bias test

Armstrong and Overton’s (1977) technique was used to check for non-response bias tests. The process involved the use of t-tests to compare the means of each of the items of the succeeding responses against the rest of the responses. There were no larger differences in the means. This suggests that non-response bias was not a threat to the research study.

3.8 Data analysis

Both descriptive and inferential statistics were used in analysing quantitative data from the questionnaire. Structural equation modelling (SEM) was used to test the posited hypotheses. Descriptive statistical analysis was achieved through the functional application of charts, tables, graphs and diagrams, and this fed into inferential statistics (Attallah, 2015). These included frequencies, mean and standard deviation. Software packages used for data visualisation were Smart PLS and SPSS, version 3 and version 25, respectively. Exploratory factor analysis (EFA) was used to identify the underlying relationships between the variables measured (Gerald, 2018). Chan and Idris (2017) advise researchers to carry out an EFA at the beginning of data analysis as part of scale validation. Keller and Kros (2011) postulate that EFA is used to measure the dimensionality of a survey, to recognize precarious and non-critical items (Attallah, 2015), to decrease the quantity of items and to re-examine the content of the factor. Effendi et al. (2019) consent that EFA help researchers who do not know how many factors which explain the interrelationship among a set of items (Maat et al., 2011). EFA was performed so as to refine and decrease the number of related variables to a more relevant (Keller and Kros, 2011) and manageable number prior to using them for further analysis (Alexander et al., 2016). To assess adequacy of the measurement model, the researchers applied CFA (Worthington et al., 2010). The researchers also used principal component analysis to consider the total variance in the data (Muposhi et al., 2021) and establish minimum number of factors that will account for the maximum variance (Da Costa Carvalho, 2015). In addition, Bartlett’s test of sphericity was applied to examine the hypothesis that the variables were uncorrelated (Alalwan et al., 2018). It was used to see whether there were some relationships between variables, which is necessary for factor analysis to be appropriate (Field et al., 2012).

3.9 Ethical considerations

Ethical considerations related to participating hotel customers’ privacy, informed consent, freedom of response, professionalism, integrity, accuracy (Blesic et al., 2011) and values of research have been adhered to by the researchers, in line with the provisions made by the marketing research society Marketing Research Society (MRS) (2022). Due to this, the researchers were obliged to observe the practices that take note of the values (Alexander et al., 2016) and the integrity of research by not making manipulations to ethical issues (Muposhi et al., 2021). They made sure that they upheld ethical considerations by maintaining integrity and professionalism about the morals of academic research (Nyagadza et al., 2021).

4. Results

The charts, tables, graphs and diagrams have been fed into inferential statistics so as to draw conclusions from a sample and generalise them to a population after having confidence that the sample accurately reflects the research population. The next section discusses factor analysis.

4.1 Exploratory factor analysis

EFA was used by researchers to discover the number of factors influencing the variables under investigation (Effendi et al., 2019), and it allowed analysis of the variables that were correlated (Hair et al., 2013). Varimax (orthogonal rotation) with principal axis factoring (PAF) on 23 items was used. Orthogonal rotation states that there is no correlation between the resulted components or factors (Tabachnick and Fidell, 2007).

Table 1 summarises the results of the rotated factor matrix (RFM) for each variable, where two items with factor loadings <0.60 were removed one by one with re-running the analysis for that specific variable (Tabachnick and Fidell, 2007). Among all, only 21 items were retained for further analysis with no cross-loadings >75% on any other item, and the eigenvalues of one were opted to extract the number of factors (Field et al., 2012).

4.2 Kaiser–Meyer–Olkin and Bartlett’s test

Results showed that the data was normally distributed; hence further analysis can be done with χ2 (28) = 2,514.301, p < 0.05 as shown in Table 2.

4.3 Reliability analysis

For overall service quality, it was given as 0.814, and for customer value indicator, Cronbach’s alpha (α) of 0.809 was produced, while Cronbach’s alpha (α) of 0.826, 0.909, 0.887, 0.876 and 0.829 were obtained for customer commitment, customer satisfaction, customer trust, attitudinal loyalty and behavioural loyalty, respectively (as shown in Table 3). These values were above the threshold of 0.7, indicating that all the constructs are internally consistent and reliable to be used as a measurement.

4.4 Correlation analysis

Table 4 gives the inter-item correlation estimates: attitudinal loyal and behavioural loyal (r = 0.729), customer commitment and attitudinal loyal (r = 0.730), customer trust and attitudinal loyal (r = 0.873), customer trust and behavioural loyal (r = 0.801), customer trust and customer commitment (r = 0.805), customer satisfaction and attitudinal loyal (r = 0.876), customer satisfaction and behavioural loyal (r = 0.901), customer satisfaction and customer commitment (r = 0.843), customer satisfaction and customer trust (r = 0.814), customer value and attitudinal loyal (r = 0.718), customer value and behavioural loyal (r = 0.770), customer value and customer commitment (r = 0.803), customer value and customer trust (r = 0.749), customer value and customer satisfaction (r = 0.835), overall service quality and attitudinal loyal (r = 0.648), overall service quality and behavioural loyal (r = 0.703), overall service quality and customer commitment (r = 0.686), overall service quality and customer trust (r = 0.705), overall service quality and customer satisfaction (r = 0.820) and overall service quality and customer value (r = 0.619). In conclusion, the relationship between the variables is classified as moderate to very strong.

4.5 Convergent validity

From the results displayed in Table 5, there are AVE values for overall service quality (0.705), customer value (0.751), customer commitment (0.724), customer satisfaction (0.822), customer trust (0.703), attitudinal loyalty (0.840) and behavioural loyalty (0.676). The AVE values for convergent validity across constructs ranged between 0.528 and 0.699 (>0.50), showing that the indicators assumed to measure the same construct adequately. All the constructs passed the convergent validity assessment.

The following formulae were used to calculate the critical ratios (CR) and AVE, respectively.

(1) CR=(F)2[(F)2+ (1F2)]
(2) AVE=Fi2n
where:

F = standardised factor loading

N = number of items

4.6 Discriminant validity

AVE were compared with squared inter-construct correlations in a bid to assess discriminant validity.

The Fornell–Larcker criterion results presented in Table 6 inform that the seven constructs, respectively, had square roots of AVE: 0.917, 0.822, 0.851, 0.838, 0.907, 0.867 and 0.840. The four latent constructs had met the criteria of discriminant validity.

4.7 Hypotheses testing

The confidence interval also confirms the significance of the paths in the model as indicated in Table 7. To test the structural relationships hypothesised in the research model (Figures A1 and A2 in Appendices 2 and 3), SEM was applied in SmartPLS v 3.

