Tourism transport enterprises — coach operators, shuttle services, and tour-vehicle providers — perform a connective function across the visitor journey, linking accommodation, attractions, and points of entry. Reliability and safety performance in this sector carry consequences beyond individual passenger experience, since transport failures can disrupt multi-stop itineraries and affect destination reputation more broadly than a single underperforming hotel or restaurant typically would.

Despite this connective importance, competitiveness measurement for tourism transport enterprises has received comparatively little attention relative to accommodation and food service. This article addresses that gap by presenting a structured survey instrument for tourism transport enterprises and applying a benchmark-normalization, weighted integral-index method — adapted with an inverse-normalization treatment for safety — to convert survey responses into a single integral competitiveness index per enterprise.

The purpose of this article is fourfold: (1) to present a survey instrument tailored to tourism transport enterprises; (2) to map survey-derived indicators onto four operational criteria — punctuality, an inverted safety indicator, customer satisfaction, and load factor; (3) to illustrate the method, including the inverse-safety-normalization step, using an eight-enterprise sample; and (4) to discuss the policy implications of the results, including the sensitivity of inverse normalization to outlying incident counts.

The United Nations World Tourism Organization's guidance on tourism transport [1] identifies punctuality and service reliability as core determinants of visitor satisfaction with ground-transport components of a trip, a finding consistent with broader transport-economics literature linking schedule adherence to perceived service quality.

Unlike the other criteria used in this and companion studies, safety carries non-uniform polarity: more incidents indicate worse, not better, performance, requiring an inverse-normalization treatment rather than the direct normalization applied to punctuality, satisfaction, and load factor. This asymmetry is a recognized feature of composite-indicator construction generally [2,3], which caution that indicators with differing polarity must be harmonized — typically by inversion — before aggregation, to avoid systematically distorting the resulting index.

As in the companion hotel- and restaurant-sector studies, Camp's benchmarking principle [4] underlies the normalization step, Man, Lau and Chan [5] and Ambastha and Momaya [6] provide the firm-competitiveness rationale for combining multiple indicator types, and Saisana and Tarantola's [7] caution regarding weighting-scheme sensitivity is revisited in the Discussion together with the additional sensitivity introduced by inverse normalization.

Duval [8] frames tourism transport as both an enabling and a constraining factor in destination accessibility, a perspective consistent with this study's treatment of transport enterprises as a distinct competitiveness-relevant sector. Khadaroo and Seetanah [9] provide empirical evidence that transport infrastructure quality is significantly associated with destination-competitiveness rankings, reinforcing the rationale for assessing transport-enterprise competitiveness alongside accommodation and food service. Prideaux [10] examines the role of transport development in shaping tourist-destination growth trajectories, while Lumsdon [11] documents demand-side determinants of tourist transport-mode choice relevant to the punctuality and satisfaction criteria used in this study. Page and Connell's [12] tourism-transport overview and Litman's [13] transportation cost-benefit framework together inform the questionnaire's operational and sustainability sections (Sections 2 and 7).

The survey instrument comprises eight sections administered to transport-enterprise managers: (1) enterprise profile — fleet size, vehicle types, year of establishment; (2) operations — average daily routes, average passengers per trip, and seasonal variation; (3) punctuality — share of trips departing/arriving within a defined schedule tolerance; (4) safety and technical reliability — recorded incidents, breakdowns, and maintenance frequency over the preceding twelve months; (5) customer satisfaction — passenger-feedback proxy; (6) capacity utilization — load factor, defined as the ratio of passengers carried to total seat-capacity offered; (7) sustainability — fuel type, vehicle age, and emissions-reduction measures; and (8) open-ended commentary on perceived competitive advantages and constraints.

