Creating exceptional guest experiences: the role of engagement and relationship building in hospitality

Abstract

Purpose

The study seeks to explore the intricate dynamics among customer relationship management (CRM) practices, guest satisfaction and loyalty in the hospitality context. Additionally, it aims to examine the moderating influence of guest engagement on the relationships between CRM practices and guest satisfaction and loyalty.

Design/methodology/approach

An integrated theoretical framework is developed by incorporating CRM practices and guest engagement into the satisfaction-loyalty framework. Two research instruments were adapted from the literature to assess the perspectives of customers and employees in the hotel industry in Kashmir. The customer survey measured guest satisfaction, loyalty, and engagement, while the employee survey focused on CRM practices, including key customer focus and CRM organization. Data was collected using a pen-and-paper survey with convenience sampling across 10 qualifying hotels, each classified as 3-star or above. A total of 270 matched responses from guests and employees were obtained and analyzed using descriptive analysis, structural equation modeling (SEM), and moderation analysis with SPSS and AMOS software. The study utilized a rigorous data matching process to ensure reliability, with guest-employee pairs verified and cross-checked with hotel records.

Findings

The results indicate CRM practices play a pivotal role in shaping guest satisfaction and loyalty. Notably, personalization and a targeted customer approach emerged as the most influential factors in enhancing tourist satisfaction. Similarly, prospecting, personalization, and effective knowledge management significantly contributed to visitor loyalty. The establishment of robust relationships is underscored through collaborative active guest engagement. Furthermore, the study highlights the nuanced relationship between satisfaction and loyalty moderated by guest engagement. High levels of guest engagement amplify the positive impact of satisfaction on loyalty, while lower engagement levels attenuate this effect. Moreover, the moderating influence of guest engagement on the relationships between CRM practices and guest satisfaction and CRM practices and guest loyalty was notably strong at elevated guest engagement levels and relatively weaker at lower engagement levels.

Research limitations/implications

While the study findings encourage organizations to prioritize customer relationship development, hospitality entities must emphasize the adoption of CRM philosophy and robust guest engagement measures. Actively involving guests in co-creating services can yield incremental benefits in terms of attracting, retaining, and effectively serving guests.

Originality/value

This study introduces novel dimensions to the existing CRM framework within the hospitality context, specifically exploring the impact of hotel-specific elements (personalization and prospecting) on customer satisfaction and loyalty. Furthermore, it innovatively investigates the moderating role of guest engagement in the satisfaction-loyalty relationship, expanding its scope to include the relationships between CRM practices and guest satisfaction and guest loyalty.

Citation

Sofi, M.R., Bashir, I., Alshiha, A., Alnasser, E. and Alkhozaim, S. (2025), "Creating exceptional guest experiences: the role of engagement and relationship building in hospitality", Journal of Hospitality and Tourism Insights, Vol. 8 No. 3, pp. 891-914. https://doi.org/10.1108/JHTI-04-2024-0318

Introduction

Fostering guest satisfaction and loyalty stands as an enduring challenge for service organizations at large, with hotels, in particular, striving to navigate this intricate landscape (Herman et al., 2020). Amidst the perpetual quest for novel clientele, retaining existing guests emerges as not only a cost-effective strategy but also a conduit to the accrual of enduring benefits for hoteliers. The dynamic relationship between guests and hotels is undergoing a paradigm shift, with hoteliers strategically honing their focus on the meticulous management of individual guest experiences and transactions (Grönroos, 2017). Undoubtedly, guests have expectations when they check into a hotel and intend to maximize their stay. Guests staying in 4 and 5-star hotels anticipate a high level of professionalism with attentive, proactive, and often personalized services focusing on attributes such as the quality of the services, hygiene and cleanliness, comfort and convenience, amenities, aesthetic appeal, Booking and Check-in/Check-out Process, Technology and Connectivity, and Sustainability Practices. In contrast, guests staying in a 3-star hotel would expect friendly, efficient, and helpful service focusing on value for the money (Li et al., 2020; Padma and Ahn, 2020). Guests compare their expectations with actual experiences at a hotel by assessing whether the hotel’s services, amenities and overall experience match the descriptions and promises made during booking. If there is a mismatch, it often leads to dissatisfaction, negative reviews, and a decreased likelihood of returning or recommending the hotel to others (Li et al., 2020; Padma and Ahn, 2020). Further, the interplay of human interactions between guests and service providers is a key attribute for not only creating a favorable guest experience but also acting as an important factor for guest satisfaction and revisiting intentions (Lin et al., 2024). Consequently, hotel managers seek to transform guests into active participants and co-creators in the service delivery process. This transformative approach has given rise to the concept of customer engagement, particularly crucial in high-contact service delivery settings like hotels, where guests assume the dual role of recipients and co-creators of the service (Gumi and Sari, 2022).

Recognizing the paramount importance of engaged customers in the hotel industry (Sawhney et al., 2005), managers now strive to evoke psychological and emotional states that foster profound, meaningful, and enduring relationships with their guests (Henderson et al., 2014). Grounded in a robust theoretical foundation on relationship marketing, implementing Customer Relationship Management (CRM) initiatives is advocated as highly instrumental in attaining elevated levels of guest satisfaction. CRM has been variedly defined, for example, Kotler and Armstrong (2004, p. 16) defined CRM as “the overall process of building and maintaining profitable customer relationships by delivering superior customer value and satisfaction.”. Swift (2001, p. 12) defined it as an “enterprise approach to understanding and influencing customer behaviour through meaningful communications in order to improve customer acquisition, customer retention, customer loyalty, and customer profitability.” Similarly, Parvatiyar and Sheth (2001, p. 5) defined CRM as “a comprehensive strategy and process of acquiring, retaining, and partnering with selective customers to create superior value for the company and the customer.” Sin et al.’s (2005, p. 1266) defined CRM as “a comprehensive strategy and process that enables an organization to identify, acquire, retain, and nurture profitable customers by building and maintaining long-term relationships with them.” Thus, CRM emphasizes acquiring building, maintaining, and developing a profitable customer over a long time through a meaningful dialogue. Consequently, CRM acts as a catalyst for amplifying guest loyalty, resulting in a cascade of positive outcomes, including revisit intentions, referrals, positive word of mouth, sustained guest retention, and the establishment of enduring guest relationships. (Dah et al., 2023).

