Subjective Norms, Innovation Source and Customer Satisfaction Among Small Hospitality Firms in Ghana

Abstract

This study examined the relationships between norm perceptions about innovation, innovation source and customer satisfaction with sample data from small-scale hospitality businesses in Ghana. We adopted the quantitative approach and correlational survey design using sample data from 465 small-scale hospitality firms. Partial Least Squares Structural Equation Modelling was used to analyse the data. Measurement model classification and validation procedures comprised construct specification, indicator reliability assessment, internal consistency reliability, convergent validity (AVE), discriminant validity (HTMT and Fornell–Larcker), and collinearity diagnostics within the PLS-SEM framework. Results showed that a significant negative relationship exists between subjective norms about innovation adoption and customer satisfaction. This finding diverges from the Theory of Planned Behaviour because, contrary to its assumption that subjective norms foster positive behavioural outcomes, socially driven innovation in small-scale hospitality settings may encourage conformity-based decisions that undermine customer-oriented value creation. However, a significant positive relationship was found to exist between subjective norm perceptions about innovation adoption and innovation source. A significant positive relationship was also found to exist between innovation source and customer satisfaction. Innovation source positively mediated the relationship between subjective norm perceptions about innovation adoption and customer satisfaction. The study’s findings are relevant for owners and managers of small-scale hospitality firms seeking to align innovation decisions with customer needs, as well as for policymakers aiming to strengthen industry support systems. It offers insights into how social influences and innovation sources can be leveraged to enhance service quality and customer satisfaction in small hospitality businesses.

1. Introduction

1.1. Background

The adoption of innovation has fast emerged as a key influencer of organisational survival and competitiveness in the modern hospitality industry, particularly among small- and medium-scale enterprises (SMEs), operating in highly dynamic, customer-oriented environments (Chotisarn & Phuthong, 2025). The dramatic pace of technological development, ever-increasing customer service requirements and changing market conditions have altered the concept of innovation from a competitive advantage to a necessity for the sustainable future of SMEs (Giannoukou, 2024). In this context the small-scale hospitality business’s ability to innovate is a critical determinant not only of its operating efficiencies but also of its capability to respond to changing customer preferences and environmental constraints (D’Souza & D’Souza, 2023).

Additional studies confirm that in a hotel context, innovation goes beyond technological innovations and includes also the reengineering of the service process and electronic systems for service delivery and customer relations, aiming to improve hotel productivity, competitiveness and service quality (Mariani et al., 2018; Martín-Rios & Ciobanu, 2019). In this way, innovation turns into a key competence for small hospitality organisations with the purpose of guaranteeing customer satisfaction and maintaining their presence on the market in the long term (Mariani et al., 2018). However, the adoption of innovation in small-scale hospitality enterprises is heavily influenced by social, psychological and environmental conditions, which affect the interpretation by managers and their responses to pressures for innovation (Hagger & Hamilton, 2024). Among the social influences, subjective norms as defined and understood as the perceived social expectations affecting managerial behaviour and have become a strong determinant of how managers accept and implement innovations (Chan et al., 2023).

Dwelling on the Theory of Planned Behaviour, subjective norms are those expectations of others such as customers, competitors, or members of professional networks that can influence an individual’s intentions to perform a behaviour and consequently an organisation’s behaviour (Ajzen, 2020). Subjective norms could have a high influence in service-oriented organisations, such as hospitality businesses, where companies must take into consideration the reputational consequences of their actions, as well as the influence of friends, colleagues and customers when making certain choices or introducing new products and services (Kashif et al., 2018; Latip et al., 2022). Accordingly, it is important to examine subjective norms, their effects on the adoption of innovation, and the impact that the adoption has on customer satisfaction (Richardson et al., 2023). This has become an essential part of improving performance and securing the future sustainability of small-scale hospitality firms (Guo et al., 2024). In the small-scale hospitality environment, innovation adoption takes place in a complex and intertwined environment of social relationships, which create the correctly motivated managerial intention to innovate (Burton et al., 2025). Lee et al. (2021) attests that the innovative behaviour of owners and managers of small-scale hospitality firms is influenced by the expectations and behaviour of relevant peer groups including customers, peers, competitive enterprises and professional associations. These social pressures form the basis of the subjective norm framework that either motivates or inhibits innovative behaviour in small hospitality environments (Polat et al., 2021).

Small hospitality operators do not have formalised structures and research and development units as found in large corporations but rather innovate reactively according to the constraints of what is compatible or acceptable in their own small business eco-systems (Yang et al., 2022). Previous studies have indicated that many SMEs in the hospitality industry are reliant on formal and informal networks, industry association membership and customer feedback to aid them when making innovation decisions as a result of limited in-house knowledge (Agyapong et al., 2018; N. Kim & Shim, 2018; Teixeira et al., 2019). Consequently, social legitimacy and peer expectations become critical drivers of innovation behaviour in such firms (Kallmuenzer et al., 2021; Pateli et al., 2020).

Within this context, subjective norms constitute an important element in the innovation process of managers, since the expectations of customers, competitors and peers can condition the choices of managers when considering the access to the sources of innovation (Ajzen, 2020; Dias et al., 2023). When managers feel pressure to innovate, they interact more intensely with suppliers, tourism boards and technology companies in order to obtain the knowledge and the inputs they need to achieve the expected level of innovation in their sector (Giotis & Papadionysiou, 2022; C. Wang et al., 2020).

Furthermore, stronger subjective norm perceptions increase managers’ responsiveness to reputational and competitive pressures within their business ecosystems. As normative expectations rise, managers proactively scan their environment, bolster inter-firm connections and draw on social capital to obtain credible innovation inputs. Specifically, subjective norms serve as moral catalysts that shift managerial attention towards a wider and embedded context for sourcing innovation inputs, increasing the shallow and deep search of knowledge needed to adopt innovation. Empirical studies in hospitality highlight the impact of social influence on business decisions and thus encourage businesses to look externally to achieve innovative solutions (Goll & Zieba, 2025; Egwuonwu et al., 2023). This behaviour for innovative solutions that is stimulated by social influence improves the firm’s access to innovation sources such as suppliers, business networks and technology or knowledge-sharing platforms in tourism value chains (Marasco et al., 2018). Indeed, a range of open-innovation literature argues that the increasing knowledge intensity of modern firms makes them more likely to link internal knowledge sources with external sources of information such as technologies and markets in order to facilitate internal knowledge creation and innovation outcomes (Bogers et al., 2019; Chesbrough, 2020). Thus, because of more interaction with different innovation sources, the chances of successful innovation adoption increases, which eventually leads to development of service quality and higher customer satisfaction in small-size hospitality firms.

