Dynamic Capabilities and Digital Orientation in the UAE

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Abstract

  • Research Purpose – The purpose of this study is to explore whether dynamic capabilities and digitalization support the improvement of hotel supply chains during crisis times based on the case of UAE, as well as to recommend potential service-level improvements to drive customer-centric thinking.
  • Research Methodology – A quantitative research approach has been chosen to validate assumptions stated in recent publications devoted to hospitality industry supply chain changes and the role of digital technologies. 42 out of 86 participants, who are employees of UAE hotels volunteered to participate in a survey that comprises 34 categorized questions related to the areas of dynamic capabilities, digital orientation, and supply chain resilience, agility, and traceability.
  • Findings – the research has shown that dynamic capabilities and digital orientation do not support supply chain optimization in UAE hotels. Furthermore, it was found that hotel employees might be over-optimistic about their opinions regarding supply chain efficiency, which potentially could lead to the loss of customer loyalty and revenues. Eventually, it could be explained by the coronavirus pandemics that led to travel bans and consecutive inconsistency in organizational transformation.
  • Practical Implications – research findings suggest that hotel management should be more proactive in developing employee motivation and business stance understanding to handle crisis periods and be prepared for new ways in improving customer service.
  • Originality/value – it was identified that there is a research gap in exploring hotel capabilities and digital orientation to optimize supply chains in a UAE context; hence, this study brings a token of academic contribution to address the aforementioned gap efficiently.

Introduction

The success of the hospitality industry in developing tourism as a source of economic prosperity heavily depends on the operational capacity of hotels. In UAE, this idea was grasped by the government and converted into a large-scale redevelopment of hospitality infrastructure to ensure that tourists from numerous locations over the world experience comfort and enjoyment after visiting the country. The need for special care and personalized services was considered one of the most significant elements of the tourist attraction strategy in line with emerging trends of service innovation. Customer value and experience co-creation were capitalized as the driving competencies to ensure that demand meets supply, as well as to ensure that hotel operators are well informed about the wants and preferences of potential customers (Gretzel et al., 2015). Overall, these efforts could be summarized as dynamic capabilities of hotels, where organization can sense the market and the customer, research and comprehend new market information, and transform business operations based on accumulated expertise.

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Alternatively, with a dynamically developing role of information and communication technologies (ICT), hotels were supposed to reapply the principles of commercial companies that seek for the data-driven solutions to improve market competitiveness and generate more revenues. A notable example is mass implementation of smart hospitality systems based on the provision of the Internet of Things (IoT) technology and active use of interactive social media platforms to understand what factors directly influence booking intentions. Buhalis and Leung (2018) mentioned several cases of innovative use of ICT in hotels, including the use of ReviewPro application to critically monitor negative reviews of the hotel services and enable hotel operators for a prompt reaction, as well as the use of automated control centers in Mariott hotels for geofencing and dynamic updates on the expected customer arrivals. Internal hotel services were also enhanced through the use of sensors and intelligent rooms, where customers are free to leverage technology to improve the sense of comfort (Lam & Law, 2019). These and related efforts could be conceptualized as digital orientation, where the use of technology transforms the way of service delivery and positively contributes to customer centricity.

Despite the aforementioned advantages in organizational structure and capabilities of UAE hotels, it remains unclear if new developments have a positive effect on the hotel supply chain productivity. Particularly, it is important to admit that overall success of hotels during the current year was heavily affected by coronavirus pandemics, travel restrictions, and global economic downfall. Resilience, or the ability of the organization to restore to normal operations remains doubtful, since the economic impact of travel bans creates a blackhole in hotel operations and requires seeking for alternative ways of attracting customers. Hence, it is important to critically evaluate if improvement in dynamic capabilities and digital orientation are advantageous for hotel supply chains in terms of resilience, agility, and traceability.

Research Objectives

The purpose of this research is to evaluate whether optimization of capabilities and focus on digitalization of services is advantageous for hotel supply chain improvements during the crisis times based on the case of UAE hotels. The research question is formulated as follows: ‘should we consider the dynamic capabilities and digital orientation as factors that contribute to changes in hotel supply chain resilience, agility, and traceability?’. Based on the above, the following research objectives are defined:

  • To explore organizational vision about current dynamic capabilities and digital orientation based on hotel staff responses in UAE;
  • To investigate if customer-centric trends of capability development and digital advancements help to optimize hotel supply chains during pandemics and critical economic periods
  • To provide recommendations for better optimization of hotel supply chains based on both primary and secondary research

Importance of the Research

The research importance to the contemporary organizational development in hospitality industry is justified in the following ways. First, it is important for hotel management to understand whether current practices and strategies are effective to attract and retain customers during crisis times. Second, it is important for evaluation and potential redesign of marketing campaigns and supply chain revamping to attract more tourists to UAE in line with economic development standards. Finally, the research will support professional interests in business sectors to ensure that hotels are seen as strategic players in UAE economy because of focus on tourism and construction industry as new value-generating segments.