The existing body of literature has extensively delved into the intricate web of relationships between guest satisfaction and loyalty from diverse perspectives, including advisory services and capacity building (Obuobi et al., 2023), examination of customer-brand relationships (Vivek et al., 2012), and relationship marketing (Calder et al., 2013), Notably, prior research has significantly enriched this field by identifying factors that predict customer satisfaction and loyalty. In the recent times, a spotlight has emerged on the pivotal role of customer engagement in shaping and managing customer relations within the hospitality industry. Customer engagement, in essence, guarantees positive experiences across all customer touchpoints (Rasool et al., 2021). Given that the provision of hotel services unfolds through interactions between service providers and customers at various points of contact during the service encounter, it becomes imperative for hotels to prioritize the cultivation of favorable customer interactions to enhance the overall customer experience. The interplay of CRM practices and customer engagement can augment the satisfaction-loyalty relationship, thereby, conferring manifold advantages upon hospitality organizations (Nannelli et al., 2023). Despite the well-established relationship between customer satisfaction and loyalty in consumer behavior literature, the intersection of CRM practices (key customer focus, CRM organization, managing knowledge, CRM-based technology, customer prospecting, and Personalization) with these variables and the moderating role of customer engagement remains largely unexplored. The inclination towards a hotel demonstrated through customer engagement, goes beyond the mere act of purchase. It involves behavior initiated by the customer, driven by their motivations. This customer effort materializes as positive word of mouth (WOM), a significant asset that diminishes perceived risk or uncertainty among potential customers, thereby influencing their behavioral intentions (Lim and Rasul, 2022). In response to these gaps, our research aims to address two central questions: (1) How do extended CRM practices influence satisfaction and loyalty? (2) Does customer engagement moderate the satisfaction-loyalty relationship? To unravel these intricacies, we construct a conceptual model grounded in the existing literature and subsequently empirically validate its propositions. Through this investigation, we seek to contribute nuanced insights and practical implications to the evolving discourse on guest satisfaction, loyalty, and the interplay of CRM and customer engagement in the dynamic hospitality landscape.

Kashmir vis-à-vis tourism sector

A cradle of breath-taking beauty, the Kashmir Valley unfolds in the northern reaches of India’s Jammu and Kashmir union territory. Nestled between the majestic peaks of the Greater Himalayas to the northeast, and the Pir Panjal Range to the southwest, the valley boasts of stunning scenery. Its geographic coordinates lie between 32 and 34° north latitude and 74 and 75° east longitude. This region is a tapestry woven with natural wonders and vibrant cultural heritage, and opportunities for adventure tourism abound here. Popular destinations like Srinagar, Sonmarg, Gulmarg, Pahalgam,

Doodpathri, attract travelers seeking picturesque landscapes, diverse flora and fauna, and thrilling experiences. Before 2019, the region enjoyed a steady flow of visitors, however, the tourist arrivals have fluctuated in recent years. Political uncertainties and security concerns surrounding the abrogation of Article 370 in August 2019 have led to a temporary decline in tourist arrivals. The tourism industry quickly launched revival efforts, but the global COVID-19 pandemic in 2020 caused a sharp drop due to travel restrictions. With easing restrictions in 2021, domestic tourism rebounded, fueled further by promotional campaigns and infrastructural improvements in 2022–2023. According to the Department of Tourism, J&K, domestic tourist arrivals soared from a pandemic low in 2020 to a record 21.1 million in 2023. Foreign tourist arrivals increased, showing signs of recovery, with a 350% increase post-G20 meetings in 2023. This influx has translated to increased hotel stays, particularly during peak seasons. Interestingly, the hotel sector has witnessed significant growth, particularly in the 3, 4, and 5-star categories. Budget-conscious travelers found a haven in the expanding 3-star segment, while those seeking a balance between affordability and luxury opted for 4-star hotels. The 5-star category saw a remarkable expansion, catering to high-end tourists with luxurious accommodations and premium services.

Theoretical framework

The foundation of this study rests upon the integration of relationship marketing and social exchange theory. According to relationship marketing, establishing a robust connection with customers is pivotal for fostering trust and loyalty over an extended duration, ensuring sustained commitment, and nurturing enduring customer allegiance. In the realm of the tourism sector, particularly for hoteliers, constructing and sustaining meaningful relationships with guests poses a significant challenge. To fortify marketing endeavors in this context, the incorporation of intangible elements, such as loyalty, satisfaction, and positive word of mouth; implementation of CRM becomes imperative. Organizations aligned with the principles of relationship marketing consistently prioritize the cultivation of relationships with customers, endeavoring to instill brand attachment for the establishment and perpetuation of robust bonds (Morgan et al., 2024). However, in the contemporary landscape, the task of building and preserving enduring customer relationships is exceptionally daunting. A proactive approach to engaging customers and ensuring positive experiences across all touchpoints emerges as a potent strategy to influence customers to instinctively choose the advocated brand during their purchasing decisions. Furthermore, CRM is identified as a facilitator capable of maximizing customer satisfaction and loyalty to the utmost extent and competitive advantage (Alam et al., 2021). Consequently, this indirect connection between customer relationship, satisfaction, and loyalty constitutes the bedrock of relationship marketing theory.

Complementing this perspective, the study incorporates social exchange theory (Blau, 1964) to elucidate the relational dynamics inherent in customer engagement and guest loyalty. Within a social framework, individuals partake in the exchanges and evaluate the rewards and costs associated with a specific exchange, comparing what is received against what is given. The theory posits that customers possess an innate inclination to reciprocate the favors bestowed upon them by the firm/brand to sustain a stable connection. Customers benefit from high-quality service, efficient inquiry handling, customer-centric interactions, and diverse engagement programs at each touchpoint, coupled with the provision of distinctive brand experiences (Hollebeek, 2011). In return, customers express their allegiance to the firm through positive word-of-mouth and participation in organizational citizenship behaviors. In essence, relationship marketing acknowledges the underlying factors that motivate customers to forge and uphold enduring connections, while social exchange theory recognizes customers' motivation to remain loyal to a firm as a means of reciprocating the benefits received from the firm.

The proposed theoretical framework (see Figure 1) outlines the dimensions of CRM practices explored in this study, including Key Customer Focus (KCF), CRM Organization (CRMO), Managing Knowledge (MK), CRM-based Technology (CRMBT), Customer Prospecting (CP), and Personalization (PZ). These dimensions represent the strategies and actions a hotel employs to manage customer relationships effectively. The framework suggests that these CRM practices influence specific marketing outcomes, namely Guest Satisfaction (GS) and Guest Loyalty (GL). Additionally, Guest Engagement (GE) is posited to moderate the effect of CRM practices on Guest Satisfaction and Guest Loyalty. This framework underscores the significance of various CRM practices and highlights their impact on achieving favorable customer-related outcomes.