The relationship that exists between innovation adoption and customer satisfaction is complex and contextually determined. Although studies argue that innovations are generally associated with improvement of service quality, efficiency and customer satisfaction, the positive outcomes are not always guaranteed (Mutuku & Wambua, 2019; Richardson et al., 2023). Researchers argue that the extent to which innovation improves customer satisfaction also depends on the source of the innovation, which establishes whether it is internally developed or externally acquired or sourced (Khassawneh et al., 2024; Mutuku & Wambua, 2019). Internally developed innovations are generally considered to be more compatible with the culture, operational processes and customer expectations of the firm as opposed to externally sourced innovations, e.g., franchised innovation technologies or imported business models, etc., which offer proven solutions (Sigala, 2018; Kaewkamol & Chen, 2023).

1.2. Research Gaps

Empirical studies in the hospitality literature consistently emphasise the significant role of social and psychological parameters in innovation behaviour (Chen et al., 2021; Rodríguez-Sánchez et al., 2020). That notwithstanding, most of these studies have focused on the context of large companies, such that small-scale hospitality firms, which are more reliant on social legitimacy and peer pressure, remain woefully under-studied (Fu et al., 2020). Studies on hospitality SMEs also appear to confirm that there is limited research into innovation in small hospitality businesses, and that what exists is fragmented and does not sufficiently focus on the behavioural factors that influence the management’s decision to innovate (Kumar et al., 2024; Cao et al., 2022).

While customer satisfaction remains paramount in hospitality studies, there has been limited research on the effect of subjective norm perceptions about innovation adoption on customer satisfaction (Chan et al., 2023) as well as the mediating effect of innovation source on these relationships (Latip & Sharkawi, 2021), thereby creating a research gap. Furthermore, most studies explored the direct impact of innovation adoption on different organisational performance and service quality dimensions without adequately explaining how social pressures influence the type of innovation that firms adopt and how this subsequently affects customer satisfaction outcomes (Yikilmaz et al., 2025; Van Huy et al., 2024). This research gap limits theoretical and practical understanding of how social influence mechanisms may be utilised strategically by small-scale hospitality firms in Ghana to adopt appropriate innovation that enhances improvements in customer satisfaction (Baba et al., 2025). Therefore, a clearer understanding of how subjective norms shape innovation-sourcing decisions and how these decisions translate into customer satisfaction outcomes is necessary to advance both hospitality innovation theory and SME management practice.

1.3. Aim of the Study

This study therefore seeks to achieve the following objectives:

• To examine the effect of subjective norm perceptions about innovation on the satisfaction of small-scale hospitality firm guests.
• To examine the effect of subjective norms perceptions about innovation on innovation source among small-scale hospitality firms.
• To examine the effect of innovation source on customer satisfaction among small-scale hospitality firms.
• To examine the mediating role of innovation source on the relationship between subjective norm perceptions about innovation and customer satisfaction.

1.4. Novelty of Study

This study advances the Theory of Planned Behaviour (TPB) and the hospitality innovation literature in three distinct ways.

This study first reconceptualizes the subjective norms as the strategic resource influencing organisational innovation behaviour in small hospitality firms. The study further applies subjective norms to the managerial decision-making in small hospitality firms, extending the Theory of Planned Behaviour (TPB) to examine the relationship between subjective norms and organisational innovation sources.

Second, the study introduces innovation source as a mediating variable that links subjective norms and customer satisfaction. This is because previous studies have generally considered the adoption of innovation as a unique construct without considering the innovation source and its implications on service outcomes (Nieves & Diaz-Meneses, 2018; Hameed et al., 2021). In this way, the integration of innovation source into the TPB provides a more nuanced understanding of how socially driven managerial perceptions of customers can lead to the implementation of specific innovation strategies that impact customer satisfaction among small hospitality firms within the context of an emerging economy like Ghana.

Through these contributions, the study moves beyond contextual validation of existing relationships and offers a theoretically grounded explanation of how social influence mechanisms shape innovation-sourcing decisions and customer satisfaction outcomes in hospitality SMEs in Ghana.

2. Literature Review

2.1. Theoretical Framework

The Theory of Planned Behaviour forms the theoretical lense for this research. The theory, proposed by Icek Ajzen (Ajzen, 1991), has established itself as a widely recognised model for the explanation of the mechanisms of intentional human action in organisational and consumer contexts and particularly the adoption of innovative solutions in a hospitality context. The TPB argues that behaviour is mainly influenced by behavioural intentions defined as being determined by three main components, namely attitudes towards the behaviour, subjective norms and perceived behavioural control (Ashaduzzaman et al., 2022).

Attitude refers to an individual’s positive or negative evaluation of performing a behaviour, shaped by beliefs about its outcomes (Djafarova & Foots, 2022). Subjective norms represent the perceived social pressure from important others, such as family, peers, or supervisors regarding whether one should or should not engage in the behaviour (Q. C. Wang et al., 2023).

Perceived behavioural control reflects a person’s belief in their ability to perform the behaviour, considering available resources, skills, and external constraints (Alhamad & Donyai, 2021; Fauzi et al., 2024).

In this study, the subjective norm component of TPB serves as the central theoretical foundation, guiding the explanation of how social expectations shape managerial decisions about innovation adoption within small-scale hospitality firms. This is because innovation choices can be socially visible, and they may be subject to the influence of the manager’s social network, the hospitality and tourism industry associations or customer expectations. Social influence is becoming more important in managers’ choices for technological and service innovations, especially in small businesses that are often part of close social and professional networks, as recent studies on the hospitality and tourism sector indicate (McLeod et al., 2024; Van Huy et al., 2024). Hence, the subjective norm construct can be regarded as a suitable construct that explains why hospitality and tourism managers follow the industry, their peers, and their customers when sourcing innovations.

Although the Theory of Planned Behaviour identifies attitudes, subjective norms and perceived behavioural control as joint predictors of behavioural intention, this study takes a theoretically focused expansion of TPB isolating the subjective norm component. This is because this study seeks to enhance the comprehension of socially embedded innovation behaviour in small-scale hospitality settings, where peer visibility and industry legitimacy exert significant influence. Earlier studies utilising the Theory of Planned Behaviour (TPB) in the hospitality sector predominantly focused on forecasting individual behavioural intentions, including technology adoption, environmental conduct, and consumer decision-making (Ajzen, 2020; Yikilmaz et al., 2025). However, limited studies exist on how subjective norms affect innovation decisions at the organisational level and the strategic sourcing of innovation in service companies.

This study isolates the subjective norm dimension of TPB, thereby expanding the application of TPB from individual behavioural intentions to organisational innovation behaviour. In this manner, the study reconceptualizes subjective norms not merely as predictors of behavioural intention but also as social influence mechanisms that shape managerial strategic decisions concerning innovation sourcing. This theoretically focused approach is particularly suitable in small-scale hospitality settings where decision-making is deeply integrated within social and professional networks, and where companies frequently evaluate their innovation practices against those of competitors and peer organisations.