Research Contribution

Researchers anticipate that findings will contribute to specific changes in UAE hospitality industry based on the following assumptions. First, it is assumed that during coronavirus pandemics hotels still can develop policies to attract local tourists, which will empower future supply chain resilience and agility in meeting customer demands under restrictive policies of serving visitors and managing personnel. Second, hotels might find new ways of persuading government to invest in digital orientation that minimizes human contact while allows to enjoy the benefit of staying in a hotel regardless its star rating. Finally, the research contributes to academia because of the existing gap in exploring how to manage hospitality industry during the times of global pandemics.

Literature Review

Dynamic Capabilities

A high-level definition for dynamic capabilities used by hotels could be summarized as the ability to sense the market, seize the business, and transform it to successfully meet increasing customer demands. Khan et al. (2017) admitted that successful development of dynamic capabilities is closely related to destination management practices, where visitors are targeted and retained through continuous efforts to capitalize on experience and location on top of services provided. Gretzel et al. (2015a) supported this idea by admitting that modern consumers no longer seek for a simple purchase of service, but rather for experiences acquired through the product consumption. In terms of economic theory, it could be said that such stance is dictated by the importance of product differentiation, where product similarity principle is rejected by educated consumers who are able to find new information on the web (Khan et al., 2017). Hence, dynamic capabilities should be based on the principles of customer centricity, where hotel operators act as professional servants who understand specific needs of particular customers, anticipate the risks of losing loyal customers, and act as advisors in choosing best service depending on the needs and travel budget.

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Digital Orientation

Digital orientation in hotels worldwide is primarily associated with the notion of smart hospitality and the use of IoT. It is majorly driven by proliferation of ICT and social media as the driving vehicles for experience co-creation (Jasinskas et al., 2016). Primarily, digital orientation is focused on developing a logical online platform for the opinion exchange between hotel operators and visitors to ensure that there is an open dialogue that could benefit both customers and service delivery agents. Jasinskas et al. (2016) also mentioned that digital orientation helps to nurture a smart ecosystem that help in trip planning, feedback collection, and improvement of destination management practices. Furthermore, digital orientation refers to the ways how hotels interact with visitors on site, starting from the check-in procedures and ending with a common in-room services. For instance, Zaidan et al. (2016) mentioned that interoperability is an integral part of the smart hospitality network based on the of estimating hotel room occupancy rates using big data and embedded statistical models, which is a cost-saving benefit for hotel operators. Hence, digital orientation helps in meeting tough customer demands and eases routine management tasks.

Supply Chain Resilience

In hospitality industry, supply chain resilience should be evaluated from two different perspectives. The first refers to the resilience in recovering from the critical phases of the business, where the hotel faced a loss of customers because of the external factors such as pandemics, travel restrictions, or government-imposed limitations to operate (Moon & Sprott, 2016). The second refers to the ability to restore supply chain of goods and services as a result of procurement changes, critical loss of supplies, internal conflicts that led to management disagreements, or legislative changes (Moon & Sprott, 2016). In the first case, the level of hotel supply chain resilience could be improved based on reapplication of crisis management practices in the industry, while eventually it can be complex to implement with maximum possible effectiveness for new or small hotels (Brown et al., 2017). In the second case, resilience improvement is a subject to thorough planning, use of effective procurement systems, and developing productive relationships with suppliers. However, in both cases hotels remain extremely vulnerable to factors such as travel bans, where they experience heavy revenue losses both in tourism and business services sectors.

Supply Chain Agility

In the context of hospitality industry, supply chain agility refers to the ability of the hotel to promote transparent information flow among customers, suppliers, and third parties and to develop network resource consistency. The most agile hotel supply chains are based on the most recent and advanced ICT that allows both virtual and temporal collaboration in a network to optimize resource usage, internal organizational processes, and improve overall market competitiveness (Samdantsoodol et al., 2017). It implies that supply chain agility relates to digital orientation and organizational mastery in leveraging the benefit brought by ICT, while also requires significant investments in revamping internal processes, systems, and employee training (Zaidan et al., 2016). Alternatively, supply chain agility should be seen through the lenses of organizational adaptivity to external shocks and ability to perform well during crisis times without taking significant time to restore back to normal operations (Moon & Sprott, 2016). Considerably, to establish agile supply chains hotels should regularly monitor and analyze market trends and focus on the principles of continuous improvement to ensure that business operations are cost-efficient and positively contribute to customer experience co-creation.