Literature review and hypothesis development

Guest satisfaction and guest loyalty

Guest satisfaction encapsulates a psychological construct centered on the sense of well-being and delight derived from attaining anticipated experiences with an attractive product or service (WTO, 1985). Within the realm of hospitality literature, it pertains to meeting guests' expectations by addressing their needs and desires. Importantly, guest satisfaction not only assures enduring revenue streams but also mitigates customer defection and complaints, augments usage patterns, and ultimately exerts a positive influence on guest loyalty (Keshavarz and Jamshidi, 2018). The recurrent behaviour of satisfied guests is characterized in stark contrast to dissatisfied guests who sever their ties with the establishment, yielding adverse outcomes (Khan et al., 2022). Consequently, managerial endeavors to fortify customer relationships become instrumental in achieving guest satisfaction and, by extension, fostering guest loyalty. This underscores the paramount importance of delivering positive experiences, as they emerge as a pivotal driver for encouraging and sustaining guest loyalty (Cheng et al., 2020). Accordingly, the following hypothesis is formulated:

H1.

There is a positive relationship between guest satisfaction and guest loyalty.

CRM practices and satisfaction-loyalty relationships

Key customer focus (KCF)

In the hospitality industry, Key Customer Focus (KCF) identifies high-value customers who consistently generate significant revenue or demonstrate exceptional loyalty and identifies a segment of guests who generate a disproportionately high share of profits over an extended period. These valuable guests warrant a strategic approach centered on a deep understanding of their specific needs and preferences. By fostering personalized relationships with key guests, hotels can cultivate loyalty and satisfaction. This translates into tailored offerings that cater to their requirements. Ultimately, this strategic focus not only strengthens the bond with key guests but also empowers managers to develop strategies that enhance the overall profitability of both key and less profitable guest segments (Josiassen et al., 2014).

Previous literature accentuates the substantial influence of Key Guest Focus (KGF) on both guest satisfaction and loyalty (Akroush et al., 2011) and business performance (AlQershi et al., 2022). Consequently, the emphasis on key customer focus not only improves overall hotel performance but also translates into economic and non-economic benefits for both the hotels and their guests. It cultivates heightened guest engagement, stimulates satisfaction, and ultimately fosters increased guest loyalty (Sofi et al., 2020; Khan et al., 2022). As a result, the following hypotheses are posited:

H2.

There is a positive relationship between key customer focus and guest satisfaction.

H3.

There is a positive relationship between key customer focus and guest loyalty.

CRM organization (CRMO)

Successfully organizing the entire business around CRM involves restructuring the organization and ensuring a company-wide commitment of resources and personnel. This comprehensive approach is referred to as CRM organization (Sin et al., 2005). The approach serves as a foundation for fundamental organizational transformation. Thus, guest-oriented behaviour thrives in a supportive work environment equipped with modern tools and technology, systems for tracking guest satisfaction and managing complaints, inspirational leadership, and a suitable reward system (Sofi and Hakim, 2018). A growing body of research highlights the positive correlation between strong CRM organization and hotel performance Akroush et al. (2011). By integrating all organizational resources into a unified system, CRM facilitates a systematic and data-driven approach to achieving and reinforcing guest satisfaction and loyalty. The hospitality sector offers a compelling case study. Dah et al. (2023) found a significant link between well-organized CRM and guest satisfaction. Furthermore, Sofi et al. (2020) add to this evidence by establishing a positive association between CRM organization and various aspects of hotel performance. Accordingly, the following hypotheses are posited;

H4.

There is a positive relationship between CRM organization and guest satisfaction

H5.

There is a positive relationship between CRM organization and guest loyalty.

Managing knowledge (MK)

In CRM, managing knowledge refers to the creation, dissemination, and utilization of guest information acquired through learning or empirical analysis of guest data (Sin et al., 2005). This information is transformed into valuable knowledge, which is then shared across the hotel to address both current and anticipated guest needs (Sofi et al., 2020). The effectiveness of CRM is gauged by its ability to convert guest knowledge into marketing intelligence, facilitate efficient guest relationships, and thereby exert a positive influence on hotel performance (Alam et al., 2021). Essentially, CRM involves the strategic management of guest knowledge to enhance guest satisfaction and loyalty (Sofi et al., 2020). The management of knowledge has been consistently linked to a positive relationship with market and financial performances (Akroush et al., 2011), and profit margins (Sofi et al., 2020). Moreover, empirical evidence emphasizes the significant impact of knowledge management on hotel performance (AlQershi et al., 2022). Therefore, the following hypotheses are posited:

H6.

There is a positive relationship between managing knowledge and guest satisfaction.

H7.

There is a positive relationship between managing knowledge and guest loyalty.

CRM-based technology (CRMBT)

In the hospitality industry, CRM technology assumes a pivotal role in the precise gathering of guest data, proving essential for the successful implementation of CRM and aiding hotels in acquiring information crucial for delivering desired services (Sofi et al., 2020; Sofi and Hakim, 2018). The integration of technology in the tourism and hospitality sector has induced shifts in guest behaviour, offering substantial opportunities for hotel establishments to gain a competitive edge (Al-Karim et al., 2024). Technologies are recognized as fundamental catalysts for change, playing a crucial role in ensuring the adoption of guest-centric strategies. Consequently, CRM-based technology empowers hotels to strategize and execute effective marketing actions, ensuring long-term guest satisfaction and loyalty; consequently, enhancing profitability (Rafiki et al., 2024). Additionally, it revealed a positive relationship between CRM-based technology and guest loyalty (Sofi et al., 2020) as well as competitive advantage (AlQershi et al., 2022). Therefore, the following hypotheses are posited:

H8.

There is a positive relationship between incorporating CRM-based technology and guest satisfaction.

H9.

There is a positive relationship between incorporating CRM-based technology and guest loyalty.

Customer prospecting (CP)

Customer prospecting involves the identification and targeting of potential customers (Reinartz and Kumar, 2003). Through customer prospecting, firms not only acquire customers but also retain them through cooperative and collaborative relationships (Wasan, 2018). In the competitive landscape of hospitality, attracting new guests, and customer prospecting is crucial. But simply acquiring guests isn’t enough, the hotels strive to build long-term relationships with them through collaborative and cooperative interactions (Wasan, 2018). This is where tools like social media come into play. By using these platforms to connect with potential guests, hotels can build a valuable database. This database allows for ongoing engagement, fostering a sense of community and leading to enhanced guest experiences (Touni et al., 2020). When positive interactions turn into valuable information, hotels gain valuable insights. This ensures guest satisfaction and cultivates loyalty (Lawson-Body and Limayem (2004)). In essence, successful customer prospecting in hospitality isn’t just about acquiring guests, it’s about transforming them into loyal advocates. Based on this perspective, the following hypotheses are posited:

H10.

There is a positive relationship between customer prospecting and guest satisfaction.

H11.

There is a positive relationship between customer prospecting and guest loyalty.