From the results displayed in Table 7, all the null hypotheses were rejected. Testing of H1(a) gave a result of overall service quality significantly affecting customer trust (β = 0.882, t = 10.457, p = 0.000). H1(b) indicated that overall service quality significantly affect customer satisfaction (β = 0.874, t = 9.304, p = 0.000). In line with this, H1(c) showed that overall service quality significantly affect customer value (β = 0.804, t = 8.561, p = 0.000). The testing of H1(d) depicted that overall service quality significantly affect customer commitment (β = 0.815, t = 9.104, p = 0.000). H2(a) concluded that customer trust significantly affect attitudinal loyalty (β = 0.010, t = 2.113, p = 0.023). The H2(b) clearly depicted that positive customers’ satisfaction positively affects attitudinal loyalty. H2(c) testing showed the result that customer commitment significantly affect attitudinal loyalty (β = 0.612, t = 8.014, p = 0.000). As well as H2(d) came out indicating positive customers’ value positively affects attitudinal loyalty (β = 0.129, t = 3.084, p = 0.011. Testing of H3(a) showed that customer trust significantly affects behavioural loyalty (β = 0.200, t = 4.341, p = 0.0003). Positive customers’ satisfaction positively affect behavioural loyalty [H3(b)]. Testing of H3(c) depicted a result that customer commitment significantly affect behavioural loyalty (β = 0.1999, t = 0.199, p = 0.007). Testing of H3(d) depicted a result that customer value significantly affect behavioural loyalty (β = 0.085, t = 2.930, p = 0.015).

4.8 Testing of mediation effect

Mediation analysis was done using Sobel’s test in this study since there was a third variable (mediator) between the two variables. Sobel’s test uses the product of coefficients. The results are presented in Table 8.

Taking, for example, the path OSQCTAL, the results are the product of 0.010 and 0.882, which are beta values for OSQCT and CTAL, respectively. Thus, with 0.01 × 0.882, we get 0.009. According to the results in Table 8, the relationship between OSQ and AL is significantly mediated by CT (β = 0.009, p < 0.001), the relationship between OSQ and BL is significantly mediated by CT (β = 0.176, p < 0.001), the relationship between OSQ and AL is significantly mediated by CST (β = 0.120, p < 0.001). From the analysis, it shows a direct and an indirect relationship, implying that there was no change in terms of the significance of the constructs. The only notable change was the reduction in the beta (β) value, and this indicates the existence of a partial mediation (Effendi et al., 2019).

4.9 Evaluation of the structural model

The developed model has a moderate explaining power. An effect size f 2 ≤ 0.30, 0.3 < f 2 ≤ 0.50 and f2 > 0.50 is thought to represent a weak, moderate and strong effect, respectively. The effect size in Table 9 calculated from the research model shows a moderate-to-strong effect is moderate.

In addition to R2 as a predictive criterion, Hair et al. (2013) recommended that researchers examine Q2 to assess the predictive relevance of the structural model. Chin (1998) mentions that the predictive relevance of constructs must be positive and with values greater than zero, so also Hair et al. (2013). The size of the Q2 effect allows to evaluate how an exogenous construct contributes to an endogenous latent construct Q2 as a measure of predictive relevance, which can be small (0.02), medium (0.15) or large (0.35). From the current research study, the Q2 was medium and depicted the model’s forecasting relevance was enough for the endogenous construct.

4.10 Standardised root mean square residual

The standardised root mean square residual for the current study’s model depicted proper fit with 0.013, a chi-square of 888.898 and normed fit index of 0.877, as indicated in Table 10.

4.11 Overall assessment

Goodness of fit (GoF) is defined as the geometric mean of both AVE and the average of R2 of all endogenous variables (Akter et al., 2011). Smart PLS results can be assessed globally for the overall mode and locally for the measurement model and the structural model (Henseler, 2017). The criteria of GoF to decide whether GoF values are not fit, small, medium or large to be considered as global valid PLS model are given by (Akter et al., 2011) as GoF less than 0.1 (not fit), GoF between 0.1 and 0.25 (small), GoF between 0.25 and 0.36 (medium) and GoF greater than 0.36 (large). The formula for calculating GoF was adopted from (Henseler, 2017) as follows:

GoF =AVE ×R2
Therefore, the GoF value for this study is 0.739 (Table 11), which is above 0.36 as indicated (Akter et al., 2011). This proves that the developed model is large in explaining the issues of customers’ attitudinal and behavioural loyalty.

5. Discussion

In line with this, all the testing of H1(a) to H3(d) indicated positive significance levels. Testing of H1(a) gave a result of overall service quality significantly affecting customer trust (β = 0.882, t = 10.457, p = 0.000). Trust and intention to try hotel service are connected to the level of customers’ loyalty to a given corporate brand of the hotel (Nyagadza et al., 2021) and associated satisfaction levels (Papacharissi and Mendelson, 2011). The H1(b) indicated that overall service quality significantly affects customer satisfaction (β = 0.874, t = 9.304, p = 0.000). This is supported by the view that customer satisfaction is a consequence of the comparison between the expected hotel service or product performance, product brand performance (Nyagadza, 2020) and comparison standard type of customer expectations (Smith et al., 1999). The key values are a vital reflection of the ways in which customers make decisions on their needs regarding specific hotel services (Saarijarvi, 2012). In line with this, H1(c) showed that overall service quality significantly affects customer value (β = 0.804, t = 8.561, p = 0.000). During the process of making a decision to get hotel service, customers tend to refer to some key specifications of the offers, compare and examine other options (Kotler and Armstrong, 2012) for sustainable enjoyment. The testing of H1(d) depicted that overall service quality significantly affects customer commitment (β = 0.815, t = 9.104, p = 0.000). This is so because the intention to enjoy hotel service is positively related with satisfaction and trust (Bae, 2018). Bold associations exist between using hotel services and products and customer commitment. Attitudinal loyalty of hotel customers is directly to the latter’s attachment to inner thoughts, word of mouth which is positive and constructive recommendations (Zeithaml et al., 1996). H2(a) concluded that customer trust significantly affect attitudinal loyalty (β = 0.010, t = 2.113, p = 0.023). This is due to the fact that trust plays a crucial role in determining attitudinal loyalty. On the other hand, behavioural loyalty is built through evaluating whether customers remain with the same hotel in the future (Worthington et al., 2010).

The H2(b) clearly depicted that positive customers’ satisfaction positively affects attitudinal loyalty. For customers and other stakeholders to be able to evaluate the level of satisfaction in the overall hotel service offered via various platforms including digital ones (Nyagadza et al., 2021), behavioural loyalty role (Bagozzi et al., 1999) plays an important role. H2(c) testing showed the result that customer commitment significantly affect attitudinal loyalty (β = 0.612, t = 8.014, p = 0.000). As well as H2(d) came out indicating positive customers’ value positively affect attitudinal loyalty (β = 0.129, t = 3.084, p = 0.011). Commitment and trust in overall hotel service quality are both connected to the level of loyalty to a given corporate brand of hotel and associated customer satisfaction levels (Papacharissi and Mendelson, 2011). This is so because the intention to enjoy hotel service is positively related with satisfaction and trust (Bae, 2018). Testing of H3(a) showed that customer trust significantly affect behavioural loyalty (β = 0.200, t = 4.341, p = 0.0003). Prior research studies have depicted that there are many factors which influence the loyalty of customers and overall service quality remains the dominant one. There are several research studies that have indicated that greater degree of customer trust as a result of positive overall service quality can drive customer behavioural loyalty (Dabholkar et al., 2000; Kushwana et al., 2013). Positive customers’ satisfaction positively affects behavioural loyalty [H3(b)]. Customer satisfaction is a consequence of the comparison between the expected hotel service or product performance, product brand performance (Nyagadza et al., 2021) and comparison standard type of customer expectations (Smith et al., 1999). Testing of H3(c) depicted a result that customer commitment significantly affect behavioural loyalty (β = 0.1999, t = 0.199, p = 0.007). Behavioural loyalty is built through evaluating whether customers remain with the same hotel in the future (Worthington et al., 2010). Customer value and trust levels have been operationalized in prior research (Alalwan et al., 2018) as the customers’ integrity, benevolence and ability in relation to the perception of overall hotel’s service quality. Testing of H3(d) depicted a result that customer value significantly affect behavioural loyalty (β = 0.085, t = 2.930, p = 0.015).

