Ibrahim Zuhuree (2018), PhD.

Abstract

Tourism small and medium-sized enterprises (SMEs) can identify new ideas yet lack the resources to bring them into routine use. This study examines whether managerial human capital, organisational learning and perceived destination support are associated with innovation among guesthouses in the Maldives during the formative expansion of local-island tourism. Establishment survey data cover 111 probability-selected unique firms across 24 islands. Innovation is measured in six domains and analysed as both the breadth of implemented innovation and an ordered adoption stage (no innovation, in progress, implemented). Poisson QMLE results show that firm size is the strongest correlate of implemented innovation breadth: a doubling of room capacity is associated with about 19% more implemented innovation domains. Human-capital, learning and destination-support measures do not explain breadth at conventional levels. In the adoption-stage model, however, at least three months of tourism-specific training is associated with a higher innovation stage (OR = 1.69), increasing the model-predicted probability of implementation from about 33% to 44%. The findings suggest that innovation breadth and innovation progression capture different constraints. In resource-constrained tourism SMEs, sector-specific skills may be more relevant to moving initiatives towards implementation, while organisational scale matters for implementing innovation across multiple business domains. The cross-sectional design supports associative, not causal, interpretation.

Keywords: tourism SMEs; innovation implementation; human capital; organisational learning; island destinations; Maldives

1. Introduction

Small tourism firms are expected to adapt quickly to changing technologies, distribution channels and visitor expectations, often with limited finance and management capacity. Innovation is therefore important to their competitiveness, but tourism SMEs facing similar market conditions differ markedly in their ability to introduce new services, processes and business practices. Tourism research has examined product, process, managerial and marketing innovation (Hjalager, 2010; Pikkemaat et al., 2019), while longitudinal evidence links training, collaboration, ICT and finance to innovation among tourism SMEs (Nguyen et al., 2021). Yet less is known about a practical problem faced by small firms: what helps an innovation move from an idea or work in progress into actual use?

This distinction matters because innovation adoption and implementation are not the same organisational outcome. A firm may recognise an opportunity and begin developing a change without having the resources or skills to complete it. Research on innovation implementation emphasises the organisational conditions needed to move an adopted idea into routine practice (Klein & Sorra, 1996; Enz & Way, 2016). In tourism, this issue is especially relevant because many innovations are operational rather than formal R&D: management software, a new marketing method, an accommodation product, food and beverage changes, wellness services or recreational activities.

Three sets of resources may shape this process. First, managerial human capital provides knowledge accumulated through education, training and industry experience (Becker, 1993). Second, firms need practices through which knowledge is acquired and used. This is consistent with absorptive-capacity theory, which links prior knowledge to the ability to recognise, assimilate and exploit external information (Cohen & Levinthal, 1990; Zahra & George, 2002). Tourism research has shown that external knowledge is particularly important to tourism organisations and that the capacity to use it cannot be taken for granted (Thomas & Wood, 2014, 2015). Third, tourism firms operate within destinations and depend on public agencies, associations, other businesses and communities. Destination relationships can provide information and complementary resources, although perceived support should not be equated automatically with social capital (Rastrollo-Horrillo & Rivero Díaz, 2019).

This study examines these relationships in the Maldivian guesthouse sector during an early phase of local-island tourism expansion. The setting is analytically useful because small accommodation firms emerged alongside a much older resort industry with established hospitality skills, international market links and formal management systems. The original doctoral research used a subset of the survey primarily to study room prices and service quality; the innovation, organisational-learning and destination-support blocks were not developed as a separate research problem (Zuhuree, 2017).

The study asks: To what extent do tourism-specific managerial human capital, organisational learning and perceived destination support explain the breadth and stage of innovation adoption in tourism SMEs after accounting for firm scale and maturity?

Innovation is observed in six business domains and measured in two ways: the number of domains in which innovation was already implemented, and the stage reached in each domain (no innovation, in progress, implemented). This distinction is consistent with the Oslo Manual's separation of innovation activity from innovations that have been introduced or brought into use (OECD & Eurostat, 2018).