Personalization (PZ)

Adopting a customer-centric approach and providing personalized products and services add significant value to hotel guests (Krishna and Ravi, 2016). In the hospitality industry, CRM has become a prominent strategy for managing guest interactions effectively, focusing on cultivating and maintaining individual guest relationships. CRM facilitates efficient management of guest information and targeted actions (Sofi et al., 2020). Hotel managers prioritize maximizing guest touchpoints to manage relationships and create personalized guest experiences (Aksoy et al., 2021), aiming to achieve greater guest satisfaction, retention, and increased profitability, thereby making personalization unique to each guest (Chandra et al., 2022). The literature consistently supports the idea that personalization positively and significantly impacts guest satisfaction and loyalty. Notably, Lawson-Body and Limayem (2004), Tong et al. (2012), found that personalization demonstrated positive effects on guest satisfaction and loyalty, and increased guest perceptions (Wang et al., 2017). This evidence suggests that personalization contributes to enhanced business outcomes in the hospitality industry. Based on these insights, the following hypotheses are formulated:

H12.

There is a positive relationship between personalization and guest satisfaction.

H13.

There is a positive relationship between personalization and guest loyalty.

Moderating role of guest engagement

Guest engagement refers to the active participation of guests, allowing them to connect with the hotel brand on a deeper level (Afaq et al., 2023). This can involve providing feedback through surveys, participating in loyalty programs, or interacting with the hotel on social media. Effective CRM practices play a crucial role in driving guest engagement. By leveraging guest data and preferences, hotels can personalize communication, offer targeted promotions, and create a sense of exclusivity for guests (Jaakkola and Alexander, 2024). This personalized approach fosters two-way communication, encouraging guest participation and strengthening engagement. Previous studies validate the effect of guest engagement on the relationship between satisfaction and loyalty (Van Tonder and Petzer, 2018). When guests feel actively involved and valued, their overall satisfaction with the hotel experience increases. Positive experiences shared through social media or online reviews further enhance the hotel’s reputation, attracting new guests. Highly engaged guests are more likely to become loyal patrons. Their positive experience creates an emotional connection with the hotel brand (Jaakkola and Alexander, 2024). This emotional connection, fostered by engagement, strengthens the link between satisfaction and loyalty (Khan et al., 2023). Loyal guests become repeat customers, recommend the hotel to others, and are less price-sensitive. In this direction, Thakur (2019) concluded that the effect of customer satisfaction on customer loyalty is largely determined by the amount of customer engagement. Thus, it reflects that at higher levels of guest engagement, the relationship between satisfaction and loyalty strengthens (Khan et al., 2023). While research highlights the positive impact of guest engagement in other industries, the tourism sector lags (Dewnarain et al., 2018). Hotels have a unique opportunity to bridge this gap by implementing effective CRM practices that nurture guest engagement. Thus, by fostering active guest participation and creating emotional connections, hotels can cultivate a loyal customer base and achieve a sustainable competitive advantage. Hence, the following hypotheses are posited;

H14.

The impact of guest satisfaction on guest loyalty will be intensified at higher levels of guest engagement (vs lower levels of guest engagement).

H15.

The impact of CRM practices on guest satisfaction will be intensified at higher levels of guest engagement (vs lower levels of guest engagement).

H16.

The impact of CRM practices on guest loyalty shall be intensified at higher levels of guest engagement (vs lower levels of guest engagement).

Method

Research instruments

In the study, two research instruments were adapted from the extant studies and are detailed in Appendix 1, with separate parts for customer/guest respondents (Part A) and employee respondents (Part B). The survey instrument for customer respondents comprised 14 items across three constructs: guest satisfaction, guest loyalty, and guest engagement. The items for guest satisfaction and guest loyalty were adapted from Çavusoglu et al. (2020). The items for guest engagement were adapted from Verhagen et al. (2015). The instrument for employee respondents included six constructs related to CRM practices: key customer focus, CRM organization, managing knowledge, CRM-based technology, customer prospecting, and personalization. These constructs were adapted from Sofi and Hakim (2018), who originally developed them based on the works of Sin et al. (2005) and Lawson-Body and Limayem (2004). Both instruments used a five-point Likert scale, with responses ranging from 1 (strongly disagree) to 5 (strongly agree). Additionally, both the employee and customer questionnaires included questions to capture the demographic details of the respondents, which were measured on a nominal scale. Initially, two hotel managers and three expert academicians provided suggestions and feedback on the readability and relevance of the statements to ensure the content validity of the scale.

Sample and data collection

The study utilized a pen-and-paper survey with convenience sampling to gather data from guests and employees at hotels in Kashmir. Only 10 hotels qualified for data collection, based on meeting specific inclusion criteria:

• (1)

  Implementation of Customer Relationship Management (CRM) initiatives.

• (2)

  Being classified as 3-star, 4-star, or 5-star establishments.

Within each hotel, 27 guests and 27 employees were surveyed to ensure equal representation across the sample. This resulted in a total of 270 guest responses and 270 employee responses. Data collection was conducted over 10 weeks. A trained team of surveyors coordinated with hotel management to schedule visits, minimize disruptions, and obtain necessary permissions. Guests were approached in common areas where surveyors explained the study, obtained verbal consent, and administered the survey. Two filter questions were used to screen guests:

• (1)

  “Have you stayed at this hotel before?” (Yes/No)

• (2)

  “Please recall your experiences at the hotel and provide the name of the employee who served you.”

Only those guests who had previously stayed at the hotel were considered eligible to participate (repeat-stay guests) as the study aimed to provide a more comprehensive perspective on guest satisfaction and loyalty. Further, only those guests who could recall the name of the employee who served them were included as data matching with employees was not possible for others. Each guest respondent at each hotel was allocated a specific code, and the same code along with the name of the employee mentioned by the guest was written on the employee questionnaire. Employees identified by guests were contacted through hotel management to participate in the survey. Only mid and lower-level executives were considered as they are mainly responsible for the implementation and execution of CRM practices and handling guests. Moreover, executives who have worked for two years at the hotel were eligible to participate in the survey to provide genuine responses to the CRM practices adopted by the hotel.

Following the collection, the data underwent a structured matching and verification process (Schneider and Bowen, 1985). Filled-in questionnaires obtained from guests were matched with those of employees using the same unique codes assigned to each pair. The accuracy of matched data was then verified for inconsistencies (e.g. missing data, unmatched and unverifiable data) and cross-checked with hotel records. After excluding 20 incomplete/unmatched/unverifiable data points the final matched data set (n = 270), composed of verified guest-employee pairs, was compiled for subsequent analysis. This rigorous procedure ensured the reliability and validity of the data for subsequent research analysis.