The contribution of the current study is of explaining the hotel’s overall service quality effect on customers’ attitudinal and behavioural loyalty. In addition to this, the current article innovatively separates itself from earlier research inquiries which were both generalised on measuring customer retention and customer satisfaction by accounting for the customers’ attitudinal and behavioural loyalty. The importance of studying this issue in the context of Zimbabwe is that of enhancing the business performance of hospitality firms by embracing the ideas of knowing how overall service quality of hotel influences customers’ behavioural and attitudinal loyalty and informing development of human capital policies for such organisations.

Uniqueness of the study area, Harare city, is that it has different types of hotels and mainly serves local (internal), regional and international (external) target markets (Britannica, 2022). It is modern, well-planned, with multi-storey buildings, tree-lined avenues and is the centre of Zimbabwe’s industry and commerce (Britannica, 2022), with stiff competition. The city includes Harare urban with 1.5 million, Harare rural, Chitungwiza and Epworth with 2.1 million people. Of this population, 1 million are men and 1.1 million are women (Harare City, 2022). The city of Harare lies at an elevation of 1.489 m, covering 559 km2, with a temperate climate and it is the hub of rail, road and air transport (Chikazhe et al., 2021), making it easy for customers to access desired hotel services as when they need them. They were more women than men. Majority of the respondents (69.2%) were aged between 20 and 39 years. Most of the respondents (67.2%) had already earned at least a bachelor’s degree. Majority of the respondents (84.4%) were earning less than US$1,500 per month. The results of the current research study can be applied in different parts of the world due to the fact that in the study area, Harare city hotels have benchmarked their services charges (ranging from a minimum of US$35 to around US$133 as the most expensive per night) with other competitive global hoteliers around the continent and international scale.

Conclusion, theoretical, research practical implications, limitations and future research directions of the study are discussed in the succeeding section.

6. Conclusion

Despite the limitations of the current study, the results have contributed to the better understanding of overall service quality, satisfaction, trust, value and commitment nexus with hotel customers’ attitudinal and behavioural loyalty. Complementary research studies can be done in other developed parts of the world (not only in Zimbabwe, located in southern Africa) to be able to come up with cross-cultural comparisons, as well as methodological validation. In summary, the results hopefully may influence further future research study inquiries. Key lessons learnt in the current study include the notion that, during the process of making a decision to get hotel service, customers tend to refer to some key specifications of the offers, compare and examine other options for enjoyment.

Furthermore to this, the key values are an important cogitation of the ways in which customers make decisions on their needs or wants regarding specific hotel services. Whenever customers are satisfied with the perceived value of the overall service of any given hotel, it leads to the development high customer attitudinal loyalty. If an organisation is able to satisfy the basic wants of its customers such as the ones for hotels, it is very easy to achieve customers’ behavioural and attitudinal loyalty to the overall service offered to them. The intention to enjoy offered hotel’s overall service is highly related to the trust that customers place in transacting and receiving envisaged hospitality services. This directly affects customers’ attitudinal and behavioural loyalty to the hotel.

7. Theoretical implications

Despite the need for making an improvement in overall service quality to enhance customers’ attitudinal and behavioural loyalty, there is a need to involve other variables such as customer trust, satisfaction and value (Chawla and Joshi, 2017; Gong and Yi, 2018). The model developed in the current research study has managed to comprehensively integrate predictors from the existing literature in connection with exploratory, empirical, conceptual and anecdotal literature conducted in the hotel service quality research stratification. Hotel overall service quality study is a complicated phenomenon which may require more than one model to test its validity and reliability than the theoretical model explicated in the current study. A results comparison with the extant literature is anchored on the hypothetical context incubated to address the main research objectives. These are very key in a service sector such as hotels, tourism and hospitality.

The theoretical findings in the current study complement those in the existing body of literature related to the overall service quality area, such as Abd-El-Salam et al. (2013), Asongu et al. (2020), Chiguvi and Guruwo (2017), Kamboj and Singh (2018), Thakur (2014) and Chikazhe et al. (2021). The study’s contribution to theory indicated the necessity of using overall service to predict attitudinal and behavioural loyalty. This is theoretically enriching in the understanding of mediation analysis for the link between the predictor (overall service quality, customer value, satisfaction, commitment and trust) and response variables (attitudinal and behavioural loyalty) as applied in the current study.

8. Practical implications

The current research study’s practical implications are for the hospitality service industry and other related services sector organisations. The research has determined useful, practical contributions to hotel service practice and implications for pushing the agenda of fostering sterling hotels’ overall service quality in Zimbabwe. The study indicates that overall service quality, satisfaction, trust, value and commitment have a direct effect on hotel customers’ attitudinal and behavioural loyalty, as indicated by the hypotheses test results [H1(a) to H3(d)]. Since overall service quality is highly subjective, there is a need for physical experience and interaction with the service offerings of a hotel before a customer regard a hotel to be of high overall service quality.

Due to the fact that Harare city has different types of modern hotels, being the centre of Zimbabwe’s industry and commerce, and mainly serves local, regional and international target markets, this implies a great deal of competition. Hotels are compelled to improve their overall service quality to maintain positive customer trust, value, commitment and satisfaction. Practically, the perception of the customers towards overall hotel service quality is a major driver of attitudinal and behavioural loyalty. When customers are positively satisfied by hotels’ overall service quality, they believe that they have made the correct decisions, and it increases their probability of repeat service purchases. This is so because the overall service quality of hotels have been found to be with the direct effect on customers’ trust and customers’ satisfaction.

9. Study limitations and future research implications

The study has limitations which may affect the generalisability of the results since they can only be applied to the population and country or area studied. Another limitation was the nature of the study (cross-sectional), which does not allow conclusions to be made about the development of hotel service due to the smaller sample size used and insufficient survey questions number. A fairly bigger sample and more accurate sampling plan may be needed in future to improve the study. This implies that in future, longitudinal research study inquiries can be made to check different variations of economic situations in other relevant studies. Future research studies can include evaluating other relevant theoretical frameworks in service quality, satisfaction, trust and value and commitment nexus with hotel customers’ loyalty.

Figures

Conceptual research study model

Figure 1.

Conceptual research study model

Initial structural model

Figure A1.

Initial structural model

Final structural model

Figure A2.