Beyond this theoretical extension, the study also integrates TPB with insights from the innovation sourcing and open-innovation literature. By emphasising the role of socially endorsed behaviours, the study draws on subjective norms to explain how external approval influences not only the intention to innovate but also the selection of credible innovation sources that ultimately contribute to enhanced customer satisfaction.

The subjective component of TPB is therefore particularly relevant for this study because perceptions of subjective norms about innovation can directly affect customer satisfaction since managers and owners of small-scale hospitality firms could be motivated by social pressures to adopt innovation or feel pressured to engage in behaviours that accommodate stakeholder expectations and enhance customer satisfaction. Furthermore, subjective norms not only affect customer satisfaction but dictate the source of innovation based on the expectations of peers, networks or competitors as to whether the firm ought to pursue an internally developed or externally created innovation. The source of innovation, in turn, has a direct effect on customer satisfaction since the relevance, congruency and utility of the innovation adopted directly facilitates service quality. The source of innovation mediates the relationship between subjective norms and customer satisfaction by translating the effect of social pressure into a hard outcome in the form of a decision about the type of innovation to adopt, which in turn determines the quality of service the customer experiences.

In addition to the Theory of Planned Behaviour, institutional theory is also applied in this study to reinforce the explanation of socially embedded innovation decisions (Meyer et al., 2017; Krell et al., 2016). According to institutional theory, the actions of firms are influenced by coercive, normative and mimetic pressures in their external environments (Dua, 2022; Kauppi, 2022; Zhao et al., 2017). In small-scale hospitality contexts, managers work within heavily intertwined industry networks where mimicking accepted behaviour serves to increase legitimacy and lower uncertainty. Such normative expectations from professionalistic associations, peer firms or suppliers and customer communities may consequently influence not only the intention to innovate but also the strategic choice of innovation sources. The study elucidates how this social pressure becomes institutionalised in sourcing decisions, which cumulatively impact service quality and customer satisfaction outcomes, through the lens of institutional theory.

This study makes a theoretical contribution by reorienting the role of subjective norms away from being simply another antecedent of behavioural intention (Ajzen, 1991) to becoming a strategic factor influencing innovation-sourcing decisions in organisational contexts. Building on the Theory of Planned Behaviour, this study transcends micro-level intention models by integrating the institutional theory, thereby explaining how normative, mimetic and industry pressures shape firm-level strategic choices. This research shows that subjective norms, rather than being considered only as drivers of behavioural intention, are socially embedded pressures impacting not just whether innovation is adopted but also how and where it is sourced. This perspective reframes TPB by situating it in an institutional and innovation management development view, associating mechanisms of social conformity to service performance outcomes in small-scale ventures.

2.2. Conceptual Review

2.2.1. Subjective Norms

The subjective norm perception of innovation adoption refers to the perception an individual has about whether significant others (such as peers, customers, regulators, and networks of practice) approve or expect them to introduce an innovation (Sullivan et al., 2022). These perceptions are relevant social motivators and help form the intention and subsequent behaviour of managers and owners of a business within the organisational context by communicating what is seen to be legitimate or desirable behaviour in given environments (Shou et al., 2023). In the context of small-scale hospitality firms in Ghana, subjective norm perceptions are contextualised as a high level of sensitivity to the established practices operating within the industry and the consequential influence and pressure imposed upon the businesses by significant norm-giving stakeholders and reference groups (Awusiedu et al., 2024). For hospitality enterprises, therefore, adoption of innovation is not the result of rational assessment of their experiences, or the availability of resources, but a result of normative pressures to conform, achieve competitive parity or meet the heightened expectations of customers (Quaye, 2024).

2.2.2. Innovation Source

Innovation source refers to the specific origins from which an organisation obtains new ideas, technologies, or practices to improve efficiency, competitiveness, and customer experience (Sonmez Cakir et al., 2024). According to Hervas-Oliver et al. (2021), internal innovation sources emerge from within the firm, driven by employee creativity, operational problem-solving, experiential learning, and incremental process improvements that align closely with the organisation’s unique culture and customer needs. External innovation sources originate outside the firm and include inputs from customers, suppliers, competitors, consultants, franchisors, tourism associations, and digital technology providers (Pessot et al., 2025; Audretsch & Belitski, 2023; Amato et al., 2022). For small-scale SMEs in Ghana’s hospitality industry, innovation sources are particularly important because these firms often face financial and technical constraints (Alhassan et al., 2025). As a result, internal sources help leverage staff expertise and local knowledge, while external sources provide accessible, cost-effective solutions such as digital booking tools, service innovations, or training programmes (Adu-Yeboah et al., 2022).

2.2.3. Customer Satisfaction

Customer satisfaction is defined as a consumer’s overall evaluation of the extent to which products or services meet or exceed expectations (Singh et al., 2023; Agag et al., 2024). In the hospitality industry, customer satisfaction is largely determined by service quality and the degree of opportunity offered for personalising the service to the guests (Paulose & Shakeel, 2022). High degrees of satisfaction on the part of customers lead to a high degree of customer loyalty, which enhances positive word-of-mouth effects and customer re-patronage of hospitality services (Kachwala, 2023; Al-Shidhani & Tumati, 2021). For small-scale hospitality firms in Ghana, customer satisfaction is determined by various dimensions of service quality such as responsiveness, empathy, tangibility, assurance and reliability (Adzinyo et al., 2024; Boateng et al., 2021; Abekah-Nkrumah et al., 2021).

Although there have been previous studies that have investigated subjective norms (Singh et al., 2023), innovation source (Shou et al., 2023) and customer satisfaction (Agag et al., 2024; Sonmez Cakir et al., 2024) in various other sectors, few have explored their ramifications within resource-constrained and socially embedded small-scale hospitality environments of emerging economies like Ghana, leading to a research gap. In this context, our study argues that managerial decisions are highly relational and publicly observable, which accentuates the performance implications of socially motivated innovation behaviour. In this sense, by empirically testing both direct and mediated pathways between subjective norms, innovation source and customer satisfaction, this study’s novelty extends beyond a mere contextual replication to providing process-based insight regarding how social influence manifests in operational service effectiveness.

2.3. Hypotheses Development

2.3.1. Subjective Norm Perceptions About Innovation Adoption and Customer Satisfaction

Empirical studies have indicated that subjective norm perceptions about innovation adoption have significant positive implications on customer satisfaction (J. J. Kim & Han, 2022). Subjective norms are a major motivator for organisations to utilise new technologies and innovative procedures which facilitate stakeholder acceptance and customer satisfaction (Irimia-Diéguez et al., 2023). In a study conducted by Du et al. (2025), it was found that subjective norms enhance innovative adoption of smart hotel technologies and result in a better guest experience, which is a clear precursor for higher customer satisfaction. Sun et al. (2020) attests that, when organisational managers perceive an optimum time for innovative operational activity because of strong peer or societal subjective norm pressures, the adoption of innovative practices such as mobile payment systems and self-service technology, which are directly impactful on service ratings, enhances customer satisfaction. A study conducted by Lahap et al. (2024) also indicates that subjective norms are positively and significantly associated with customer purchase intention and satisfaction among hospitality firms. Elgarhy and Abou-Shouk (2024) assert that subjective norms impact both innovation adoption and quality of customer experience, which translates into customer satisfaction. Based on studies conducted by Du et al. (2025), Elgarhy and Abou-Shouk (2024), Irimia-Diéguez et al. (2023), J. J. Kim and Han (2022), Lahap et al. (2024) and Sun et al. (2020), the study hypothesises the following:

H1. 