Supply Chain Traceability

Supply chain traceability is a concept that explains the ability of organization to trace history and application of particular entity through records over the entire supply chain. For the hospitality industry, the concept primarily refers to the ability of tracing the source of raw materials such as food supply, the ability to evaluate the level of supply chain’s environmental footprint, as well as having robust tracking systems to understand the overall supply chain effectiveness. However, for the hotels in particular traceability is also important to explore customer behaviors and destination management through the use of ICT, since hotels gain advantage from understanding the home location of the most frequent tourists and therefore could target prospective customers more effectively (Masiero et al., 2019). Furthermore, traceability is beneficial in terms of monitoring and collecting customer feedbacks regarding hotel services through circulation of reviews posted in both social networks and recommendation websites such as TripAdvisor. Consequently, the profound capacity on tracing customers through their digital footprints in the web helps hotel operators to develop effective attraction and retention strategy that is based on the analysis of past experiences through the use of technology.

Research Gaps

Previous analysis shows that there is sufficient research for defining terms chosen for evaluating the effectiveness of supply chains in hotels. However, several research gaps were identified as those expected to be addressed through the primary data analysis. First, it was found that in UAE context there is a lack of publications that should be considered as a good source of understanding how capability building and digital focus are related to the supply chain effectiveness in hotels. The exception are two studies by Khan et al. (2017) and Zaidan et al. (2016), while both have their own limitation with the first being extensively focused on digital orientation and the second attempting to evaluate social implications rather than supply chain resilience. Second, because of pandemics there are little studies that provide recommendations for improving supply chain resilience and agility in hotels, which requires more active research in this area. Hence, this study aims to address these gaps and provide factual background to improve awareness about current state of hotel supply chains in UAE.

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Research Methodology

Research Approach

The overarching research approach used for the current study is quantitative analysis. Academic researchers use quantitative analysis to verify specific theory or phenomenon through a scientific inquiry that is based on defining variables and seeking for particular relationships through the use of hypotheses and generalizations (Creswell & Creswell, 2017). The data collection method used for the study is questionnaire distribution among hotel employees in UAE. The data analysis is based on identifying the relationship between two variables and seeking for the statistical significance that confirms such relationship. The selection criteria used to identify participants is random sampling, since our objective is to research UAE hotels in general without any specific prerequisites. Hence, it is expected that quantitative approach will help to effectively use numbers and statistics to meet the research objectives.

Questionnaire Design

The questionnaire for the study was designed in a way to assess respondent opinions in the dimensions of dynamic capabilities, digital orientation, as well as supply chain resilience, agility, and traceability. The first part of the questionnaire was intended to collect personal information to determine respondent’s profile. The second part of the questionnaire assesses employee’s opinion about dynamic capability of the organization and comprises three subsections of sensing, seizing, and transforming. Sensing subsection evaluates respondent’s opinion about market stance and competitiveness of the organization, seizing subsection evaluates respondent’s opinion about knowledge acquisition and use of technological innovation within the organization, and transforming subsection evaluates respondent’s opinion about organizational readiness to change. The third part of the questionnaire inquires the respondent about the effectiveness of using digital technologies and their acceptance on organizational level. The consequent parts are designed to explore opinions about the stance of hotel supply chain resilience, agility, and traceability based on the ideas and observations earlier specified in a literature review section. Overall, participants were asked to answer 34 questions by indicating their level of agreement and disagreement by 7-point Likert scale.

Data Collection

The data for the research was collected by surveying research participants online using the Freeonlinesurveys.com website. Research participants were targeted by a single criterion of being employed with either local or international hotel located in UAE and contacted using email, Facebook, LinkedIn, and follow-up calls were appropriate between September 9, 2020 and September 26, 2020. All research participants were notified about the experimental purpose of the research conducted by the students of Abu Dhabi University doing a course project on hotel supply chains. As a result of this inquiry, researchers were able to receive 42 responses out of 86 potential participants targeted.