Final structural model

Summarised results of rotated factor matrix (RFM)

Item OSQ factors CV factors COM factors CST factors CT factors AL factors BL factors
1 2 3 4 5 6 7
OSQ1 0.881
OSQ2 0.82
OSQ3 0.816
CV1 0.862
CV2 0.835
CV3 0.902
CV4 0.531
COM1 0.822
COM2 0.871
COM3 0.858
CST1 0.921
CST2 0.901
CST3 0.898
CT1 0.852
CT2 0.826
CT3 0.836
CT4 0.482
AL1 0.937
AL2 0.91
AL3 0.903
BL1 0.821
BL2 0.803
BL3             0.842

Source: Primary data (2021), key to acronyms in Table 1: overall service quality (OSQ), customers’ value (CV), customers’ trust (CT), customers’ satisfaction (CST), attitudinal loyalty (AL), behavioural loyalty (BL), commitment (COM). Specific details appear in Appendix 1. Extraction method: principal axis factoring (PAF) and factor loading in Italic are <0.60

KMO and Bartlett’s test

Kaiser–Meyer–Olkin measure of sampling adequacy 0.903
Bartlett’s test of sphericity Approx. chi-square 2,514.301
Df 28
Sig. 0.000
Notes:

Kaiser–Meyer–Olkin (KMO) values found acceptable >0.60, with 0.903 (Tabachnick and Fidell, 2007). Whereas Bartlett’s test of sphericity was significant with p < 0.001 (Field et al., 2012). Communalities value for each item was >0.2

Source: Primary data (2021)

Descriptive statistics

Construct Item Descriptive statistics Cronbach alpha (α) Result Communalities
Mean SD Sk Ku
Overall Service Quality (OSQ) OSQ1 4.17 1.12 0.815 1.73 0.814 Reliable 0.801
OSQ2 0.826 1.32
OSQ3 0.914 1.76
Customer Value (CV) CV1 4.13 1.05 1.09 1.62 0.807 Reliable 0.803
CV2 1.21 1.75
CV3 0.925 1.70
Customer Commitment (COM) COM1 4.14 1.10 0.776 1.61 0.826 Reliable 0.816
COM2 0.812 1.72
COM3 1.24 1.72
Customer Satisfaction (CST) CST1 4.76 1.21 1.32 1.82 0.909 Reliable 0.854
CST2 1.46 1.85
CST3 1.81 1.79
Customer Trust (CT) CT1 4.56 1.16 0.88 1.74 0.887 Reliable 0.836
CT2 1.33 1.74
CT3 1.21 1.71
Attitudinal Loyalty (AL) AL1 4.29 1.13 0.952 1.41 0.876 Reliable 0.825
AL2 0.794 1.78
AL3 1.37 1.45
Behavioural Loyalty (BL) BL1 4.07 1.02 1.71 1.58 0.829 Reliable 0.812
BL2 1.45 1.35
BL3 1.25 1.37

Source: Primary data (2021)

Correlation between constructs

Variables AL BL COM CT CST CV OSQ
AL 0.917
BL 0.729 0.822
COM 0.730 0.716 0.851
CT 0.873 0.819 0.805 0.838
CST 0.876 0.801 0.843 0.814 0.907
CV 0.718 0.770 0.803 0.749 0.835 0.867
OSQ 0.648 0.703 0.686 0.705 0.820 0.619 0.840
AVE 0.840 0.676 0.724 0.703 0.822 0.751 0.705

Notes: Key to acronyms in Table 4: overall service quality (OSQ), customers’ value (CV), customers’ trust (CT), customers’ satisfaction (CST), attitudinal loyalty (AL), behavioural loyalty (BL), commitment (COM). specific details appear in Appendix 1

Source: Primary data (2021)

Convergent validity

Construct Item Factor loading (FL) FL2 1–FL2 No. of indicators(n) CR AVE Result
Overall Service Quality (OSQ) OSQ1 0.881 0.776 0.224 3 0.739 0.705 Achieved
OSQ2 0.820 0.672 0.328
OSQ3 0.816 0.666 0.336
Customer Value (CV) CV1 0.862 0.743 0.257 3 0.777 0.751 Achieved
CV2 0.835 0.697 0.303
CV3 0.902 0.814 0.186
Customer Commitment (COM) COM1 0.822 0.676 0.324 3 0.755 0.724 Achieved
COM2 0.871 0.759 0.241
COM3 0.858 0.736 0.264
Customer Satisfaction (CST) CST1 0.921 0.848 0.152 3 0.836 0.822 Achieved
CST2 0.901 0.812 0.188
CST3 0.898 0.806 0.194
Customer Trust (CT) CS1 0.852 0.726 0.274 3 0.738 0.703 Achieved
CS2 0.826 0.682 0.318
CS3 0.836 0.699 0.301
Attitudinal Loyalty (AL) AL1 0.937 0.878 0.122 3 0.852 0.840 Achieved
AL2 0.910 0.828 0.172
AL3 0.903 0.815 0.185
Behavioural Loyalty (BL) BL1 0.821 0.674 0.326 3 0.717 0.676 Achieved
BL2 0.803 0.645 0.355
BL3 0.842 0.709 0.291

Source: Primary data (2021)

The Fornell–Larcker criterion

Variables AL BL COM CT CST CV OSQ
AL 1
BL 0.729 1
COM 0.730 0.716 1
CT 0.873 0.819 0.805 1
CST 0.876 0.901 0.843 0.854 1
CV 0.718 0.770 0.803 0.749 0.835 1
OSQ 0.648 0.703 0.686 0.705 0.820 0.619 1
AVE 0.917 0.822 0.851 0.838 0.907 0.867 0.840
Notes:

Key to acronyms in Table 6: overall service quality (OSQ), customers’ value (CV), customers’ trust (CT), customers’ satisfaction (CST), attitudinal loyalty (AL), behavioural loyalty (BL), commitment (COM). Specific details appear in Appendix 1

Source: Primary data (2021)

Results of bootstrapping

Path Path coefficients (β-value) Confidence intervals t-value p-value Significance level
2.5%97.5%
COM→AL 0.612 0.415 0.817 8.014 0.000 Significant
COM→BL 0.199 0.070 0.387 3.731 0.007 Significant
CT→AL 0.010 0.009 0.139 2.113 0.023 Significant
CT→BL 0.200 0.137 0.383 4.341 0.003 Significant
CST→AL 0.197 0.116 0.287 3.431 0.011 Significant
CST→BL −0.123 −0.167 0.188 2.009 0.034 Significant
CV→AL 0.129 0.086 0.298 3.084 0.013 Significant
CV→BL 0.085 0.041 0.211 2.930 0.015 Significant
OSQ→COM 0.815 0.655 0.983 9.104 0.000 Significant
OSQ→CT 0.882 0.475 0.992 10.457 0.000 Significant
OSQ→CST 0.874 0.646 0.979 9.304 0.000 Significant
OSQ→CV 0.804 0.585 0.998 8.561 0.000 Significant
Notes:

Key to acronyms in Table 7: overall service quality (OSQ), customers’ value (CV), customers’ trust (CT), customers’ satisfaction (CST), attitudinal loyalty (AL), behavioural loyalty (BL), commitment (COM). Specific details appear in Appendix 1

Source: Primary data (2021)

Mediating effect analysis via Sobel test

Path Std beta Std error T statistics p-values Decision Bootstrapping confidence interval
95%
CI LL
95%
CI UL
OSQ→CT→AL 0.009 0.003 2.659 <0.001 Supported 0.003 0.101
OSQ→CT→BL 0.176 0.062 5.024 <0.001 Supported 0.015 0.627
OSQ→CST→AL 0.120 0.054 5.834 <0.001 Supported 0.098 0.198
OSQ→CST→BL −0.108 0.285 2.041 <0.001 Supported −0.298 0.112
OSQ→COM→AL 0.499 0.021 7.635 <0.001 Supported 0.372 0.673
OSQ→COM→BL 0.597 0.014 7.831 <0.001 Supported 0.121 0.605
OSQ→CV→AL 0.104 0.087 4.923 <0.001 Supported 0.073 0.184
OSQ→CV→BL 0.068 0.105 4.162 <0.001 Supported 0.032 0.217
Notes:

Key to acronyms in Table 8: overall service quality (OSQ), customers’ value (CV), customers’ trust (CT), customers’ satisfaction (CST), attitudinal loyalty (AL), behavioural loyalty (BL), commitment (COM). Specific details appear in Appendix 1

Source: Primary data (2021)

Variance inflation factor (VIF) and effect size

Constructs R2 Attitudinal and behavioural loyalty as dependent variables
Tolerance VIF Effect size
Customers’ Trust 0.684 0.316 3.16 2.16
Customers’ Satisfaction 0.708 0.292 3.42 2.42
Commitment 0.587 0.413 2.42 1.42
Customers’ Value 0.717 0.283 3.53 2.53
Behavioural Loyalty 0.934 0.066 15.15 14.15
Attitudinal Loyalty 0.855 0.145 6.90 5.9

Source: Primary data (2021)

Model fit summary

Estimated model
Standardised root mean square residual 0.013
d_ULS 2.531
d_ G1 0.874
d_G2 0.728
Chi-square 888.898
Normed fit index 0.877

Source: Primary data (2021)

Goodness of fit index calculation

Construct Average variance extracted R2
OSQ 0.705
CV 0.751 0.646
COM 0.724 0.664
CST 0.822 0.764
CT 0.703 0.778
AL 0.840 0.752
BL 0.676 0.795
AVE 0.746
AVE × R2 0.546
GoF 0.739

Notes: Key to acronyms in Table 11: overall service quality (OSQ), customers’ value (CV), customers’ trust (CT), customers’ satisfaction (CST), attitudinal loyalty (AL), behavioural loyalty (BL), commitment (COM). Specific details appear in Appendix 1

Source: Primary data (2021)

Instrument statements and reliability

Construct Item Statement F α
Overall Service Quality (OSQ) OSQ1 The overall service quality is excellent 0.881 0.814
OSQ2 The overall service quality is high quality 0.820
OSQ3 The overall service quality is of a high standard 0.816
Customers’ Value (CV) CV1 I would choose this hotel service because it is good to pay for 0.862 0.807
CV2 The hotel service offer is worthwhile 0.835
CV3 The hotel service is good value for money 0.902
Customers’ Trust (CT) CS1 Many people I know use this hotel service 0.852 0.826
CS2 My behaviour is shaped by people who also use this hotel 0.826
CS3 The people I value their opinions also use the hotel 0.836
Customers’ Satisfaction (CST) CST1 This is my right choice to choose the hotel 0.921 0.909
CST2 I am not ashamed of my decision to choose this hotel 0.901
CST3 Overall, I am happy about this hotel 0.898
Attitudinal Loyalty (AL) AL1 I am a patron of this hotel due to its service quality effectiveness 0.937 0.825
AL2 I am somebody who is positive about the hotel 0.910
AL3 I am willing to refer someone to this hotel 0.903
Behavioural Loyalty (BL) BL1 This hotel is my first option 0.821 0.812
BL2 I am of the notion of increasing more service seeking in future 0.803
BL3 I will not increase service seeking in the coming years 0.842
Commitment (COM) COM1 I will keep on paying for the services offered by this hotel 0.822 0.826
COM2 In future, I will be fully committed for its services 0.871
COM3 I will not pay for this hotel’s service in future 0.858

Appendix 1. Instrument statements and reliability

Appendix 2

Appendix 3

References

Abd-El-Salam, E.M., Shawky, A.Y. and El-Nahas, T. (2013), “The impact of corporate image and reputation on service quality, customer satisfaction and customer loyalty: testing the mediating role. Case analysis in an international service company”, The Business and Management Review, Vol. 3 No. 2, pp. 177-189.

Adetunji, O., Yadavalli, V. and Malada, A. (2013), “Assessment of the quality of service provided by a national regulatory institution”, The South African Journal of Industrial Engineering, Vol. 24 No. 1, pp. 29-49.

Akter, S., D’Ambra, J. and Pradeep. R. (2011), An evaluation of PLS based complex models: the roles of power analysis, predictive relevance and GoF index”, Proceedings of the 17th Americas Conference on Information Systems (AMCIS2011), Association for Information Systems, Detroit, USA, pp. 1-7.

Alalwan, A.A., Dwivedi, Y.K., Rana, N.P. and Algharabat, R. (2018), “Examining factors influencing Jordanian customers’ intentions and adoption of internet banking: extending UTAUT2 with risk”, Journal of Retailing and Consumer Services, Vol. 40, pp. 125-138.

Alexander, J.R., Houghton, D.C., Twohig, M.P., Franklin, M.E., Saunders Neal-Barnett, A.M. and Woods, D.W. (2016), “Factor analysis of the Milwaukee inventory for subtypes of trichotillomania-adult version”, Journal of Obsessive-Compulsive and Related Disorders, Vol. 11, pp. 31-38.

Armstrong, J.S. and Overton, T.S. (1977), “Estimating non-response bias in mail surveys”, Journal of Marketing Research, Vol. 14 No. 3, pp. 396-402.

Arnold, A. (2018), “How chatbots feed into millennials’ need for instant gratification”, www.forbes.com/sites/andrewarnold/2018/01/27/how-chatbots-feed-into-millennials-needfor-instant-gratification/?sh=79a719ac3675 (accessed 31 October 2020).

Ashraf, S., Ilyas, R., Imtiaz, M. and Ahmad, S. (2018), “Impact of service quality, corporate image and perceived value on brand loyalty with presence and absence of customer satisfaction: a study of four service sectors of Pakistan”, International Journal of Academic Research in Business and Social Sciences, Vol. 8 No. 2, pp. 452-474.

Asongu, S., Nnanna, J. and Acha-Anyi, P. (2020), “Inclusive education for inclusive economic participation: the financial access channel”, Gender in Management: An International Journal, Vol. 35 No. 5, pp. 481-503.

Attallah, N.F. (2015), “Evaluation of perceived service quality provided by tourism establishments in Egypt”, Tourism and Hospitality Research, Vol. 15 No. 3, pp. 149-160.

Bae, M.-Y. (2018), “Understanding the effect of the discrepancy between sought and obtained gratifications on social networking site users’ satisfaction and continuance intention”, Computers in Human Behavior, Vol. 79, pp. 137-153.

Bagozzi, R.P., Gopinath, M. and Nyer, P.U. (1999), “The role of emotions in marketing”, Journal of the Academy of Marketing Science, Vol. 27 No. 2, pp. 184-206.

Bahadur, W., Aziz, S., Zulfiqar, S. and Wright, L.T. (2018), “Effect of employee empathy on customer satisfaction and loyalty during employee–customer interactions: the mediating role of customer affective commitment and perceived service quality”, Cogent Business and Management, Vol. 5 No. 1.