The executive and customer respondents' profiles are presented in Tables 1 and 2, respectively. The executive respondents comprised around 83.70% males and 16.30% females. The majority of the executive respondents were from lower levels of management (88%) and the remaining were from mid-level management (around 12%). Regarding education, most executive respondents were graduates (around 63%) and the remaining 30% were post-graduates and above. The tenure of service data for the hotel employees indicated that most employees (77.10%) have been with their hotels for 5–15 years. A smaller segment, 5.92%, consists of employees with over 15 years of tenure. Meanwhile, 15.92% of employees are relatively newer, with 2–5 years of service.

The demographic data of hotel guests reveal a predominantly male clientele, accounting for 63.33% of the guests, with females making up 36.66%. A significant majority, 70% are married and age-wise distribution shows that 58.88% of guests are under 35 years old, while 30% are aged 36–50, and 11.11% are over 50 years. In terms of education, 45.55% of guests have a graduate-level education, and 38.5% have postgraduate degrees, while 15.92% are undergraduates. The monthly income distribution of hotel guests reveals a diverse clientele with nearly half (48.15%) earning more than 2 and but less than 5 Lakhs. High-income guests, earning over 5 Lakhs, make up 31.48%, meanwhile, 20.37% of guests earn up to 2 Lakhs, reflecting a segment of budget-conscious tourists.

Data analysis

The collected and matched data was subjected to descriptive analysis, structural equation modeling, and moderation analysis using SPSS (Version 26.0), AMOS (Version 26.0), and PROCESS Macro, respectively. A two-step SEM recommended by Anderson and Gerbing (1988) was employed. In the first step, the measurement model, specifying the relationships between observed variables (indicators) and their underlying latent constructs through confirmatory factor analysis (CFA) was assessed. The model fit was evaluated using several goodness-of-fit indices, following the threshold limits recommended by Byrne (2016) and Hair et al. (2019). These indices include the chi-square to degrees of freedom ratio (χ²/df) with a threshold level between 1 and 3, and the Root Mean Square Error of Approximation (RMSEA) with values less than 0.08 indicating a good fit. Additionally, the Comparative Fit Index (CFI), Adjusted Goodness of Fit Index (AGFI), and Normed Fit Index (NFI) should all be greater than 0.90. Finally, the Standardized Root Mean Square Residual (SRMR) should be less than 0.08 to indicate a good fit.

In the second step, the structural model was specified to test the hypothesized relationships between latent constructs using maximum likelihood estimation with bootstrapping to enhance the accuracy of the parameter estimates and their standard errors. The significance of path coefficients was examined, with a p-value less than 0.05 indicating statistical significance. The strength of the effect was assessed based on the magnitude of the path coefficient. The same fit indices used in the measurement model were applied to assess the structural model’s fit. PROCESS macro was utilized in SPSS to investigate whether the effect of an independent variable (IV) on a dependent variable (DV) was moderated by guest engagement. The analysis involved specifying a simple moderation model (Model 1 in PROCESS), where an interaction term between the IV and the moderator was included. The moderation analysis began by defining the independent variable, dependent variable, and moderator, followed by the automatic creation of an interaction term by PROCESS. The model estimation provided results, including interaction effects, which were interpreted to determine the presence of moderation. A significant interaction term indicated moderation which was further probed to explore how the relationship between the IV and DV changed at different levels of the moderator.

Results

Common method bias

Harman’s single-factor test was used to evaluate common method bias (CMB). The results demonstrated the absence of CMB because only 31% of the variance was accounted for by a single factor, which is less than the 50% requirement (Podsakoff et al., 2003). Moreover, multicollinearity was assessed by calculating the inter-construct correlation and variance inflation factor (VIF). Results demonstrate that all correlation coefficients were well below the threshold limits of 0.08 and VIF values were below 5 (Hair et al., 2019).

Measurement model

In the first step of SEM, the measurement model was examined by evaluating the goodness of fit indices. The model demonstrated an adequate fit to the data according to the specified criteria. The Adjusted Goodness of Fit Index (AGFI) was 0.94, the Normed Fit Index (NFI) was 0.93, and the Comparative Fit Index (CFI) was 0.96, all exceeding the recommended threshold of 0.90 as recommended by Hair et al. (2019), indicating a strong fit. The Root Mean Square Error of Approximation (RMSEA) was 0.036, well below the threshold of 0.08, suggesting a close fit of the model in the population. The chi-square statistic was 239.21 with 129 degrees of freedom, resulting in a χ²/df ratio of 1.85, which falls within the acceptable range of 1–3 (Byrne, 2016). Additionally, the Standardized Root Mean Square Residual (SRMR) was 0.041, below the cut-off of 0.08 as recommended by Byrne (2016), further confirming the model’s adequacy. These fit indices collectively indicate that the model is a good representation of the observed data. Subsequently, the reliability and validity estimates were assessed. The instrument’s reliability was measured by assessing Cronbach’s alpha and composite reliability (CR). The alpha values for all constructs ranged from 0.812 to 0.953, and the CR values ranged from 0.784 to 0.941, thus, the values were above the threshold limit of 0.70 recommended by Nunnally (1978) as reflected in Table 3. Moreover, the item loading exceeded 0.6 indicating a strong relationship between the observed and the latent construct, thus, confirming convergent validity. It reflects that the study used strong, reliable indicators that enhance the validity and reliability of the measurement model, indicating that the items are effectively measuring their respective latent constructs.

The discriminant validity was examined by comparing the average variances extracted (AVE) and squares of the correlations, as shown in Table 4. The results show sufficient convergence because the square of the correlation between factors was less than AVE (Fornell and Larcker, 1981). Additionally, the heterotrait-monotrait ratio (HTMT) was used to assess the discriminant validity. (HTMT) ratio was evaluated using the HTMT 0.90 criteria proposed by Henseler et al. (2015). As shown in Table 5, the HTMT values for all construct values are less than 0.90, thus, confirming validity.

