Role of network capability, structural flexibility and management commitment in defining strategic performance in hospitality industry
Bibliographic record
Abstract
Purpose This study aims to present an empirical model related to strategic performance (SP) of the hospitality industry. It focuses on the role of network capability (NC) in defining SP through the mediating role of structural flexibility (SF). Furthermore, the interaction effect of NC and top management commitment to strategic performance (MCSP) on SP is also tested. Design/methodology/approach A sample of 279 managerial-level employees of four-star and five-star hotels has been used to confirm the proposed hypotheses by using the technique of structural equation modeling. Findings The results reveal that NC positively affects SP. Moreover, the mediating role of SF in defining the nexus of NC and SP has also been confirmed. Results of moderation analysis reveal that MCSP strengthens the relationship between NC and SP. Research limitations/implications This study used a cross-sectional design for data collection, which prevents strong causal inferences. The authors recommend scholars to explicitly test for causal effect. This study used a cross-sectional design for data collection, which prevents strong causal inferences. The authors recommend scholars to explicitly test for causal effect among all these variables by using a longitudinal study in the future. Practical implications In developing countries, it has been observed that the hospitality industry pays less attention to its strategic targets. Operating in a network or adapting flexible structures is also not on their priority list. This study presents a pragmatic approach based on strong theoretical grounds to attain the goals of SP in the hospitality industry through NC and SF. Therefore, this study suggests that organization operating in the tourism and hospitality industry should pay greater attention toward synergies and business networks to achieve SP. Originality/value This research enriches the prevailing knowledge by testing a mediating role of SF between NC-SP link and, therefore, makes an important addition to the existing knowledge on tourism and hospitality industry by concentrating on the relationship between NC, SF, MCSP and SP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".