Blesic, I., Tesanovic, D. and Psodorov, D. (2011), “Consumer satisfaction and quality management in the hospitality industry in South-East Europe”, African Journal of Business Management, Vol. 5 No. 4, pp. 1388-1396.

Boohene, R. and Agyapong, G.K. (2010), “Analysis of the antecedents of customer loyalty of telecommunication industry in Ghana: the case of Vodafone (Ghana)”, International Business Research, Vol. 4 No. 1, pp. 229-240.

Boulding, W., Kalra, A., Staelin, R. and Zeithaml, V.A. (1993), “A dynamic process model of service quality: from expectations to behavioural intentions”, Journal of Marketing Research, Vol. 30 No. 1, pp. 7-27.

Brady, M.K. and Cronin, J.J. (2001), “Some new thoughts on conceptualizing perceived service quality: a hierarchical approach”, Journal of Marketing, Vol. 65 No. 3, pp. 34-49.

Britannica (2022), “Harare national capital, Zimbabwe”, accessed from (22/01/2022”, ): www.britannica.com/place/Harare

Buttle, F. (1996), “SERVQUAL: Review, critique, research agenda”, European Journal of Marketing, Vol. 30 No. 1, pp. 8-32.

Chawla, D. and Joshi, H. (2017), “Role of demographics as moderator in mobile banking adoption”, Twenty-third Americas Conference on Information Systems, Boston.

Chan, L.L. and Idris, N. (2017), “Validity and reliability of the instrument using exploratory factor analysis and Cronbach’s alpha”, International Journal of Academic Research in Business and Social Sciences, Vol. 7 No. 10, pp. 400-410.

CGS (2019), “CGS survey reveals consumers prefer a hybrid AI/human approach to customer service. Is there chatbot fatigue?”, available at: www.cgsinc.com/en/resources/2019-CGSCustomer-Service-Chatbots-Channels-Survey (accessed 31 October 2020).

Cheng, Y. and Jiang, H. (2020), “How do AI-driven chatbots impact user experience? Examining gratifications, perceived privacy risk, satisfaction, loyalty, and continued use”, Journal of Broadcasting and Electronic Media, Vol. 64 No. 4, pp. 592-614.

Chiguvi, D. and Guruwo, P.T. (2017), “Impact of customer satisfaction on customer loyalty in the banking sector”, International Journal of Scientific Engineering and Research (IJSER), pp. 53-63.

Chikazhe, L., Makanyeza, C. and Chigunhah, B. (2021), “Understanding mediators and moderators of the effect of customer satisfaction on loyalty”, Cogent Business and Management, Vol. 8 No. 1.

Chin, W.W. (1998), “The partial least squares approach for structural equation modelling”, in Marcoulides, G.A. (Ed.), Modern Methods for Business Research, Lawrence Erlbaum Associates Publishers, pp. 295-336.

Chongsanguan, P., Trimetsoontorn, J. and Fongsuwan, W. (2016), “Hierarchical model of service quality and its effect on consumers’ perceived image, satisfaction and behavioural intentions: a study of Bangkok’s mass rapid transit systems, Thailand”, J. For Global Business Advancement, Vol. 9 No. 4, pp. 331-356.

Churchill, G.A. (1979), “A paradigm for developing better measures of marketing constructs”, Journal of Marketing Research, Vol. 16 No. 1, pp. 64-73.

Cronin, J.J. and Taylor, S.A. (1992), “Measuring service quality: a re-examination and extension”, Journal of Marketing, Vol. 56 No. 3, pp. 55-68.

Dabholkar, P.A., Shepherd, C.D. and Thorpe, D.I. (2000), “A comprehensive framework for service quality: an investigation of critical conceptual and measurement issues through a longitudinal study”, Journal of Retailing, Vol. 76 No. 2, pp. 139-173.

Da Costa Carvalho, P.D. (2015), “An integrated conceptual model of destination branding – touristmind”, Journal of Tourism Management Research, Vol. 2 No. 2, pp. 24-40.

Dube, L. and Maute, M. (1996), “The antecedents of brand switching, brand loyalty and verbal responses to service failures”, in Swartz, T., Bowen, D. and Brown, S. (Eds), Advances in Services Marketing and Management, JAI Press, Greenwich, CT, Vol. 5, pp. 127-151.

Duggal, E. and Verma, H.V. (2018), “Intention to consume junk food: a study of drivers for control implications”, Malaysian Management Journal, Vol. 22, pp. 109-124.

Effendi, M., Matore, E.M., Khairani, A.Z. and Adnan, R. (2019), “Exploratory factor analysis (EFA) for adversity quotient (AQ) instrument among youth”, Journal of Critical Reviews, Vol. 6 No. 6, pp. 234-242.

El Essawi, N. and El Aziz, R.A. (2012), “Determining the main dimensions that affect e-customer relationship management readiness in the Egyptian banking industry”, International Journal of Electronic Customer Relationship Management, Vol. 6 No. 3/4, pp. 217-234.

Field, A., Miles, J. and Field, Z. (2012), Discovering Statistics Using R, Sage, London.

Fornell, C. (1992), “A national customer satisfaction barometer: the Swedish experience”, Journal of Marketing, Vol. 56 No. 1, pp. 6-21.

Fornell, C. and Larcker, D.G. (1981), “Evaluating structural equation models with unobservable variables and measurement error”, Journal of Marketing Research, Vol. 18 No. 1, pp. 39-50.

Gerald, B. (2018), “A brief review of independent, dependent and one sample t-test”, International Journal of Applied Mathematics and Theoretical Physics, Vol. 4 No. 2, p. 50.

Grant, M.J. and Booth, A. (2009), “A typology of reviews: an analysis of 14 review types and associated methodologies”, Health Information and Libraries Journal, Vol. 26 No. 2, pp. 91-108.

Groonros, C. (1984), “A service quality model and its marketing implications”, European Journal of Marketing, Vol. 18 No. 4, pp. 36-44.

Groonros, C. (2010), Service Management and Marketing: A Customer Relationship Management Approach, 3rd Ed., Wiley. Chichester.

Gong, T. and Yi, Y. (2018), “The effect of service quality on customer satisfaction, loyalty, and happiness in five Asian countries”, Psychology and Marketing, Vol. 35 No. 6, pp. 427-442.

Hair, J.F., Ringle, C.M. and Sarstedt, M. (2013), “Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance”, Long Range Planning, Vol. 46 No. 1-2, pp. 1-12.

Harare City (2022), “The sunshine city”, available at: www.hararecity.co.zw/about (Accessed 22 January 2022)

Henseler, J. (2017), “Partial least squares path modelling”, in Advanced Methods for Modelling Markets, Springer, Cham, pp. 361-381.

Hosseini, M.H. and Saravi-Moghadam, N. (2017), “A model of customer-based brand equity: evidence from the banking service in Iran”, International Journal of Productivity and Quality Management, Vol. 21 No. 1, pp. 23-44.

Iqbal, M.S., Hassan, M.U. and Habibah, U. (2018), “Impact of self-service technology (SST) service quality on customer loyalty and behavioural intention: the mediating role of customer satisfaction”, Cogent Business and Management, Vol. 5 No. 1.

Junior, P.C.R. and de Aquino Guimarães, T. (2012), “Service innovation: the state of the art and a proposal of a research agenda”, Review of Business Management, Vol. 14 No. 44, pp. 293-313.