The paper makes two contributions. First, it brings managerial human capital, organisational learning and destination support into one parsimonious framework while treating firm scale and maturity as structural conditions. Second, it shows why innovation breadth and innovation progression should not be treated as interchangeable outcomes. The evidence indicates that firm scale is most clearly related to implemented breadth, whereas tourism-specific training is more closely related to progression through the adoption process. This stage-sensitive view adds precision to tourism-SME innovation research, where recent reviews continue to call for stronger theory and context-specific evidence (Thomas et al., 2025).

2. Theoretical framework and hypotheses

2.1 Innovation in tourism SMEs

Tourism innovation often involves adapting services, technologies, marketing or organisational practices rather than formal R&D (Hjalager, 2010). Earlier hotel studies show that innovation varies with management arrangements, market strategy, size and location (Orfila-Sintes et al., 2005; Martínez-Ros & Orfila-Sintes, 2009). This is important for SMEs because introducing an innovation and completing its implementation can require different resources. The present study therefore treats innovation breadth and adoption stage as related but distinct outcomes.

2.2 Managerial human capital

Human-capital theory treats education, training and experience as investments in productive knowledge (Becker, 1993). In small tourism firms, strategic and operational decisions are often concentrated in the owner-manager, making sector-specific knowledge particularly relevant. Absorptive-capacity theory provides a mechanism: prior related knowledge helps organisations recognise and use new external information (Cohen & Levinthal, 1990; Zahra & George, 2002). Tourism applications similarly emphasise the capacity to acquire, assimilate and exploit knowledge (Thomas & Wood, 2014, 2015).

Empirical tourism research links training and skill-enhancing HR practices to innovativeness (Capelleras et al., 2021; Nguyen et al., 2021). The present data distinguish general formal education from prior resort-sector experience and tourism-specific training. Because resort employment may expose managers to service standards, technologies and formal operating systems, and sector-specific training provides directly applicable knowledge, both may support innovation.

H1. Higher tourism-specific managerial human capital is positively associated with tourism-SME innovation adoption.

2.3 Organisational learning

Individual knowledge does not automatically become a firm capability. Organisations need routines through which knowledge is shared and applied. This is central to absorptive capacity and is particularly relevant in service firms where innovation depends on employees across several operational roles (Thomas & Wood, 2015).

The survey observes two forms of learning practice. In-house training reflects internal development of supervisory, clerical/operative and labour staff. External knowledge acquisition is represented by formal or on-the-job learning, online learning and participation in tourism fairs. These channels are kept separate because they do not form a reliable single scale in the data. Firms that train staff and access relevant external knowledge more frequently should be better placed to develop and implement new practices.

H2. More intensive organisational-learning practices are positively associated with tourism-SME innovation adoption.

2.4 Destination support

Tourism products are jointly produced within destinations. Small accommodation firms depend on public services, local authorities, industry organisations, other businesses and communities. Relational resources can therefore supplement limited internal capabilities. Tourism evidence links relationships with firms, institutions and communities to SME innovation (Rastrollo-Horrillo & Rivero Díaz, 2019).

The present survey does not measure network density, trust or tie strength, so it would be misleading to label the measure “social capital”. Instead, it captures the breadth of perceived destination support: the number of relevant actors whose contribution or relationship is rated as adequate or somewhat adequate.

H3. Greater perceived destination support is positively associated with tourism-SME innovation adoption.

Firm scale and maturity are included as structural controls. Larger firms may have greater financial and staffing capacity, while older firms have had more opportunity to introduce changes. Prior hotel research identifies firm size as a recurrent correlate of innovation (Martínez-Ros & Orfila-Sintes, 2009).

3. Data and methods

3.1 Survey and analytical sample

The study uses establishment-level survey data collected from Maldivian guesthouses between August and September 2016. The questionnaire adapted established service-sector and tourism survey instruments to the Maldivian context (Enright & Newton, 2004; Poon, 1990; World Bank, 2013). Respondents were majority owners, share-owners or managers directly involved in operations. Interviews were normally conducted in Dhivehi and entered electronically during fieldwork. The survey covered 24 islands in five atolls and was restricted to islands with more than two guesthouses and to areas accessible within the fieldwork budget.

The raw Data sheet contains 150 completed records. Three records lack usable establishment identifiers. The remaining 147 observations represent 144 unique firms because three guesthouses were interviewed twice. For duplicate firms, one observation was retained using a pre-specified rule that prioritised a majority owner, then a share-owner, then a hired manager. The file identifies a probability-selected component and an additional oversample concentrated in Hulhumalé and Maafushi. After deduplication, the primary analytical sample contains 111 probability-selected unique establishments. The additional 33 firms are used only in robustness analysis.