There is a significant positive relationship between subject norm perceptions about innovation adoption and customer satisfaction.

While Hypothesis 1 posits a positive association between subjective norms and customer satisfaction, it is theoretically feasible that normative pressures also result in unwanted negative effects. Particularly, when selection for the adoption of innovation is largely determined by peer group, industry or societal pressure, rather than having a clear strategic rationale, implementation may be tokenistic, precipitated and/or out of synchrony with operational capacity. In these contexts, poor integration, employee resistance or technological mismatch could undermine the quality of service and ultimately degrade instead of improve customer satisfaction.

2.3.2. Subjective Norm Perceptions About Innovation Adoption and Innovation Source

Fu et al. (2020) found that subjective and social information strongly influence how decision-makers of firms evaluate and select both internal and external innovation sources in their innovation adoption decisions. Mustofa et al. (2025) extended technology acceptance models by showing that perceived social pressures affect whether firms choose high-quality external innovation sources or develop internal innovation sources when making decision on innovation adoption. In a study conducted by Baba et al. (2025), the results showed that subjective norms have a strong positive effect on innovation source strategies and encourage the adoption of solutions that have been validated by the sector’s reference groups and the business community. Studies by Amato et al. (2022) and Chou et al. (2025), grounded in social exchange and open-innovation theories, reveal that stronger subjective norms related to external partnerships and industry best practices increase a firm’s tendency to source innovations from outside rather than rely solely on internal solutions. This finding highlights the influential role of peer and stakeholder approval in shaping organisational innovation-sourcing decisions (Chou et al., 2025). Based on studies conducted by Mustofa et al. (2025), Baba et al. (2025), Chou et al. (2025), Amato et al. (2022) and Fu et al. (2020), the study hypothesises the following:

H2. 

There is a significant positive relationship between subjective norm perceptions about innovation and innovation source.

2.3.3. Innovation Source and Customer Satisfaction

A study conducted by Khassawneh et al. (2024) provides strong support that eco-innovation sources, typically available externally, can greatly increase customer satisfaction when integrated with service quality improvements in hospitality firms. Aljawarneh et al. (2025) indicate that both product/service and process innovations, regardless of whether developed internally or externally, exhibit statistically significant positive relationships with hotel guest satisfaction. Amoako et al. (2023) found that online innovation, such as the application of digital platforms and service automation, which frequently employs strategic external technological alliances, enhances customer experience, which directly improves customer satisfaction and repurchase intention in the Ghanaian hotel industry. Further, Lee et al. (2021) emphasised that open innovation, employed through collaboration with external partners as well as customers, provides hotels with a better understanding of guest needs and also with the means to present increasing personal value, thereby leading to greater customer satisfaction and competitive differentiation. Based on studies conducted by Aljawarneh et al. (2025), Khassawneh et al. (2024), Amoako et al. (2023) and Lee et al. (2021), the study hypothesises the following:

H3. 

There is a significant positive relationship between innovation source and customer satisfaction.

2.3.4. Mediating Role of Innovation Source

Research shows that innovations obtained from trusted social contacts, peer networks, and collaborative partners enhance organisational confidence in adopting new practices (Wan et al., 2022). Such innovation sources also strengthen positive social norm conditions when managers assess customer-related issues (Quratulain et al., 2021). The credibility and legitimacy of internal and external innovation sources enhance customer trust and satisfaction and increase the likelihood of adoption of these innovations recommended by their social and or professional networks (Truong et al., 2020). In the hospitality industry, innovations promoted by reputable industry sources, stakeholders, or networking or industry groups tend to translate social norm expectations into actual improvements in customer service quality and customer satisfaction (Karim et al., 2024). Latip and Sharkawi (2021) highlight that the adoption of novel and commercially viable innovations derived from socially validated sources enhances their relevance, strengthens customer confidence, and increases the perceived service value. Their findings indicate that innovation source functions as a key mediating mechanism through which social reference effects translate into improved customer satisfaction outcomes (Latip & Sharkawi, 2021). Based on studies conducted by Karim et al. (2024), Wan et al. (2022), Quratulain et al. (2021), Latip and Sharkawi (2021) and Truong et al. (2020), the study hypothesises the following:

H4. 

Innovation source mediates the relationship between subjective norm perceptions about innovation and customer satisfaction.

2.4. Conceptual Framework

The aforementioned reviews, objectives and hypotheses lead to the research model illustrated in Figure 1.

3. Methodology

This research adopted the positivist research paradigm, which rests on a foundation of objectivity, empirical measurement, and the application of scientific methods in the assessment of social phenomena (Abdala & Elnadeef, 2025). The positivist research paradigm was most appropriate because the study sought to establish measurable and verifiable relationships between subjective norms about innovation adoption, innovation source and customer satisfaction among small-scale hospitality firms in Ghana. A quantitative research approach was also adopted to facilitate the empirical collection of numerical data, statistical analysis and interpretation (Jamieson et al., 2023), to examine the direct and mediating relationships between subjective norms about innovation adoption, innovation source and customer satisfaction among small-scale hospitality firms in Ghana. The study also adopted a cross-sectional survey research design (Chen et al., 2021), which ensured that responses from respondents were obtained at one point in time, to facilitate the exploration of the relationships existing between subjective norms about innovation adoption, innovation source, and customer satisfaction in the specific context of Ghanaian small-scale hospitality firms. The adoption of the cross-sectional survey design also enabled the researchers to obtain a realistic and relatively large representation of small-scale hospitality firms in Ghana. The population of the study comprised all small-scale hospitality firms operating in the Ashanti Region of Ghana. According to the Ghana Tourism Authority (2022), approximately 1100 licenced hospitality firms exist in the region, out of which approximately 750 are deemed as small-scale firms, comprising lodges, guest houses and restaurants. From this population, a sample size of 465 participants was determined using Slovin’s formula (Slovin, 1960) to ensure statistical reliability at a 97% confidence level with a margin of error of 0.03.

$$n={\displaystyle \frac{N}{{1+Ne}^2}}$$

where

• n = required sample size;
• N = population size (750);
• e = margin of error (0.03).