Data Analysis

The data collected from the participants was analyzed through the application of a series of quantitative statistical techniques. First, the descriptive statistics was used to analyze profiles of respondents and describe the overall variable dynamics for the consequent parts of the questionnaire. Second, the ANOVA analysis was used to describe the relationships between chosen research variables. Finally, the reliability testing was performed to evaluate internal consistency of the data and its trustworthiness for the study.

Theoretical Framework

The guiding framework for the study was inspired by three studies devoted to hotel management trends in UAE. The study by Khan et al. (2017) explored the notion of smart tourism as a subset of smart city based on the case of Dubai, which supported the initial assumption of seeing digital orientation as an important factor in analyzing hotel supply chain effectiveness. The study by Zaidan et al. (2016), despite being focused on the societal implication rather than supply chains, included several important arguments about the need of dynamical change in hotel industry in UAE to maintain competitiveness and sustainability based on the new trends in destination management and customer orientation. Lastly, the study by Masiero et al. (2019) was inspirational in understanding how hotel supply chain effectiveness should be distinguished based on customer centricity and analytics, which has further inspired thinking of supply chain from the perspectives of resilience, agility, and traceability as separate variables. Based on the above, it was considered that it is logical to test the impact of dynamic capabilities and digital orientation on each supply chain component separately as shown in Figure 1.

Theoretical framework.
Figure 1. Theoretical framework.

Analysis of Results

Respondent Profile

As previously mentioned, it was possible to collect 42 responses from hotel employees across UAE for the future quantitative analysis summarized in Table 1. Employee locations included Al Raha Beach Hotel, Kempinski Hotel, The Ritz Carlton RAK, Rosewood Hotel, Emirates Palace, The Cove Rotana, Hyatt Place Dubai, W Dubai The Palm, Marriott International, St. Regis Abu Dhabi, Park Hyatt Hotel, Rixos Premium Saadiyat Island, Copthorne Hotel Sharjah, and other smaller resorts. The majority of respondents (41 out of 42 or 97.6%) are employed in local hotels. Similarly, the majority of respondents (38 individuals or 90.5%) work in 5-star hotels, with much smaller respondents (3 and 1 or 7.1% and 2.4%) are work in 4-star and 3-star hotels respectively. Common position titles include concierges, directors and general managers, guest relation supervisors, and talent coaches. Overall, the job seniority level is distributed among 15 individuals or 35.7% of respondents employed at lower management positions, 20 individuals or 47.6% of respondents employed at middle management positions, and 7 individuals or 16.7% of respondents employed at top management positions.

In terms of employee service period, the largest cohort is 22 individuals or 52.4% of respondents working in the organization for two or less years. They are followed by 11 individuals or 26.2% of respondents working in the organization for more than 5 years, and 9 individuals or 21.4% of respondents working less than 5 years. In terms of the workforce size, the half of all respondents indicated that the hotel employs less than 500 employees, followed by 17 individuals or 40.5% of respondents specifying that the hotel employs between 500 and 1000 employees, and 4 individuals or 9.5% of respondents indicating that the hotel employs more than 1000 employees. Finally, in terms of sales volumes as of 2019, 18 individuals or 42.9% of respondents reported having more than $5 million, 16 individuals or 38.1 of respondents reported having between $1 and $5 million, while 8 individuals or 19% of respondents reported less than $1 million of sales volume.

Table 1. Respondent profile.

Variables Items Frequency Percentage
Hotel Location Local 41 97.6
International 1 2.4
Hotel Status 5-star 38 90.5
4-star 3 7.1
3-star 1 2.4
Respondent’s service level Lower management 15 35.7
Middle management 20 47.6
Top management 7 16.7
Respondent’s service period Two or less years 22 52.4
Less than 5 years 9 21.4
More than 5 years 11 26.2
Total number of employees Less than 500 21 50.0
Between 500 and 1000 17 40.5
More than 1000 4 9.5
Approximate total sales volume in 2019 (in $ million) 0.5 to 1 (million) 8 19.0
1 to 5 (million) 16 38.1
More than 5 (million) 18 42.9

Descriptive Statistics

Research participants were asked to evaluate 34 statements based on the 7-point Likert scale, with extreme ‘1’ of ‘strongly disagree’ to extreme ‘7’ of ‘strongly agree’. All responses rated from 1 to 3 were considered as a general disagreement with the statement, the response of 4 was considered as a neutral opinion, whereas all responses rated from 5 to 7 were considered as general agreement with the statement. Overall, it was found that the majority of respondents (more than 90% per item) were positive about each of 34 statements, rating their opinions from 5 to 7.