Special survey codes for unavailable, non-applicable or non-response answers were converted to missing values. No multiple imputation was used because missingness in the variables entering the main models is small. The fully specified Poisson model uses 107 firms across 24 islands.

3.2 Innovation outcomes

The questionnaire asked whether, during the previous two years, the establishment had introduced a new or significantly improved practice in six domains: hotel-management software, marketing, accommodation, food and beverage, wellness, and recreational activities. Responses were “Yes”, “No”, “Still in progress”, “Don't know”, or “Not relevant”.

The primary dependent variable, implemented innovation breadth, is the number of domains reported as “Yes”, producing a count from 0 to 6. “No”, “Still in progress” and “Not relevant” are coded zero for this measure; “Don't know” is treated as missing. A robustness outcome counts both implemented and in-progress innovations.

A complementary firm-domain dataset preserves adoption stage: 0 = no innovation, 1 = in progress, and 2 = implemented. “Don't know” and “Not relevant” are excluded. This yields 618 usable firm-domain observations from 109 firms in the estimation sample.

3.3 Explanatory variables

Managerial human capital. Prior resort experience is entered categorically: none, 1–2 years, 3–5 years, 6–10 years, and more than 10 years. The open-ended top category is the reason categorical coding is used in the main models; midpoint years are used in robustness tests. Tourism-specific training equals one where the respondent reports at least three months of tourism-related training in the Maldives or abroad. Formal education records the highest level attained from primary to degree and above.

Organisational learning. In-house training is the mean of training-intensity items for supervisory/technical, clerical/operative and labour staff. The three items show strong internal consistency (Cronbach's α = .959). Formal/on-the-job learning, online learning and tourism-fair learning are reverse-coded so that higher values indicate more frequent use. They are entered separately because they do not form a reliable common scale.

Destination support. Six items cover government ministries, local councils, the Maldives Association of Tourism Industry, guesthouse associations, local businesses and the local community. Each receives one where its contribution or relationship is rated “adequate” or “somewhat adequate”, and zero otherwise. Their sum (0–6) measures the breadth of perceived destination support. It is a formative index, not a reflective social-capital scale.

Controls. Firm maturity is the natural logarithm of months in operation plus one. Firm size is the natural logarithm of room capacity; employee count is used as an alternative measure. A respondent-position indicator distinguishes owners/share-owners from hired managers. Atoll fixed effects capture broad geographic differences.

3.4 Estimation

Implemented innovation breadth is estimated using Poisson quasi-maximum likelihood (QMLE). The model specifies

E(Y_i|X_i) = exp(α + H_iβ + L_iθ + δS_i + C_iγ + λ_a),

where H denotes human-capital variables, L organisational-learning variables, S destination support, C firm controls, and λ_a atoll fixed effects. The models are estimated in blocks: controls; human capital; learning; and destination support. Poisson QMLE is used for the conditional mean rather than because the outcome is assumed to follow a literal Poisson distribution (Cameron & Trivedi, 2013). The observed variance of innovation breadth is close to its mean, providing no initial case for preferring a negative-binomial model.

The adoption-stage outcome is estimated using ordered logistic regression with fixed effects for the six innovation domains. The proportional-odds assumption is checked, and threshold-specific cumulative logits are reported where it is violated (Brant, 1990; Williams, 2006).

Standard errors are clustered by island because firms share local infrastructure, institutions and market conditions. With 24 clusters, conventional cluster-robust inference may be optimistic, so key results are checked with few-cluster bootstrap diagnostics (Cameron & Miller, 2015; Roodman et al., 2019). Coefficients are interpreted as conditional associations, not causal effects.

Robustness tests use the full unique-firm sample, alternative size and experience measures, innovation activity including in-progress projects, and a restricted sample of firms operating for at least 24 months. The main count model contains 107 firms and about 227 implemented innovation-domain events; model complexity was therefore kept deliberately modest and large structural models were avoided.