Substituting in the values yields the following:

$$n={\displaystyle \frac{750}{{1+\left(750\right)\left(0.03\right)}^2}}=465$$

From a population of 750, a sample of 465 respondents was selected to participate in the study to ensure a high statistical level of reliability. A stratified random sampling technique was adopted to ensure proportional representation, and at the same time limit selection bias (Nguyen et al., 2021). The population was initially stratified based on the districts that constitute the entire region, after which random selection was conducted within each stratum using the official register of hospitality enterprises provided by the Ghana Tourism Authority. This sampling design enhances both the representativeness and the generalisability of the findings to the regional context (Zaman & Bulut, 2023). Furthermore, by ensuring that the diverse characteristics of hospitality businesses were proportionately captured, the approach strengthens the validity and reliability of the results obtained. Data were primarily collected through a structured questionnaire developed by the researchers based on the existing literature on the variables subjective norm perceptions about innovation adoption (Sullivan et al., 2022; Shou et al., 2023), innovation source (Pessot et al., 2025; Sonmez Cakir et al., 2024; Hervas-Oliver et al., 2021) and customer satisfaction (Singh et al., 2023; Agag et al., 2024). The questionnaire was structured into sections, comprising demographic information such as gender, age, race, education, and business characteristics, followed by items measuring the main variables, namely, subjective norms toward adoption of innovation, innovation source and customer satisfaction (see Appendix A). Subjective norms comprised 7 items adapted from Rachmawati et al. (2020). Innovation sources comprised 8 items adapted from De Jong and Den Hartog (2010). Customer satisfaction comprised 4 items adapted from the American Customer Satisfaction Index (ACSI) (Vinyard, 2015). The questionnaire items were recorded on a six-point Likert scale where 1 = Strongly Disagree, 2 = Disagree, 3 = Slightly Disagree, 4 = Slightly Agree, 5 = Agree and 6 = Strongly Agree. The questionnaire was piloted with 40 respondents to enable the researchers to effectively deal with all issues pertaining to ambiguity of questionnaire items and thereby enhance content validity (Khanal & Chhetri, 2024). Data collection was conducted primarily through visits to participating small-scale hospitality firms in the Ashanti Region of Ghana. Researchers self-administered the questionnaires to respondents, explained the purpose of the study, and clarified items when needed. The study was conducted within a 6-month period. Thus, the study period spanned from 20 March 2025 to 20 September 2025.

To ensure that ethical principles were followed during data collection, participants were informed that participation was voluntary and that their responses would remain strictly confidential (Akhurst & Leach, 2023). Data analysis was undertaken by using Statistical Package for the Social Sciences (SPSS) version 28 from IBM Armonk, New York, USA and Smart PLS version 4 (Ringle et al., 2024).Descriptive statistics were conducted using SPSS software, version 28 for the demographic variables of the study using frequencies and percentages. Smart PLS 4 was used to conduct Partial Least Squares (PLS) Structural Equation Modelling (SEM) for the direct and mediating relationships between subjective norms about innovation, innovation source and customer satisfaction among small-scale hospitality firms. This analytical approach was deemed highly appropriate because of its strong capacity to handle complex models and estimate multiple relationships simultaneously, thereby offering a more comprehensive understanding of the phenomenon under investigation (Sarstedt et al., 2021)

4. Results

The results of the study are presented using both descriptive statistics (frequencies and percentages) and PLS-SEM. Out of a total of 465 questionnaires administered, a total of 450 were retrieved, indicating 98.7%.

4.1. Demographics

The demographic and business characteristics described in

Table 1. Owner/manager and business characteristics.

Variable	Category	Frequency (n)	Percentage (%)
Gender	Male	199	44.2
	Female	251	55.8
Age	25–35 years	24	5.4
	35–45 years	335	74.6
	45–55 years	91	20.0
Highest Educational Level	Master’s degree	35	7.8
	Bachelor’s degree	161	35.8
	Diploma	111	24.7
	SSCE/WASSCE/technical/vocational	112	24.9
	Junior high school	22	4.9
	No formal education	9	2.0
Type of Hospitality Business	Food and beverage	147	32.7
	Lodging	120	26.6
	Recreation	51	11.3
	Travel and tourism	82	18.2
	Meetings and events	50	11.2
Star Rating of Business	No star rating	257	57.1
	One star	85	18.9
	Two star	108	24.0
Years of Operation	2–5 years	125	27.8
	5–10 years	139	30.9
	More than 10 years	186	41.3
Role in the Business	Owner/founder	184	40.9
	Manager	266	59.1
Ownership Structure	Sole proprietorship	377	83.8
	Partnership	73	16.2

provide a profile of the respondents and businesses owned and managed by them. The sample included a fair and dependable profile of small-sized operators in the hospitality industry in the Ashanti Region of Ghana. There were slightly more female (55.8%) than male respondents (44.2%), suggesting a trend towards more female entrepreneurs and managerial participation in the industry. The age distribution was heavily tipped to the economically active 35–45 years of age category (74.6%), indicating that the industry was managed mostly by people in or around the mid-career age bracket. Educational levels of the respondents varied widely but the fact that most had at least a diploma or degree emphasises that there is an improving skills base in the small-scale hospitality ecosystem. Demographic results further indicated that most respondents conducted business in the food and beverage (32.7%) and lodging (26.6%) spaces, which are mainstays of Ghana’s hospitality business. More than half (57.1%) did not have a formal star rating attached to their business, indicative of the largely informal/semi-formal nature of many SMEs in Ghana. Business longevity was strong, as more than 72% have operating for over five years, indicating resilience and stable presence. Managers dominated the response rate (59.1%) compared owners (40.9%). The dominance of sole proprietorships (83.8%) suggests that the operating dynamics were founded on a personalised decision-making structure as expected of most SMEs in Ghana.

4.2. Construct Validity

Construct validity concerns how well a scale represents the construct or theoretical concept that it is supposed to measure (Hair et al., 2019). Construct validity appears to exist when items strongly load on their respective constructs and show little association with unrelated constructs (Hair et al., 2019). Based on factor loadings from

Table 2. Construct validity statistics.

Construct	Customer Satisfaction	Innovation Source	Subjective Norms
CS1	0.770
CS2	0.917
CS4	0.844
SBN1			0.890
SBN2			0.909
SBN3			0.942
SBN4			0.944
SBN5			0.950
SBN6			0.937
SBN7			0.934
SOI10		0.916
SOI11		0.929
SOI7		0.723
SOI8		0.779
SOI9		0.922

, the three constructs show evidence of construct validity. Customer satisfaction items load well (between 0.770 and 0.917), indicating that the items assess reasonably well. Subjective norms items load substantially (0.890–0.950). Innovation source items load between 0.723 and 0.929. Importantly, all factor loadings are greater than the acceptable minimum of 0.70, indicating the constructs are being measured reliably with no threats of cross-loading. CS3 had a factor loading less than 0.7, which warranted its omission, since it did not meet the acceptable threshold of 0.7.