Kamboj, N. and Singh, G. (2018), “Customer satisfaction with digital banking in India: exploring the mediating role of demographic factors”, Indian Journal of Computer Science, Vol. 3 No. 2, pp. 9-32.

Keller, C.M. and Kros, J.F. (2011), “An innovative excel application to improve exam reliability in marketing courses”, Marketing Education Review, Vol. 21 No. 1, pp. 21-28.

Kim, K.W. and Mauborgne, R. (2015), Blue Ocean Strategy, Expanded Edition: How to Create Uncontested Market Space and Make the Competition Irrelevant, MA Harvard Business Review Press. MA.

Klimek, K. (2013), “Destination management organisations and their shift to sustainable tourism development”, European Journal of Tourism, Hospitality and Recreation, Vol. 4 No. 2, pp. 27-47.

Kushwana, R.K., Mohan, M. and Mandal, D. (2013), “An empirical study of SERVQUAL, customer satisfaction and loyalty in Indian banking sector”, International Journal of Research in Commerce, IT and Management, Vol. 3 No. 4, pp. 13-15.

Jaiswal, A.K. and Niraj, R. (2011), “Examining mediating role of attitudinal loyalty and nonlinear effects in satisfaction-behavioural intentions relationship”, Journal of Services Marketing, Vol. 25 No. 3, pp. 165-175.

Kotler, P. and Armstrong, G. (2012), Principles of Marketing, 14th ed., Pearson Prentice Hall. New York, NY.

Lam, S.Y., Shankar, V., Erramilli, M.K. and Murthy, B. (2004), “Customer value, satisfaction, loyalty, and switching costs: an illustration from a business-to-business service context”, Journal of the Academy of Marketing Science, Vol. 32 No. 3, pp. 293-310.

Liu, Y., Huang, D., Wang, M. and Wang, Y. (2020), “How do service quality, value, pleasure, and satisfaction create loyalty to smart dockless bike-sharing systems?”, Review of Business Management, Vol. 22 No. 3, pp. 705-728.

Ladhari, R. (2008), “Alternative measures of service quality: a review”, Managing Service Quality: An International Journal, Vol. 18 No. 1, pp. 65-86.

Maat, S., Zakaria, E., Nordin, N. and Meerah, T. (2011), “Confirmatory factor analysis of the mathematics teachers' teaching practices instrument”, World Applied Sciences Journal, Vol. 12 No. 11, pp. 2092-2096.

Malhotra, K.N. (2010), Marketing Research: An Applied Orientation, 6th ed., Pearson Education International. Upper Saddle River.

Matzler, K., Strobl, A., Thurner, N. and Füller, J. (2015), “Switching experience, customer satisfaction, and switching costs in the ICT industry”, Journal of Service Management, Vol. 26 No. 1, pp. 117-136.

Marketing Research Society (MRS) (2022), “Marketing research ethics”, available at: www.mrs.org.uk/

Menon, K. and Dube, L. (2000), “Ensuring greater satisfaction by engineering salesperson response to customer emotions”, Journal of Retailing, Vol. 76 No. 3, pp. 285-307.

Muposhi, A., Nyagadza, B. and Mafini, C. (2021), “Fashion designers’ attitude-behaviour inconsistencies towards a sustainable business model: a neutralization theory perspective”, Journal of Fashion Marketing and Management: An International Journal), Vol. 1, pp. 1-20.

Murphy, B. (2017), “Millennials and gen Z would rather text each other than do this, according to a new study”, available at: www.inc.com/bill-murphy-jr/millennials-gen-z-prefer-textingto-human-conversations-new-study-says-plus-5-other-findings.html (accessed 31 December 2019).

Neupane, S., Chimhundu, R. and Kong, E. (2021), “Strategic profile for positioning eco-apparel among mainstream apparel consumers”, Journal of Global Fashion Marketing, Vol. 12 No. 3, pp. 229-244.

Ngo, V.M. and Nguyen, H.H. (2016), “The relationship between service quality, customer satisfaction and customer loyalty: an investigation in Vietnamese retail banking sector”, Journal of Competitiveness, Vol. 8 No. 2, pp. 103-116.

Nyagadza, B. (2019), “Responding to change and customer value improvement: pragmatic advice to banks”, The Marketing Review, Vol. 19 No. 3, pp. 235-252.

Nyagadza, B. (2020), “Search engine marketing and social media marketing predictive trends”, Journal of Digital and Media Policy (JDMP), Vol. 1.

Nyagadza, B., Kadembo, E.M. and Makasi, A. (2021), “When corporate brands tell stories: a signalling theory perspective”, Cogent Psychology, Vol. 8 No. 1, pp. 1-30.

Oliver, R.L. (1997), Satisfaction. A Behavioural Perspective on the Consumer, Irwin/McGraw-Hill. Boston, MA.

Papacharissi, Z. and Mendelson, A. (2011), “Toward a new(er) sociability: uses, gratifications and social capital on Facebook”, in Papathanassopoulos S. (Ed.), Media Perspectives for the 21st Century, Routledge, New York, NY pp. 212-230.

Parasuraman, A., Zeithaml, V.A. and Leonard, L.B. (1988), “SERVQUAL: a multiple-item scale for measuring consumer perceptions of service quality”, Journal of Retailing, Vol. 64 No. 1, pp. 12-40.

Pew Research Center (2019), “Demographics of internet and home broadband usage”, available at: post.com/uploads/NielsenTotalAudienceReportQ12019.pdf (accessed October 30, 2020).

PwC. (2018), “Report millennials vs generation Z. 2018”, available at: www.pwc.com/it/it/press-room/assets/docs/cs_pwc_food.pdf (accessed 27 November 2020).

Rahman, A., Björk, P. and Ravald, A. (2020), “Exploring the effects of service provider’s organizational support and empowerment on employee engagement and wellbeing”, Cogent Business and Management, Vol. 7 No. 1.

Rajeswari, S., Srinivasulu, Y. and Thiyagarajan, S. (2017), “Relationship among service quality, customer satisfaction and customer loyalty: with special reference to wireline telecom sector (DSL service)”, Global Business Review, Vol. 18 No. 4, pp. 1041-1058.

Reeves, C.A. and Bednar, D.A. (1994), “Defining quality: Alternatives and implications”, The Academy of Management Review, Vol. 19 No. 3, pp. 419-445.

Roberts, K., Varki, S. and Brodie, R. (2003), “Measuring the quality of relationships in consumer services: an empirical study”, European Journal of Marketing, Vol. 37 No. 1/2, pp. 169-196.

Roest, H. and Pieters, R. (1997), “The nomological net of perceived service quality”, International Journal of Service Industry Management, Vol. 8 No. 4, pp. 336-351.

Saarijarvi, H. (2012), “The mechanisms of value co-creation”, Journal of Strategic Marketing, Vol. 20 No. 5, pp. 381-391.

Sardana, S. and Bajpai, V.N. (2020), “E-banking service quality and customer satisfaction: an exploratory study on India”, International Journal of Services and Operations Management, Vol. 35 No. 2, pp. 223-247.

Saunders, M., Lewis, P. and Thornhill, A. (2009), Research Methods for Business Students, 5th Edition, Pearson Education Limited. London.