4. Results

4.1 Descriptive pattern

The 111 probability-selected firms were small and young: mean room capacity was 8.2 rooms and mean operating age was 23 months. Forty-four per cent of respondents reported at least three months of tourism-related training. Among the 108 firms with a complete breadth outcome, the mean number of implemented innovation domains was 2.12 (SD = 1.39).

Innovation differed by domain. Marketing was most frequently implemented, followed by recreation, management software and food and beverage. Accommodation innovation was less common and wellness innovation was rare. The presence of substantial “in progress” responses supports analysing adoption stage as well as completed breadth.

4.2 Implemented innovation breadth

Table 2 reports Poisson QMLE estimates. Firm size is the clearest correlate of implemented innovation breadth and remains stable as capability variables are added. In the full specification, the incidence-rate ratio (IRR) for log room capacity is 1.288 (p = .003). Because size is logged, doubling room capacity corresponds to approximately a 19% increase in the expected number of implemented innovation domains, holding other observed characteristics constant.

Firm maturity is also positive (IRR = 1.212, p = .026), although the few-cluster bootstrap diagnostic weakens the evidence to approximately p = .059. It is therefore treated as suggestive.

The human-capital block is not jointly significant (p = .143). None of the resort-experience categories or formal education levels predicts breadth, and tourism training of at least three months is positive but insignificant (IRR = 1.176, p = .195). Organisational-learning variables are also jointly insignificant (p = .325), and perceived destination-support breadth is unrelated to implemented breadth (IRR = 0.967, p = .460).

4.3 Innovation adoption stage

The ordered model reveals a different result for tourism-specific training. Training of at least three months is associated with higher odds of being at a later innovation stage (OR = 1.685, p = .020). Holding the other observed characteristics at their sample values, the model-predicted probability of an innovation being implemented rises from about 33.4% without the training indicator to 44.0% with it.

Formal/on-the-job learning is positive but only marginally significant (OR = 1.152, p = .096). In-house training, online learning, tourism-fair learning and destination support do not reach conventional significance.

The proportional-odds assumption is violated for firm size (p = .012). Threshold-specific estimates show little relationship between size and merely beginning innovation activity (OR = 1.050, p = .825), but a larger association at the threshold separating implemented innovations from lower stages (OR = 1.502, p = .058). This pattern suggests that organisational scale may matter more for completing innovation than for starting it.

Robustness checks using employee numbers instead of rooms retain the positive size result. Midpoint years of resort experience remain insignificant. In the larger unique-firm sample, the tourism-training coefficient for innovation breadth becomes more precise but remains outside the 5% threshold. Overall, H1 receives partial and outcome-dependent support, H2 limited support, and H3 no support.

5. Discussion

The principal result is the distinction between innovation breadth and innovation progression. Firm scale is the strongest correlate of how many innovation domains have been implemented, while tourism-specific training is more clearly associated with how far a particular innovation has progressed. Treating firms simply as innovators and non-innovators would obscure this difference.

This finding adds a stage-sensitive perspective to tourism-SME innovation research. Nguyen et al. (2021) show that training, collaboration, ICT and finance are associated with innovation among tourism SMEs. The present results refine that picture: a capability may not increase the number of areas in which a small firm innovates, but it may help move an existing initiative towards implementation. That interpretation is consistent with the wider distinction between adoption and implementation (Klein & Sorra, 1996) and with hotel evidence showing that post-adoption implementation strategies matter for innovation outcomes (Enz & Way, 2016).

The scale result is equally important. A doubling of room capacity is associated with about 19% more implemented innovation domains, and the threshold analysis suggests that size matters more at implementation than at initiation. Earlier hotel studies also identify size as a determinant of innovation (Orfila-Sintes et al., 2005; Martínez-Ros & Orfila-Sintes, 2009). The present data cannot show why scale matters, but a resource-threshold interpretation is plausible: beginning a marketing change or considering a new service may require limited resources, whereas putting it into routine operation can demand capital, employee time and management attention. Recent emerging-market evidence similarly emphasises constraints that shape innovation adoption in tourism SMEs (Lin et al., 2023).

Tourism-specific training appears to operate differently. It does not significantly widen the number of implemented innovation domains, but it is associated with a higher adoption stage. This is consistent with absorptive-capacity arguments that relevant prior knowledge helps firms interpret and exploit external information (Thomas & Wood, 2014, 2015). The result does not establish that training causes implementation: innovative managers may seek training, and firms preparing changes may train managers because implementation requires additional skills. Nevertheless, the contrast between training and general education is informative. Human capital is not homogeneous, and sector-specific knowledge may be more closely connected to operational implementation than general qualifications.