4.3. R² Statistics

R² in PLS-SEM signifies the proportion of variance explained in an endogenous construct by its predictor variables (Ardi, 2020). It reflects the explanatory power of the model, with larger values signifying higher predictive accuracy (Ardi, 2020). From the results in

Table 3. R² statistics and model fit.

Construct	R2	R2 Adjusted	SRMR
Customer satisfaction	0.163	0.159
Innovation source	0.125	0.123
			0.079

, subjective norm perceptions explained 16.3% variance in customer satisfaction and 12.5% in innovation source. The Standardised Root Mean Square Residual (SRMR = 0.079) is below the recommended threshold of 0.08, indicating an acceptable model fit. This suggests that the discrepancy between the observed correlations and the model-implied correlations is relatively small.

4.4. F² (Effect Size)

F² in PLS-SEM measures the impact that an exogenous variable has on endogenous variables (Hossan et al., 2020). It is the degree to which R² for the endogenous construct changes as each specific predictor is either included or part of the model (Hossan et al., 2020). Following Cohen’s guidelines, 0.02 is a small effect, 0.15 indicates medium effect values and larger effect sizes are interpreted as 0.35 (Gülkesen et al., 2022). As shown in

Table 4. F² (effect size).

Path	F2
Innovation source -> Customer satisfaction	0.195
Subjective norms -> Customer satisfaction	0.028
Subjective norms -> Innovation source	0.143

, innovation source has a medium size effect on customer satisfaction (F² = 0.195). Subjective norms have a small effect size on customer satisfaction (F² = 0.028). However, subjective norms have a medium effect size on innovation source (F² = 0.143).

4.5. Reliability and Convergent Validity

Reliability refers to the degree of internal consistency of items to measure a unitary construct (Aburumman et al., 2022). Common measures of reliability are Cronbach’s alpha and composite reliability (Aburumman et al., 2022). Convergent validity is the degree to which a construct correlates well with similar constructs, often measured using Average Variance Extracted (AVE) with values above 0.50 indicating good convergence (Cheah et al., 2018). The results attained (see

Table 5. Reliability and convergent validity statistics.

Construct	Cronbach’s Alpha	Composite Reliability (rho_a)	Composite Reliability (rho_c)	Average Variance Extracted (AVE)
Customer satisfaction	0.800	0.830	0.882	0.715
Innovation source	0.908	0.917	0.933	0.736
Subjective norms	0.974	0.976	0.978	0.864

) indicate strong reliability and convergent validity in all constructs. Customer satisfaction has acceptable reliability with Cronbach’s alpha of 0.800 and strong composite reliability (0.882). Innovation source and subjective norms have excellent reliability with Cronbach’s alpha for innovation source at 0.908 and subjective norm at 0.974. All composite reliability values exceed 0.70. Convergent validity was also achieved with AVE of customer satisfaction at 0.715, innovation source at 0.736, and subjective norm at 0.864; all values are above 0.50, signifying that the constructs explain a good portion of variance in their items.

4.6. Discriminant Validity: Heterotrait–Monotrait Ratio (HTMT)

Discriminant validity using Heterotrait–Monotrait Ratio (HTMT) compares the average correlations between items of different constructs to the correlations of items within the same construct (Roemer et al., 2021). Discriminant validity is achieved when HTMT is below 0.85 (strict criterion) or 0.90 (lenient criterion) (Roemer et al., 2021). From the results, it can be deduced that discriminant validity was achieved among all constructs. The Heterotrait–Monotrait values (see

Table 6. Discriminant validity using HTMT.

	Heterotrait–Monotrait Ratio (HTMT)
Innovation source <-> Customer satisfaction	0.431
Subjective norms <-> Customer satisfaction	0.050
Subjective norms <-> Innovation source	0.366

) between innovation source and customer satisfaction (0.431), subjective norms and customer satisfaction (0.050), and subjective norms and innovation source (0.366) are all well below the 0.85 benchmark.

4.7. Collinearity Statistics

The VIF values indicate no multicollinearity concerns in the structural model. All values are well below the conservative threshold of 3.3 and the maximum threshold of 5. Specifically, the VIF of 1.143 for innovation source and subjective norms predicting customer satisfaction suggests very low correlation between predictors (see

Table 7. Multicollinearity statistics.

	VIF
Innovation source -> Customer satisfaction	1.143
Subjective norms -> Customer satisfaction	1.143
Subjective norms -> Innovation source	1.000

).The VIF of 1.000 for subjective norms predicting innovation source indicates no collinearity issue at all. Thus, structural path estimates can be interpreted reliably without bias from multicollinearity.

4.8. Direct and Mediating Effects

As can be seen from

Table 8. Direct and mediating effects among variables.

Hypotheses	Β-Value	T-Statistics	p-Values	Decision
H1: Subjective norms -> Customer satisfaction	−0.164	3.651	0.000	Not supported
H2: Subjective norms -> Innovation source	0.354	8.343	0.000	Supported
H3: Innovation source -> Customer satisfaction	0.431	8.877	0.000	Supported
H4: Subjective norms -> Innovation source -> Customer satisfaction	0.153	6.602	0.000	Supported

and Figure 2, the study found that a significant negative relationship exists between subjective norms about innovation adoption and customer satisfaction (β = −0.164, p < 0.05). Hypothesis 1 was therefore not supported. However, a significant positive relationship was found to exist between subjective norm perceptions about innovation adoption and innovation source among small-scale hospitality firms in Ghana (β = 0.354, p < 0.05). A significant positive relationship was also found to exist between innovation source and customer satisfaction among small-scale hospitality firms in Ghana (β = 0.431, p < 0.05). Hypothesis 3 was therefore supported. Finally, innovation source positively mediated the relationship between subjective norm perceptions about innovation adoption and customer satisfaction (β = 0.153, p < 0.05). Hypothesis 4 was therefore supported.