Seto-Pamies, D. (2012), “Customer loyalty to service providers: examining the role of service quality, customer satisfaction and trust”, Total Quality Management, Vol. 23 No. 11, pp. 1271-1275.

Singh, J. and Sirdeshmukh, D. (2000), “Agency and trust mechanisms in consumer satisfaction and loyalty judgements”, Journal of the Academy of Marketing Science, Vol. 28 No. 1, pp. 150-167.

Sivadas, E. and Baker-Prewitt, J.L. (2000), “An examination of the relationship between service quality, customer satisfaction, and store loyalty”, International Journal of Retail and Distribution Management, Vol. 28 No. 2, pp. 73-82.

Smith, A.K., Bolton, R.N. and Wagner, J. (1999), “A model of customer satisfaction with service encounters involving failure and recovery”, Journal of Marketing Research, Vol. 36 No. 3, pp. 356-373.

Tabachnick, B.G. and Fidell, L.S. (2007), Using Multivariate Statistics (5th Ed.), Allyn & Bacon/Pearson Education.

Thaichon, P. and Quach, T.N. (2015), “The relationship between service quality, satisfaction, trust, value, commitment and loyalty of internet service providers' customers”, Journal of Global Scholars of Marketing Science, Vol. 25 No. 4, pp. 295-313.

Valarie, A., Zeithaml, V.A., Berry, L.L. and Parasuraman, A.V. (1996), “The behavioural consequences of service quality”, Journal of Marketing, Vol. 60 No. 2. doi: 10.2307/1251929.

Woratschek, H., Horbel, C. and Popp, B. (2020), “Determining customer satisfaction and loyalty from a value co-creation perspective”, The Service Industries Journal, Vol. 40 No. 11-12, pp. 777-799.

Worthington, S., Russell-Bennett, R. and HäRtel, C. (2010), “A tri-dimensional approach for auditing Brand loyalty”, Journal of Brand Management, Vol. 17 No. 4, pp. 243-253.

Yang, Z. and Peterson, R.T. (2004), “Customer perceived value, satisfaction, and loyalty: the role of switching costs”, Psychology and Marketing, Vol. 21 No. 10, pp. 799-822.

Zeelenberg, M. and Pieters, R. (2004), “Beyond valence in customer dissatisfaction: a review and new findings on behavioural responses to regret and disappointment in failed services”, Journal of Business Research, Vol. 57 No. 4, pp. 445-455.

Zeithaml, V.A. and Bitner, M.J. (2000), Services Marketing: Integrating Customer Focus Across the Firm, 2nd ed., McGraw Hill, New York, NY.

Zeithaml, V.A., Berry, L.L. and Parasuraman, A. (1996), “The behavioral consequences of service quality”, Journal of Marketing, Vol. 60 No. 2, pp. 31-46.

Further reading

Dahiyat, S.E., Akroush, M.N. and Abu-Lail, B.N. (2011), “An integrated model of perceived service quality and customer loyalty: an empirical examination of the mediation effects of customer satisfaction and customer trust”, International Journal of Services and Operations Management, Vol. 9 No. 4, pp. 453-490.

Elbaz, A.M., Kamar, M.S.A., Onjewu, A.K.-E. and Soliman, M. (2021), “Evaluating the antecedents of health destination loyalty: the moderating role of destination trust and tourists’ emotions”, International Journal of Hospitality and Tourism Administration, Vol. 1, pp. 1-29, doi: 10.1080/15256480.2021.1935394.

Garepasha, A., Aali, S., Zendeh, A.B. and Iranzadeh, S. (2020), “Dynamics of online relationship marketing: relationship quality and customer loyalty in Iranian banks”, Review of Business Management, Vol. 22 No. 1, pp. 140-162.

Gilligan, C. (1982), In a Difference Voice: Psychological Theory and Women’s Development, Harvard University Press, Cambridge.

Lee, K. (2009), “Gender differences in Hong Kong adolescent consumers' green purchasing behaviour”, Journal of Consumer Marketing, Vol. 26 No. 2, pp. 87-96.

Parasuraman, A., Berry, L.L. and Zeithaml, V.A. (1991), “Refinement and reassessment of the SERVQUAL scale”, Journal of Retailing, Vol. 67 No. 4, pp. 420-450.

Taylor, B., Sinha, G. and Ghoshal, T. (2009), Research Methodology, PHI Learning Pvt Ltd. New Delhi.

Wang, C. and Wu, L. (2012), “Customer loyalty and the role of relationship length”, Managing Service Quality: An International Journal, Vol. 22 No. 1, pp. 58-74.

Acknowledgements

Competing interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding: Self-funding. This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.

Disclaimer: The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of any affiliated agency of the authors.

Acknowledgements: The authors thank anonymous respondents who provided data for this study.

Corresponding author

Brighton Nyagadza is the corresponding author and can be contacted at: brightonnyagadza@gmail.com

About the authors

Brighton Nyagadza is a full-time Lecturer and Acting Chairperson of the Department of Marketing at Marondera University of Agricultural Sciences and Technology (MUAST), Zimbabwe, Full Member of the Marketers Association of Zimbabwe (MAZ), an Associate of The Chartered Institute of Marketing (ACIM), UK and Power Member of the Digital Marketing Institute (DMI), Dublin, Ireland. His research expertise revolves on corporate storytelling for Branding, Digital Marketing, Public Relations (PR), Marketing Metrics and Educational Marketing. He has published in reputable international journals, such as Journal of Digital Media and Policy (JDMP) (Intellect, Bristol, UK), Journal of Asian and African Studies (JAAS) (SAGE, London, UK), Journal of Fashion Marketing and Management (JFMM) (Emerald Insight, UK), Journal of Environmental Media (JEM) (Intellect Publishers, Bristol, UK), Youth and Society (SAGE, London, UK), PSU Research Review (PRR) (Emerald Insight, UK), Cogent Business and Management, Cogent Economics and Finance, Cogent Psychology, Cogent Social Sciences (Taylor and Francis, England and Wales, UK), The Marketing Review (TMR) (Westburn Publishers, Scotland) and others. Brighton sits on various boards, including the Mashonaland East Province National Development Strategy (NDS) Committee (2021–2025) for the ICT and Human Capital Development cohort.

Gideon Mazuruse is a full-time Mathematics and Statistics Lecturer under the Teaching and Learning Institute of the Marondera University of Agricultural Sciences and Technology (MUAST), Zimbabwe. He holds MSc in Statistics and Operations Research (NUST), BSc Hons in Statistics and Operations Research (GZU), BSc in Mathematics and Computer Science (GZU) and Postgraduate Diploma in Education (ZOU).

Asphat Muposhi holds a PhD in Marketing Management and is a Lecturer in the Department of Information and Marketing Sciences at Midlands State University (MSU), Gweru, Zimbabwe. His research interests are in environmental sustainability, green consumerism and ethical fashion. He has published several articles in international peer reviewed journals.

Farai Chigora holds a Doctorate in Business Administration (DBA) from the University of KwaZulu-Natal (South Africa), a Senior Lecturer in Business Science in the College of Business, Peace Leadership and Governance, Africa University (AU) in Zimbabwe. He is a branding specialist with an interest in destination branding, strategic marketing, business research and related business areas, which he has authored in various refereed international journals.

Related articles