Four indicators populate the transport-enterprise module: punctuality (PUN), safety (SAFE, inverse-normalized from recorded incidents, ACC), customer satisfaction (SAT), and load factor (LOAD). Punctuality, satisfaction, and load factor are normalized directly against the sample maximum:

Ii,norm = Ii,raw / max_j(Ij,raw) (1)

Safety is normalized inversely, since a higher incident count indicates worse performance:

SAFEi = 1 − ACCi,raw / max_j(ACCj,raw) (2)

with SAFEi defined as 1 if max_j(ACCj,raw) = 0 (no incidents recorded across the sample). The integral competitiveness index is then the weighted sum:

Integral(Tr)i = w1·PUNi + w2·SAFEi + w3·SATi + w4·LOADi (3)

with illustrative weights w = (0.3, 0.3, 0.2, 0.2), giving punctuality and safety somewhat greater weight than satisfaction and load factor, following the general formulation developed for the BoburCalculator algorithm [14].

The instrument was administered to eight tourism transport enterprises (T1–T8) operating across Uzbekistan's principal tourist routes. As with the companion sector studies, the sample is illustrative rather than representative, disclosed to demonstrate the computational mechanics of the inverse-safety-normalization method.

T7, despite mid-range punctuality and satisfaction scores, ranks at the bottom of the sample because its incident count (six, the sample maximum) drives its inverse-normalized safety score to zero. T4 similarly underperforms across punctuality and load factor in addition to a high incident count. This pattern illustrates the safety criterion's capacity to dominate the overall ranking when incident counts vary widely across a small comparison sample — a property of the inverse-normalization step that operators and regulators should anticipate when applying the method.

Global travel and tourism employment grew from an estimated 338 million to 371 million jobs over the period reviewed by sector estimates [15], a trend that raises the operational stakes of transport reliability and safety performance as passenger volumes and the workforce supporting them continue to expand.

Three considerations qualify the use of this method. First, self-reported incident and punctuality data are exposed to under-reporting risk, particularly for safety incidents that enterprises may have an incentive to omit; independent verification against insurance-claim or regulatory-incident records, where available, would strengthen confidence in the safety criterion specifically. Second, the Analytic Hierarchy Process or comparable structured weight-elicitation methods could be used to derive weights reflecting the relative priority that destination management organizations and safety regulators place on punctuality versus safety versus service quality, rather than relying on the illustrative weights used here. Third, destination management organizations could use cluster-based route benchmarking — comparing enterprises operating similar routes or passenger volumes — to avoid penalizing enterprises operating under structurally more demanding conditions (e.g., longer routes, more challenging terrain) within the same comparison group as enterprises operating under easier conditions.

This illustrative application is not a population-representative survey of Uzbekistan's tourism transport sector; the eight-enterprise sample demonstrates the computational mechanics of the inverse-safety-normalization method rather than supporting generalizable claims. The inverse-normalization step is sensitive to the single worst-performing observation (the sample maximum incident count, ACCmax): a single high-incident outlier compresses the SAFE scores of all other enterprises toward the low end of the [0,1] range, a property that should be disclosed whenever the method is applied to small samples. Finally, the four weighted criteria omit potentially relevant dimensions such as fleet age, vehicle comfort, and fare competitiveness, which could be incorporated in extended applications of the method.

This study presented a structured survey instrument and a benchmark-normalization, weighted integral-index method — incorporating an inverse-normalization treatment for safety — for assessing tourism transport enterprise competitiveness, illustrated using an eight-enterprise sample. The results show that safety performance can dominate the overall ranking when incident counts vary widely across the comparison sample, underscoring the importance of independent safety-data verification when the method informs regulatory or recognition decisions.

The method offers destination management organizations and transport-enterprise managers a transparent way to benchmark competitiveness while explicitly accounting for safety's distinct, inverse polarity relative to other performance indicators. Cluster-based route benchmarking, independent incident-data verification, and broader criteria coverage represent priority directions for follow-up empirical work using larger, randomized samples.

The author thanks the International University of Tourism and Cultural Heritage “Silk Road” for institutional support during the preparation of this study. No additional acknowledgments apply.

This research received no external funding from any funding agency in the public, commercial, or not-for-profit sectors.

The author declares no conflict of interest.