Table 2 supports the above findings by demonstrating average values above 5 for all variables specified in the framework. The highest scores are observed for independent variables of dynamic capabilities, with a mean of 6.128, standard deviation of 0.759, and variance of 0.576, as well as digital orientation, with a mean of 6.107, standard deviation of 0.815, and variance of 0.761. However, the dependent variables show that the opinions about hotel supply chain effectiveness were less optimistic, where supply chain was reported with a mean of 5.899, standard deviation of 0.945, and variance 0.893, supply chain agility was reported with a mean of 6.040, standard deviation of 0.751, and variance of 0.565, while supply chain traceability was rereported with a mean of 5.958, standard deviation of 0.831, and variance of 0.690. Nevertheless, the overall skewness of the data with average scores higher than 5 and comparatively low values below 1 for both standard deviation and variance demonstrate that all participants positively agreed about the success of organizations they employed with. It implies that there is a positive organizational climate in researched hotels, while additional inquiry on employee judgment is needed.

Table 2. Descriptive statistics.

Item Mean Std Deviation Variance
Dynamic Capabilities 6.128 0.759 0.576
Sensing 6.167 0.821 0.675
Seizing 6.131 0.754 0.569
Transforming 6.087 0.872 0.761
Digital Orientation 6.107 0.815 0.664
Supply Chain Resilience 5.899 0.945 0.893
Supply Chain Agility 6.040 0.751 0.565
Supply Chain Traceability 5.958 0.831 0.690

Reliability Testing

To validate internal data consistency, a reliability testing was performed using Excel formulas. Creswell and Creswell (2017) explained that reliability should be measured by the Cronbach’s alpha coefficient, which is considers the number of items in a survey, the sum of variances per each response items, and the total variance per individual response. For the current study, the estimated reliability coefficient is 0.9689, which is a very good result since it is close to 1 and much higher than 0.7 as a minimum threshold to treat the survey results internally consistent (Creswell & Creswell, 2017). Hence, the data could be used for theoretical model exploration and analysis described in the next section.

Hypotheses Testing

To validate the theoretical framework assumptions, the single-factor analysis of variance (ANOVA) test was used. Overall, we test six hypotheses to evaluate the relationships of dynamic capability with three supply chain-related variables, and digital orientation with the same supply chain-related variables separately. The default significance level for hypotheses testing was set at 0.05. For the first hypothesis testing, we compare estimated mean scores for dynamic capabilities and supply chain resilience:

  • H0: There is no difference between dynamic capabilities and supply chain resilience in UAE hotels
  • H1: There is a significs.ant difference between dynamic capabilities and supply chain resilience in UAE hotels.

Table 3. Dynamic capabilities and supply chain resilience.

Sum of Squares df Mean Square F Significance
Between Groups 1.10286 1 1.10286 1.46585 0.22948
Within Groups 61.6946 82 0.75237
Total 62.7974 83

Since the significance level is more than 0.05, we reject the null hypothesis and conclude that there is a significant difference between numeric estimates for dynamic capabilities and supply chain resilience in UAE hotels.

For the second hypothesis testing, we compare estimated mean scores for dynamic capabilities and supply chain agility:

  • H0: There is no difference between dynamic capabilities and supply chain agility in UAE hotels.
  • H1: There is a significant difference between dynamic capabilities and supply chain agility in UAE hotels.

Table 4. Dynamic capabilities and supply chain agility.

Sum of Squares df Mean Square F Significance
Between Groups 0.16371 1 0.16371 0.28026 0.59796
Within Groups 47.8988 82 0.58413
Total 48.0625 83

Since the significance level is more than 0.05, we reject the null hypothesis and conclude that there is a significant difference between numeric estimates for dynamic capabilities and supply chain agility in UAE hotels.

For the third hypothesis testing, we compare estimated mean scores for dynamic capabilities and supply chain traceability:

  • H0: There is no difference between dynamic capabilities and supply chain traceability in UAE hotels.
  • H1: There is a significant difference between dynamic capabilities and supply chain traceability in UAE hotels.

Table 5. Dynamic capabilities and supply chain traceability.

Sum of Squares df Mean Square F Significance
Between Groups 0.60435 1 0.60435 0.93193 0.3372
Within Groups 53.1767 82 0.6485
Total 53.7811 83

Since the significance level is more than 0.05, we reject the null hypothesis and conclude that there is a significant difference between numeric estimates for dynamic capabilities and supply chain traceability in UAE hotels.