5. Discussion

The finding that a significant negative relationship exists between subjective norms and customer satisfaction within small-scale hospitality firms in Ghana contradicts expectations of the Theory of Planned Behaviour (TPB) (Ajzen, 1991), which stipulates that perceived social pressures positively affect behavioural outcomes. This suggests that the impact of subjective norms on organisational outcomes may depend on how firms interpret and operationalise these social pressures (Ajzen, 2020). This negative relationship can be explained by the resource-constrained and socially sensitive nature of small-scale hospitality firms in Ghana. In such environments, managers may adopt innovations primarily to comply with peer expectations, competitive pressure, or industry norms rather than in response to genuine customer needs. This behaviour aligns with what innovation research refer to as “passive” innovation adoption, where companies adopt new ideas mainly to look good or show that they are following industry standards, not to improve their operations (Krell et al., 2016; Tsinopoulos et al., 2018). In these cases, adopting new ideas becomes more of a symbolic response to social pressure than a strategic move to improve service quality. This compliance-driven behaviour may result in hurried or poorly aligned implementation of technologies such as digital booking systems or mobile payments without adequate staff training or operational readiness. Studies in hospitality SMEs suggest that when innovations are adopted without sufficient organisational readiness or managerial capability, they may disrupt service processes and negatively affect customer experiences (Nieves & Diaz-Meneses, 2018; Hameed et al., 2021). While previous studies have found that subjective norms positively affect both innovation and customer satisfaction in the hospitality context (J. J. Kim & Han, 2022; Du et al., 2025; Sun et al., 2020), the negative relationship ascertained by this study indicates that external social pressure to adopt innovations may not prove beneficial within Ghanaian small-scale hospitality firms. Rather, small firms in Ghana may adopt innovations such as digital booking systems or mobile payment systems for compliance purposes, to conform to peer pressure, customer demand or industry norms, in the absence of both the availability of funds and strategies to use the innovations most effectively. Such externally driven adoption may lead to what some scholars term misaligned innovation, wherein the adopted innovation inadequately aligns with the firm’s operational capacity or service design (Bunduchi et al., 2015). As a result, innovations that are expected to improve service quality may instead introduce operational inefficiencies, service delays or staff adaptation challenges (Chatterjee et al., 2021). Within the framework of the TPB, this sound more like normative compliance rather than internalised motivation, which results in operational inefficiencies and lower customer satisfaction. In Ghana’s hospitality environment, where small-scale hospitality firms are technically and financially constrained, it may be the case that normative pressures inhibit rather than enhance customer value creation and customer satisfaction.

The finding that a significant positive relationship exists between subjective norm perceptions about innovation adoption and innovation within small-scale hospitality firms in Ghana confirms previous empirical findings as well as the subjective norm component of the Theory of Planned Behaviour (TPB) (Ajzen, 1991). The TPB argues that subjective norms that constitute the perceived social expectations of others of influence are determinants of behavioural intentions and strategic thinking in terms of decisions, such as innovations sources (Chou et al., 2025). Empirical studies suggest that managers’ choices of innovation are conditioned by social networks and collective attitudes both within and outside the firms (Fu et al., 2020). Mustofa et al. (2025) has found that strong normative pressures towards modernisation result in firms’ preference towards external sources of innovation, particularly in instances where such sources have been endorsed by peers or industry-standard sources. Baba et al. (2025) and Amato et al. (2022) indicate that it is the recognition by peer organisations and the validation by stakeholders that push managers towards adopting externally approved innovations that enhance legitimatisation and competitiveness. Thus, this positive relationship indicates that subjective norms act as social facilitators or catalysts that inform the choices made by small-scale hospitality managers in Ghana when seeking credible innovations from externally determined sources that are affirmatory of existing norms prevailing and stakeholder expectation or pressure.

The result, which indicated a significant and positive relationship between innovation source and customer satisfaction in small-scale hospitality firms in Ghana, is consistent with the empirical literature and the subjective norm component of the Theory of Planned Behaviour (TPB) (Ajzen, 1991). Within the framework of the TPB, subjective norms that constitute perceived social expectations from key stakeholders define the managerial intentions and guide how firms choose and make use of sources of innovation in the meeting of market and consumer expectations. Empirical studies suggest that innovations derived from externally sourced validations enhance service quality and satisfaction, in that the innovations are indicative of the general usage of industry-leading ideas used to enhance the quality of customer satisfaction (Lee et al., 2021). Khassawneh et al. (2024) has shown that eco-innovations stemming from external pressure enhance customer satisfaction. Studies from Aljawarneh et al. (2025) and Amoako et al. (2023) show that external digital innovative sources affect innovative operational processes, arising from technological schedules, which positively predict a high level of customer satisfaction and customer loyalties. Lee et al. (2021) elaborate on this, showing that it is the open-innovation partnerships that allow hotels’ innovative sources to gain a better understanding of the needs of customers and a personalisation of product offerings, leading to enhanced customer satisfaction. The results indicate that credible external sources of innovation correspond most appropriately with the requirements of small-scale hospitality firms attempting to react to social pressure and dynamic market trends for modernisation. From a TPB perspective, subjective norms guide the indirect firm selection of where to source for innovation and induce a preference for socially acceptable, and externally endorsed, courses of action. This externalised innovation makes service offerings more relevant and improves service quality, thereby enhancing customer satisfaction.

The finding that innovative source positively mediates the relationship between subjective norm perceptions about innovation adoption and customer satisfaction among small-scale hospitality firms in Ghana aligns with the empirical literature and the Theory of Planned Behaviour. Relating the result with the empirical literature, it could be deduced that the finding affirms the impact of socially derived innovation sources on service outcomes, such that innovations from valued external contacts improve managerial comfort and effective implementation, leading to customer satisfaction (Wan et al., 2022). The finding also supports that socially approved innovation sources increase the positive conditions of norms and factor into decision-making for customer-related issues, enhancing customer satisfaction (Quratulain et al., 2021). In hospitality contexts, innovations from “endorsed” sources (i.e., suppliers, industry players, and stakeholder networks) facilitate service upgrades and improve customer satisfaction (Karim et al., 2024). Studies by Latip and Sharkawi (2021) indicate that innovations perceived as emanating from socially valued ties increase perceived service value such that these shared values between norms and customers yield customer satisfaction. The results also confirm the subjective norm dimension of the Theory of Planned Behaviour (TPB), which suggests that individuals and organisations make considerations in response to social pressures in forming behavioural intention. The mediation effect demonstrates that it is the subjective norms that influence what sources of innovation the firms draw from and how that impacts customer satisfaction, thus converting social expectations into service-enhancing innovations in Ghana’s small-scale hospitality context. Drawing on an institutional theory perspective, subjective norms may also manifest through normative and mimetic tensions within the industry environment that compel small-scale hospitality firms to adopt what is perceived as socially legitimate behaviours. In resource-constrained contexts like Ghana, institutional theory suggests that in sourcing innovations, firms are likely to build on the basis of reputable suppliers, established networks, and observed practices by other legitimate industry actors to acquire socially acceptable solutions and alleviate perceived uncertainty. Transforming social expectations of conformity in the sourcing behaviour results in structured organisational action that emerges from them, yielding better implementation quality and service performance when matched with credible innovation partners. Institutional theory therefore strengthens the mediating explanation by clarifying how socially embedded pressures become operationalized into innovation decisions that ultimately shape customer satisfaction outcomes.