Hypotheses testing

In the second step of the SEM process, the structural model was specified based on the proposed relationships between the latent constructs. The model fit was evaluated using the same fit indices applied in the measurement model, all of which indicated a good fit. The Root Mean Square Error of Approximation (RMSEA) was 0.049, and the Standardized Root Mean Square Residual (SRMR) was 0.037, both within acceptable limits as recommended by (Byrne, 2016). The Goodness of fit was 0.98, the Adjusted Goodness of Fit Index (AGFI) was 0.98, the Comparative Fit Index (CFI) was 0.97, and the Normed Fit Index (NFI) was 0.96, all exceeding the recommended threshold of 0.90 (Hair et al., 2019). The chi-square statistic was 241.431 with 131 degrees of freedom, resulting in a χ²/df ratio of 1.84, indicating a good fit as per Byrne (2016). Thus, fit indices confirmed that the model fits the data well. The results show an adjusted R-square of 0.56 in customer satisfaction indicating that 56% of the variance in customer satisfaction is explained by CRM practices. The findings indicate personalization demonstrated a stronger and positive effect on guest satisfaction (β = 0.31, t = 4.21, p = 0.001), thus confirming H12; followed by key customer focus (β = 0.25, t = 4.09, p = 0.001), therefore acknowledging H2. The effects of customer prospecting (β = 0.24, t = 4.96, p = 0.001) and managing knowledge (β = 0.20, t = 2.01, p = 0.031), confirming H10 and H6, respectively. Further, results indicate that CRMBT (β = 0.19, t = 5.02, p = 0.001) and CRM organization (β = 0.15, t = 2.22, p = 0.025) revealed a positive effect on customer satisfaction, thus supporting H8 and H4, respectively (Table 6). The finding therefore demonstrates that customer relationship management practices like key customer focus, CRM organization, knowledge management, CRM-based technology, customer prospecting, and personalization are the predictors of guest satisfaction.

Table 6 also presents the path analysis between CRM practices and guest loyalty, showing an adjusted R-square of 0.53. It demonstrates that 53% of the variance in guest loyalty is explained by CRM practices. The result shows CRMBT (β = 0.36, t = 6.31, p = 0.000), and CP (β = 0.31, t = 5.39, p = 0.002) produced a positive impact on guest loyalty. Thus, confirming H1, H9, and H11, respectively. Further, the results demonstrate Personalization (β = 0.31 t = 3.38, p = 0.007) exhibited a positive effect on guest loyalty, confirming H13. On the other hand, the findings indicate, that MK (β = 0.19, t = 2.32, p = 0.021) and KCF (β = 0.19, t = 2.94, p = 0.007) exerted a weak but positive effect on guest loyalty; therefore, support research hypotheses H7 and H3. The result, therefore, demonstrates that hotels have been realizing guest loyalty through strong customer relationships. The results further suggest guest satisfaction (β = 0.41, t = 7.21, p = 0.000) produced a strong and positive effect on guest loyalty. The study concludes, that guest loyalty increases with an increasing level of guest satisfaction.

To conduct moderation analysis, all the dimensions of CRM confirmed in CFA are computed and transformed into a composite construct as CRM in SPSS. The moderation analysis was conducted in PROCESS macro using Model 1 with guest engagement as moderator. First, guest satisfaction was entered as the independent variable, guest loyalty as the dependent variable, and guest engagement as the moderator. The analysis involved regressing guest loyalty on guest satisfaction, guest engagement, and their interaction term to assess the moderation effect. As shown in Table 7 the moderation was significant (F = 23.4, p < 0.05) with an R-square of 0.231. The interaction term (GE*GS) was significant (β = 0.141, p < 0.05), indicating a positive moderating effect of GE on the relationship between GS and GL. Thus, H14 was accepted. Moreover, the change in R-square (ΔR² = 0.012, p < 0.05) after adding the interaction also confirmed moderation is significant. To probe the moderation effect further, we assessed the conditional effect of GE. The results indicate that at higher values of GE (m+1), GS has a more substantial effect on CL (β = 0.453, p < 0.05) than at the mean value of GE, (β = 0.149, p < 0.05), and this effect further reduces and is weak and insignificant (β = 0.012, p > 0.05) at the lower values of GE (m-1). Thus, it reflects that the positive effect of Guest satisfaction on guest loyalty intensified at higher levels of guest engagement than at low levels of engagement.

As shown in Table 8, the moderating effect of GE on CRM practices and GS revealed that the interaction term (CRM practices*GE) was significant (β = 0.241, p < 0.05). These findings support H15. The moderating effect was also supported by the change in R-square value, which improved significantly (ΔR² = 0.021, p < 0.05) after the interaction term was added to the model. While probing the conditional effect of GE, it was found that at higher values of GE (m+1), CRM practices have a more substantial effect on GS (β = 0.545, p < 0.05) than at the mean value of GE. At the mean value, this effect reduces but is still significant (β = 0.256, p < 0.05), however, at the lower values of GE (m-1) this effect further reduces and is weak and insignificant (β = 0.012, p > 0.05). Therefore, it reflects that the positive effect of CRM practices on guest satisfaction intensifies at higher levels of guest engagement.

Next, the moderating effect of GE on CRM practices and GL was examined. We observed that the interaction term (CRM practices*GL) was significant and positive (β = 0.211, p < 0.05). Additionally, the inclusion of the interaction term in the model resulted in significant improvements in R-square (ΔR² = 0.0212, p < 0.05), which validates the moderating influence. The conditional effects of independent variables on guest loyalty at three levels of moderation: mean (average level of engagement), mean + 1 SD (high level of engagement), and mean-1SD (low level of engagement) are probed. As shown in Table 9, CRM practices have a more profound and significant impact on guest loyalty at higher levels of guest engagement (m+1), (β = 0.464, p < 0.05). While this effect is still significant at the mean value of guest engagement (β = 0.212, p < 0.05) but lesser in magnitude and it becomes weaker and less significant (β = 0.021, p > 0.05) at lower levels of guest engagement (m-1). These findings support H16, thus, confirming that the effect of CRM practices on guest loyalty intensifies when guest engagement is higher than when guest engagement is low.

Discussion and conclusions

Conclusions

The main aim of the research was to investigate the effect of CRM practices on guest satisfaction and loyalty. Also, the study aims to examine the moderating impact of guest engagement on guest satisfaction and loyalty. The findings indicate a noteworthy impact of CRM practices on both guest satisfaction and guest loyalty. The findings demonstrate that effective CRM implementation fosters guest satisfaction and loyalty, which in turn drives the intended business outcomes. As evidenced by the results, hotel guest satisfaction is most strongly predicted by CRM practices, specifically PZ and KCF. Additionally, there was a positive correlation found between CP and GL, suggesting that CP is a powerful predictor of hotel guest loyalty. The considerable impact of CRMO on guest satisfaction is one of the study’s other key findings. In addition, hotel guest loyalty has benefited greatly from CRM-based technology. This has been possible due to the assistance of technical experts in the redressal of guest grievances. The necessary IT infrastructure further helps hotels serve their guests promptly, therefore exerting a significant influence on guest satisfaction and loyalty. About guest loyalty, MK and CP dramatized greater influence. The frequent interactions with hotel guests, soliciting feedback from them, and subsequent translation into useful knowledge foster this relationship, thus, confirming the evidence of AlQershi et al. (2022).

