Transformation of the hospitality services marketing structure: A chaos theory perspective

Purpose. Drawing on chaos theory as an overarching approach, as well as guidelines from effectuation and transformative learning theories, this study evaluates the changing marketing channels in the hospitality industry in the wake of the COVID-19 pandemic. It also aims to develop a conceptual framework that demonstrates the transformation of the marketing structure; in particular, the transformation of hospitality organizations, employees, and customers. Design/methodology/approach. The study utilizes the hermeneutic method and conceptually evaluates the existing actors of the services marketing structure. It also discusses how to transform this structure into the new normal in the wake of the COVID-19 pandemic. Findings. The findings of the study demonstrated that COVID-19 has resulted in changing marketing channels in the hospitality industry. These include external, internal, interactive, and substitutional marketing channels. In response to these changes, the hospitality industry needs to adopt a more transformative marketing structure that requires the transformation of hospitality companies, employees, and customers. Originality. This study utilizes chaos, effectuation, and transformative learning theories in order to reconceptualize the hospitality services marketing structure. The contribution of this paper lies in the conceptual pathways it suggests for transforming hospitality firms, employees, and customers and for demonstrating their transformed roles and positions in the wake of the pandemic. Research limitations/implications. The conceptualized transformation of the services marketing structure could help hospitality practitioners, employees, and customers to understand the new normal and acquire new abilities, meanings, awareness, and learning accordingly.

Co-occurrence network analysis (CNA) as an alternative tool to assess survey-based research models in hospitality and tourism research

Hospitality and tourism (H&T) researchers employ structural equation modeling (SEM) and other multivariate techniques to test their models with survey data. These approaches assess relationships among constructs and model fit, but they do not highlight the most influential survey items or links among them. Other challenges include method-specific requirements for appropriate data, the best indices to identify optimal models, minimum sample sizes, missing data, and interpreting the results from complex models. Co-occurrence network analysis (CNA) can mitigate these limitations. This study validates CNA in the H&T field with a survey dataset that assesses market strategy, nonmarket strategy (NMS), organizational values, and firm performance. CNA is proposed as a complement to existing multivariate approaches for assessing survey data. The assessment includes nine steps: (1) identify the research purpose and hypothesis, (2) determine the hypothesis-related items to measure, (3) determine the sample, (4) administer the survey, (5) determine the analysis method, (6) test the hypotheses, (7) prepare survey inputs for CNA, (8) employ CNA, and (9) visualize and interpret results. This pathway demonstrates how future research can apply and address CNA’s advantages and limitations

Co-occurrence network analysis (CNA) as an alternative tool to assess survey-based research models in hospitality and tourism research

Hospitality and tourism (H&T) researchers employ structural equation modeling (SEM) and other multivariate techniques to test their models with survey data. These approaches assess relationships among constructs and model fit, but they do not highlight the most influential survey items or links among them. Other challenges include method-specific requirements for appropriate data, the best indices to identify optimal models, minimum sample sizes, missing data, and interpreting the results from complex models. Co-occurrence network analysis (CNA) can mitigate these limitations. This study validates CNA in the H&T field with a survey dataset that assesses market strategy, nonmarket strategy (NMS), organizational values, and firm performance. CNA is proposed as a complement to existing multivariate approaches for assessing survey data. The assessment includes nine steps: (1) identify the research purpose and hypothesis, (2) determine the hypothesis-related items to measure, (3) determine the sample, (4) administer the survey, (5) determine the analysis method, (6) test the hypotheses, (7) prepare survey inputs for CNA, (8) employ CNA, and (9) visualize and interpret results. This pathway demonstrates how future research can apply and address CNA’s advantages and limitations

Appreciation to referees, 2023

Saif Benjaafar, Editor-in-Chief of Service Science, thanks the referees who have generously provided expert counsel and guidance on a voluntary basis to the journal. Without them, the journal would not be able to function. The following list acknowledges those who acted as referees for papers considered during this past calendar year

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)