6. Conclusions

This research examined the relationships between subjective norm perceptions about innovation adoption, innovation source and customer satisfaction among small-scale hospitality firms in Ghana and deduced the following conclusions. Firstly, the efficient use of innovation sources offers an avenue for enhancing the service quality and competitiveness of small-scale hospitality firms in Ghana. This is because, by drawing on credible internal and external sources, small-scale hospitality firms are better able to pursue innovations that respond to the changing needs of customers and the market in general. Secondly, the service context shapes managerial choices over how innovations are sourced and adopted. The social expectations, norms and opinions of peers, customers, suppliers and professional associations in the hospitality industry support a climate of expectations that encourages firms to seek the most reliable and validated sources of innovation. Thirdly, hospitality firms benefit more from engaging directly and consciously with professional networks, stakeholder and interest groups, and platforms that expose them to best practices and market-driven innovations. These engagements empower small-scale hospitality firms to improve their service offerings and keep abreast with competition despite their resource constraints. Finally, the extent to which firms can translate social pressures into conscious choice over how they source innovations hints to a core mechanism for ameliorating customer experiences and satisfaction. Thus, by commensurately satisfying normative expectations with their sourcing practice, small-scale hospitality firms can improve the value of their service offerings, cultivate customer loyalty and sustain performance in the long run.

The result of this study has important theoretical implications for the Theory of Planned Behaviour (TPB) (Ajzen, 1991) and the institutional theory. First, the significant negative association between subjective norms and customer satisfaction refutes the assumption of the TPB that social pressures will characteristically lead to effective behavioural outcomes. In the small-scale hospitality sector of Ghana, it is frequently the case that firms adopt innovations largely to conform to peer pressure, customer trends or industry expectations rather than through the agency of conviction or strategic intent. Such compliance behaviour frequently results in the adoption of innovative technologies or practices which are ill-suited to the operational capacity or consumer preferences of the firm and ultimately negatively affects consumer satisfaction. Thus, the present study extends TPB by showing that subjective norms may influence negative behavioural outcomes when the adoption of innovations is externally influenced rather than internally integrated. Second, the positive relationship between subjective norms and sources of innovation supports the assertion of TPB that perceived social pressures influence behavioural intentions. In the Ghanaian context, this indicates that business owners need to employ socially validated sources of the innovations such as recommendations from industry networks or successful competitors when making innovation adoption decisions. This finding enhances the explanatory power of TPB by showing how subjective norms influence behaviours associated with strategic sources of innovation. Furthermore, the study extends the subjective norm component of the Theory of Planned Behaviour, demonstrating that social pressures not only influence intentions to adopt innovation, but also influence the choice of innovation sources, which in turn translates into customer-related outcomes. Thus, by demonstrating that managers of small-scale hospitality firms rely on socialising credible innovation sources as a basis for improving service quality and customer satisfaction, the current study extends the subjective norm component of the Theory of Planned Behaviour beyond behavioural intention to encompass strategic sourcing. In this regard, the positive mediation path illustrates that subjective norms influence customer satisfaction indirectly through innovation sourcing, thus revealing a nuanced way through which societal expectations are transposed into performance-enhancing innovations in small-scale hospitality firms in Ghana. This study also advances the institutional theory by demonstrating how normative and mimetic pressures extend beyond symbolic conformity to shape concrete innovation-sourcing decisions in small-scale hospitality firms. It explains how socially embedded expectations are translated into structured operational actions through partnerships with legitimate industry actors. By linking institutional pressures to implementation quality and customer satisfaction outcomes, the study deepens understanding of how institutional forces influence not only organisational legitimacy but also service performance in resource-constrained contexts.

The practical implications of this study are profound for managers and policymakers in Ghana’s small-scale hospitality industry. First, the negative effect of subjective norms on customer satisfaction shows that managers should refrain from introducing innovations in reaction to peer or industry pressures. Innovation decisions must be made based on customer needs and relevance. In this regard, small-scale hospitality firms must perform customer need analyses and pilot studies before implementing technology or service innovations to be sure that they fit their capacity and clientele. Managers should also establish structured innovation evaluation frameworks that assess strategic alignment, operational readiness, financial feasibility, and customer impact before implementation. Developing simple innovation scorecards or decision matrices would help prevent socially driven but strategically misaligned adoption. Second, the positive effect of subjective norms on innovation source shows that social networks and industry conventions play an important role in determining innovation practice. Managers should utilise these networks not only as a source of social pressure, but also as a means of knowledge-sharing, collaboration and mentorship. Enabling partnerships with reputable suppliers, industry participants and tourism boards would facilitate the introduction of high-quality innovations, which have intrinsic value for the firms’ operations. At the policy level, tourism authorities and trade associations in Ghana should formalise innovation knowledge hubs, cluster networks, and shared digital learning platforms where small hospitality operators can access vetted innovation providers and best-practice case studies. Such institutional coordination would reduce misinformation and improve the quality of innovation diffusion across the sector. Third, the positive effect of innovation sources on customer satisfaction indicates that firms should make use of credible, relevant and customer-friendly sources of innovation. Small-scale hospitality firms should choose innovation providers who offer support and customisation in the locality, ensuring that the innovations adopted enhance service effectiveness, personalisation and positive customer experience. Managers should further integrate customer feedback analytics, post-adoption performance tracking, and service quality measurement tools to continuously evaluate whether adopted innovations translate into measurable satisfaction improvements. Data-driven monitoring will ensure innovation outcomes remain customer-centred rather than trend-driven. Finally, the positive mediation of sources of innovation indicates that small-scale Ghanaian hospitality firms would achieve better customer satisfaction by giving priority to access credible sources of both external and internal innovation that are perceived as socially approved by peers, industrial associations and customer networks. Managers must liaise with suppliers, tourism bodies, and professional groups to identify the socially validated source of an innovation used in their services that contributes to improved service quality and customer satisfaction. Beyond this, government agencies and financial institutions should design targeted innovation support schemes, including subsidised digital transformation programmes, innovation grants, and technical advisory services tailored to small hospitality enterprises. Providing structured capacity-building programmes in innovation management, digital literacy, and customer experience design would enhance firms’ ability to convert socially endorsed innovation sources into tangible service improvements.

While this study has made a substantial contribution to the greater understanding of the direct and mediating relationships between subjective norms, innovation source and customer satisfaction among small-scale hospitality firms, it is coupled with various limitations. First the theoretical framing focused only on the subjective norm component of the Theory of Planned Behaviour. While this provides interesting insights into the extent to which social pressures influence sourcing decisions for innovations, future studies that incorporate the attitude and perceived behavioural control components of TPB will provide comprehensive understanding of the behaviour determinants that account for innovation adoption by small-scale hospitality firms. Second, the study considered customer satisfaction as the only outcome variable; while this is important, it represents a partial view of customer response. In this regard, the inclusion of other customer-focused outcome variables such as customer loyalty, customer retention, customer service value and customer repurchase in future studies could enhance detailed understanding of broader customer-related outcomes in the small-scale hospitality industry. Finally, the employed purely quantitative research approach was effective in establishing the direct and mediating relationships between the variables under study. However, it failed to explain why such relationships exist or the experiences of respondents regarding the variables under investigation. Therefore, future studies could adopt a mixed-approach methodology, where qualitative measures such as interviews and focus group discussions could provide an in-depth understanding of how subjective norms about innovation adoption enhance innovation source and customer satisfaction.