Guest engagement moderates the relationship between guest satisfaction and loyalty, suggesting a positive impact of guest satisfaction on guest loyalty intensifies at high versus low levels of guest engagement. One notable finding is that the effect of CRM practices on guest satisfaction and loyalty intensifies when guest engagement is higher than when guest engagement is low. The findings clearly show that when guest engagement levels are high, the beneficial impact of CRM practices on guest satisfaction and loyalty strengthens, and at lower levels, it diminishes. Consequently, the degree of guest engagement determines the strength of the relationship between CRM practices and guest satisfaction and loyalty (Thakur, 2019).

Theoretical implications

Our research contributes to hospitality theory in several ways. We extend existing knowledge by empirically validating the direct and indirect relationship between guest satisfaction and loyalty, emphasizing the pivotal role of CRM practices. Additionally, we illuminate the mediating influence of guest engagement on this relationship, deepening our understanding of relational dynamics. Furthermore, we highlight the moderating impact of CRM practices on the satisfaction-loyalty link, emphasizing its strategic importance. Our study also pioneers research on the role of technology in guest engagement, underscoring its potential to enhance customer experiences. By examining the complex interplay of these factors, we offer a nuanced perspective on building and maintaining strong guest relationships in the hospitality industry.

Managerial implications

The study provides valuable insights for hotel managers, emphasizing the necessity of personalized service to stay competitive in the highly demanding hospitality industry. Establishing and maintaining a loyal clientele depends on creating interactive and collaborative relationships with guests, which are essential for sustainable success in the corporate world. Hospitality businesses can greatly benefit from implementing Customer Relationship Management (CRM) framework, which optimizes customer value, increases guest satisfaction, and enhances both financial and non-financial business performance (Al-Karim et al., 2024). For this to be effective, hotel staff must be adequately trained to meet guest requirements and provide personalized services and offerings. Furthermore, the study suggests that managers in the hospitality sector should refine their CRM strategies to prioritize guest acquisition and interaction through various techniques. Continuous investment in employee skill development and fostering a customer-centric business culture are crucial for effectively meeting customer needs. Additionally, involving employees at all levels in customer-related activities is a key factor in enhancing hotel performance. Moreover, hospitality organizations should aim to go beyond mere customer satisfaction to ensure loyalty by optimizing touchpoints for recording customer data and delivering real-time experiences. Highly customized and interactive engagements can build strong connections between guests and hospitality organizations. Segmenting guests based on engagement levels allows for targeted efforts to improve satisfaction, thereby increasing the likelihood of guest retention. These strategies collectively enhance the overall performance and competitiveness of hotels in the tourism industry.

Limitations and future research

This study’s scope is limited to hotels in the Kashmir region, restricting generalizability. Additionally, the focus on customer satisfaction and loyalty as outcome measures precludes a comprehensive assessment of CRM’s impact on overall business performance. The cross-sectional design hinders causal inferences, while the exclusive focus on guest engagement as a moderator limits the exploration of other potential moderating factors. To address these limitations, future research should expand the sample to include diverse hospitality sectors, incorporate a wider range of performance metrics, adopt longitudinal designs, and explore additional moderators.

Figures

Figure 1

Proposed framework

Table 2

Demographic characteristics of the customer respondents (n = 270)

Characters	Class	Frequency	Percentage
Gender	Male	171	63.33
	Female	99	36.66
Marital status	Married	189	70.00
	Unmarried	81	30.00
Age	Up to 35	159	58.88
	36–50	81	30.00
	Above 50	30	11.11
Education	Undergraduate	43	15.92
	Graduate	123	45.55
	PG and above	104	38.51
Monthly income	≤2 Lakhs	55	20.37
	>2 Lakh and ≤5 Lakhs	130	48.15
	>5 Lakh	85	31.48

Source(s): Authors’ own work

Table 3

Factor loading and reliability measures

Variables	Item loadings	Cronbach’s alpha	Composite reliability
KCF1	0.714	0.881	0.873
KCF2	0.769
KCF3	0.841
KCF4	0.863
KCF5	0.789
KCF6	0.764
CRMO1	0.869	0.825	0.819
CRMO2	0.904
CRMO3	0.819
CRMO4	0.793
CRMO5	0.715
CRMO6	0.751
MK1	0.739	0.791	0.784
MK2	0.830
MK3	0.813
CRMBT1	0.744
CRMBT2	0.702	0.803	0.792
CRMBT3	0.813
CRMBT4	0.7930
CRMBT5	0.766
CP1	0.816	0.863	0.851
CP2	0.871
CP3	0.769
CP4	0.840
PZ1	0.791	0.801	0.787
PZ2	0.779
PZ3	0.831
GE1	0.716	0.941	0.928
GE2	0.853
GE3	0.873
GE4	0.765
GS1	0.828	0.914	0.903
GS2	0.850
GS3	0.820
GS4	0.749
GS5	0.839
GL1	0.778	0.871	0.859
GL2	0.739
GL3	0.728
GL4	0.820
GL5	0.818

Source(s): Authors’ own work

Table 4

Validity measures

Construct	KCF	CRMO	MK	CRMBT	CP	PZ	CE	CS	CL
KCF	0.62А
CRMO	0.06*	0.61А
MK	0.27**	0.12**	0.60А
CRMBT	0.15**	0.03*	0.02*	0.66А
CP	0.08*	0.28**	0.05*	0.21**	0.71А
PZ	0.07*	0.18**	0.01*	0.13*	0.40**	0.58А
GE	0.25**	0.17**	0.03*	0.07*	0.12**	0.19**	0.51A
GS	0.11**	0.09*	0.06*	0.12**	0.18**	0.23**	0.31*	0.62A
GL	0.03*	0.19**	0.19**	0.22**	0.31**	0.17**	0.20*	0.31*	0.59A

Note(s): ^*denotes significance at 0.05, ^**denotes significance at 0.001, ^Adenotes average variance extracted

Source(s): Authors’ own work

Table 5

Validity measures (HTMT ratio)

Constructs	KCF	CRMO	MK	CRMBT	CP	PZ	GE	GE	GL
KCF	1
CRMO	0.781	1
MK	0.831	0.819	1
CRMBT	0.510	0.593	0.821	1
CP	0.793	0.813	0.783	0.701	1
PZ	0.779	0.735	0.813	0.811	0.793	1
GS	0.763	0.782	0.791	0.613	0.689	0.639	1
GE	0.778	0.639	0.539	0.379	0.579	0.631	0.459	1
GL	0.486	0.713	0.333	0.341	0.810	0.430	0.337	0.349	1