Prior resort experience does not display the expected positive pattern. This is notable because the Maldivian resort sector is a plausible source of hospitality knowledge for guesthouses. One possibility is that resort experience provides operational skills without necessarily transferring entrepreneurial routines suited to small firms. Another is measurement: the survey records duration, not job function or managerial responsibility. The null result therefore cautions against assuming that labour mobility automatically produces innovation-relevant knowledge spillovers.

H2 receives only limited support. The highly reliable in-house training index and separate external-learning channels do not predict breadth, while formal/on-the-job learning is only weakly associated with adoption stage. This does not imply that learning is unimportant. The survey measures participation in learning activities, not the full process of acquiring, assimilating, transforming and exploiting knowledge. Thomas and Wood (2015) make precisely this distinction in their tourism-specific model of absorptive capacity. Future surveys should measure those stages directly.

Perceived destination support is unrelated to innovation. This differs from research linking destination social capital to SME innovation (Rastrollo-Horrillo & Rivero Díaz, 2019), but the constructs are not equivalent. The present measure records whether several actors are perceived as supportive; it does not observe trust, tie strength, knowledge exchange or collaboration. One intensive knowledge-sharing relationship may matter more than generally adequate relationships with several organisations. The null result therefore reinforces the need for precise measurement rather than showing that destination relationships are irrelevant.

The managerial and policy implication is practical. Innovation support should distinguish between encouraging firms to generate or begin new ideas and helping them complete implementation. Training programmes may be more useful when linked to mentoring, technical assistance or implementation plans. If micro-enterprises face resource constraints at the completion stage, shared digital tools, pooled technical services, cooperative marketing or implementation finance may lower fixed costs. These are policy propositions, not effects tested by this study.

More broadly, the findings support a resource-conditioned view of tourism-SME capability. Skills and learning opportunities operate within organisations of different scale and maturity. For small firms, the central question may not be whether they can identify innovation opportunities, but whether they can mobilise the resources needed to finish what they start.

6. Conclusion, limitations and future research

This study examined innovation among guesthouse SMEs during the formative expansion of local-island tourism in the Maldives. Its main contribution is to show that implemented innovation breadth and progression through the adoption process are distinct outcomes. Firm scale is most consistently associated with breadth and appears particularly relevant at implementation, while tourism-specific training is associated with a higher stage of innovation adoption. General education, prior resort experience, the measured learning channels and perceived destination support provide little independent explanatory power.

The results should be read within clear limits. The data are cross-sectional, so temporal ordering and causality cannot be established. The 2016 survey captures an earlier phase of the guesthouse sector and should not be treated as a description of current firms. The sampling frame was geographically restricted and based on establishments visible through an online booking platform. Several constructs are coarse: resort experience records years rather than job role, training records duration rather than content, and destination support is not a network measure. Innovation is self-reported and captures adoption status rather than commercial success, investment value or persistence.

These limitations also define the research agenda. A follow-up of the identified firms could test whether 2016 capabilities and innovation profiles predict survival, growth and subsequent innovation. Future surveys should distinguish opportunity recognition, adoption, implementation and sustained use; record why projects stall; measure absorptive capacity directly; and map actual knowledge-sharing relationships. Linking innovation data to revenue, occupancy, employment and tax records would allow the economic returns to different innovations to be assessed.

For tourism-SME research, the practical distinction is simple but important. Small firms may be able to generate ideas and begin changes without being able to complete them. Understanding innovation therefore requires attention not only to who innovates, but to what enables an SME to move innovation into routine use.

Tables

Table 1. Descriptive statistics for the probability-selected sample

Table 2. Poisson QMLE models of implemented innovation breadth (IRRs)

Island-clustered p-values in parentheses; atoll fixed effects included in every model.

Table 3. Innovation adoption stage: ordered and threshold-specific estimates

Note. The proportional-odds assumption is violated for firm size (p = .012); threshold-specific estimates are therefore central to its interpretation. The ordered model uses 618 firm-domain observations from 109 firms.

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