Source(s): Authors’ own work

Table 6

Hypothesis results

Hypothesis	Path	β value	t-value	Sig. value	Remarks
H1	GS → GL	0.41	7.12	0.000	Supported
H2	KCF → GS	0.25	4.09	0.003	Supported
H4	CRMO → GS	0.15	2.22	0.025	Supported
H6	MK → GS	0.20	2.01	0.031	Supported
H8	CRMBT → GS	0.19	5.02	0.000	Supported
H10	CP → GS	0.24	4.96	0.001	Supported
H12	PZ → GS	0.31	4.81	0.001	Supported
H3	KCF → GL	0.19	2.94	0.007	Supported
H5	CRMO → GL	0.18	3.05	0.004	Supported
H7	MK → GL	0.36	2.32	0.021	Supported
H9	CRMBT → GL	0.25	6.31	0.000	Supported
H11	CP → GL	0.31	5.39	0.002	Supported
H13	PZ → GL	0.27	3.38	0.007	Supported

Source(s): Authors’ own work

Table 7

Moderation analysis: GE moderator

Hxx	GS → GL
GS		0.325*
GE		0.156*
Interaction (GS*GE)		0.141*
Constant		4.042*

Conditional effects on CL at different values of GE
Values of GE	Effects of GS
Mean −1SD	−1.323	0.012(ns)
Mean	0	0.149*
Mean +1SD	1.323	0.453*

Note(s): Dependent variable: customer loyalty, ^**p < 0.05, ^*p < 0.01 ns: non-significant, SD: standard deviation

Source(s): Authors’ own work

Table 8

Moderation analysis: GE moderator

Hxx	CRM → GS
GS		0.423*
GE		0.213*
Interaction (CRM*GE)		0.241*
Constant		6.042*

Conditional effects on GS at different values of GE
Values of GE		Effects of GS
Mean −1SD	−1.76	0.0212(ns)
Mean	0	0.256*
Mean +1SD	1.76	0.545*

Note(s): Dependent variable: GS, ^**p < 0.05, ^*p < 0.01 ns: non-significant, SD: standard deviation

Source(s): Authors’ own work

Table 9

Moderation analysis: GE moderator

Hxx	CRM → GL
CRM		0.411*
GE		0.221*
Interaction (CRM*GE)		0.211*
Constant		5.042*

Conditional effects on GL at different values of GE
Values of GE		Effects of GS
Mean −1SD	−1.67	0.021(ns)
Mean	0	0.212*
Mean +1SD	1.67	0.464*

Note(s): Dependent variable: GL, ^**p < 0.05, ^*p < 0.01 ns: non-significant, SD: standard deviation

Source(s): Authors’ own work

Table A1

Research instrument

Part-A		Instrument for customers
Constructs	Code	Items	Source
Guest satisfaction	GS1	I feel satisfied with the hotel’s accommodation	Çavusoglu et al. (2020)
	GS2	I am pleased with the services of the hotel
	GS3	My decisions to avail the services of the hotels was wise
	GS4	The performance of the hotel has met your expectations
	GS5	The satisfaction level of this accommodation is quite close to my ideal accommodation
Guest loyalty	GL1	I will speak positively about my experience at this hotel	Çavusoglu et al. (2020)
	GL2	I will stay at this hotel on my next visits
	GL3	I will recommend this hotel to others
	GL4	This hotel is my first choice among others
	GL5	I intend to stay on as a guest of this hotel
Guest engagement	GE1	I want to help other guests with their questions	Verhagen et al. (2015)
	GE2	I want to help the hotel to improve its service
	GE3	I pay a lot of attention to any information about the hotel
	GE4	I enjoy exchanging ideas with other hotel guests

Part – B		Instrument for employees
Construct	Code	Items	Source
Key customer focus	KCF1	Our hotel provides service as per the individual requirements of the customer(s)	Sofi and Hakim (2018) Sin et al. (2005)
	KCF2	Our hotel strives to constantly improve services beyond customer expectations
	KCF3	Our hotel strengthens the emotional bonds with customers by wishing them on important occasions
	KCF4	Our hotel charges comparatively less for its offerings (accommodation etc)
	KCF5	Our hotel employee directly works with individual customer(s)
	KCF6	All the people in our hotel treat customers with great their care
CRM organization	CRMO1	Our hotel guides building and maintaining long-lasting customer relationships	Sofi and Hakim (2018) Sin et al. (2005)
	CRMO2	Our hotel commits time and resources to managing customer relationships
	CRMO3	It is easy to use the offerings of the hotel
	CRMO4	Generally, Our hotel doesn’t offer rewards/discounts on its offerings
	CRMO5	My hotel delivers consistent customer service across all customer touchpoints
	CRMO6	Customer-centric performance standards are established and monitored at all customer touchpoints
Managing knowledge	MK1	Our hotel provides an effective guarantee for service failures	Sofi and Hakim (2018) Sin et al. (2005)
	MK2	At the hotel, customer feedback is taken through E-mails, schedules (Questionnaires)etc.
	MK3	Our hotel has a process in place to obtain and validate customer’s permission to interact with them through various channels
CRM-based technology	CRMBT1	Our hotel has automated marketing, sales, and other departments	Sofi and Hakim (2018) Sin et al. (2005)
	CRMBT2	Our hotel doesn’t have well developed Customer Information System*
	CRMBT3	The hotel’s Customer Relationship Management (CRM) provides for the integration of touch points to obtain a single view of customers at every point of customer contact
	CRMBT4	My hotel has the right infrastructure to serve customers promptly
	CRMBT5	My hotel has technical staff to provide technical support to customers
Customer prospecting	CP1	My hotel tracks and prospects for new customer(s)	Sofi and Hakim (2018) Lawson-Body and Limayem (2004)
	CP2	My hotel doesn’t devote resources to help in the information of new customers
	CP3	My hotel distributes brochures, booklets, pamphlets, web pages, etc., to share their action plan with the customer(s)
	CP4	My hotel salespeople use references while reaching out to prospective customers
Personalization	PZ1	My hotel sends customized mail to customers	Sofi and Hakim (2018) Lawson-Body and Limayem (2004)
	PZ2	The customer complaints are addressed on a one-to-one basis in the hotel
	PZ3	My hotel manages its customer problem(s) while dealing with electronic transactions

Note(s): * denotes negative item

Source(s): Compiled by authors

Appendix 1

Table 1

Demographic characteristics of the executive respondents (n = 270)

Characteristics	Category	Frequency	Percentage
Gender	Male	226	83.70
	Female	44	16.29
Management level	Middle	32	11.86
	Lower	238	88.14
Tenure at current hotel	>2 years and ≤5 years	43	15.92
	>5 years and ≤10 years	136	50.37
	>10 years and ≤15 years	75	27.77
	>15 years	16	05.92
Educational qualification	Graduate	170	62.96
	Post Graduates and above	55	37.04

Source(s): Authors’ own work