For the fourth hypothesis testing, we compare estimated mean scores for digital orientation and supply chain resilience:

  • H0: There is no difference between digital orientation and supply chain resilience in UAE hotels.
  • H1: There is a significant difference between digital orientation and supply chain resilience in UAE hotels.

Table 6. Digital orientation and supply chain resilience.

Sum of Squares df Mean Square F Significance
Between Groups 0.91146 1 0.91146 1.1428 0.2882
Within Groups 65.4003 82 0.79756
Total 66.3118 83

Since the significance level is more than 0.05, we reject the null hypothesis and conclude that there is a significant difference between numeric estimates for digital orientation and supply chain resilience in UAE hotels.

For the fifth hypothesis testing, we compare estimated mean scores for digital orientation and supply chain agility:

  • H0: There is no difference between digital orientation and supply chain agility in UAE hotels.
  • H1: There is a significant difference between digital orientation and supply chain agility in UAE hotels.

Table 7. Digital orientation and supply chain agility.

Sum of Squares df Mean Square F Significance
Between Groups 0.09557 1 0.09557 0.15186 0.69777
Within Groups 51.6045 82 0.62932
Total 51.7001 83

Since the significance level is more than 0.05, we reject the null hypothesis and conclude that there is a significant difference between numeric estimates for digital orientation and supply chain agility in UAE hotels.

Finally, for the sixth hypothesis testing, we compare estimated mean scores for digital orientation and supply chain traceability:

  • H0: There is no difference between digital orientation and supply chain traceability in UAE hotels.
  • H1: There is a significant difference between digital orientation and supply chain traceability in UAE hotels.

Table 8. Digital orientation and supply chain traceability.

Sum of Squares df Mean Square F Significance
Between Groups 0.46503 1 0.46503 0.67037 0.41529
Within Groups 56.8824 82 0.69369
Total 57.3475 83

Since the significance level is more than 0.05, we reject the null hypothesis and conclude that there is a significant difference between numeric estimates for digital orientation and supply chain traceability in UAE hotels.

Discussion and Conclusion

Findings of the study should be interpreted from two related perspectives based on the data analysis and present research on the subject. Referring to the descriptive results, the study suggests that overall hotel employees are confident about organizational stance and potential to satisfy increasing customer demands, as well as feel confident about the overall hotel supply chain effectiveness. With minor deviations, almost all respondents rate dynamic capabilities and digital orientation as highly developed components of the internal service model, while some express certain concerns about the supply chain effectiveness. Considerably, it is generally a good sign of organizational climate and future potential of hospitality industry in UAE during the crisis times that would support emerging trends in developing customer focus as a core competence.

Nevertheless, further statistical analysis has show that capability building and digital focus are not related to a common understanding of supply chain trends among hotel employees at all levels. Apparently, it might be explained by a remark of Khan et al. (2017) on the emerging trend of anticipating smart tourism trends that are yet to be grasped by the contemporary consumers. Alternatively, it could be seen as a consequence of travel restrictions and biased demonstrated by respondents in terms of cultural match, where the loss of profits does not create a positive attitude to reflect on the business benefits or losses sincerely. Hence, it is tentatively concluded that the study might be enhanced with using psychological factors to evaluate overall job attitude to sense employee behaviors more specifically.

Research Implications

Hotels could benefit from the research findings in a following manner. First, a proactive engagement in employee sensing is required to understand their readiness and preparedness to organizational transformation, since pure optimism during crisis times might lead to future shocks and burnouts. Second, hotels should think about ways to integrate capabilities and digital focus more effectively; for instance, by providing cheaper hotel services for local citizens who are allowed to visit hotels despite travel bans. Finally, hotels should seek for the partnership with a government that committed to support tourism industry as a driver of economic growth, further developing resort networks that would be ready to welcome new visitors after the travel bans are lifted.

Research Limitations

From the critical perspective, it is also important to admit certain gaps and limitations of the study. First, the basis for developing research framework was generalized because of the lack of similar studies in UAE context; therefore, main findings are tied to the international rather than local perspectives. Second, in terms of quantitative research requirements, data validity is best achieved when there are more than 100 of responses, whereas current study has only 42 (Creswell & Creswell, 2017). It means that current findings could be somewhat biased and require further investigation based on the more detailed analysis of factors that would help to improve overall efficiency of the hotel supply chains in UAE.

References

Brown, N.A., Rovins, J.E., Feldmann-Jensen, S., Orchiston, C., & Johnston, D. (2017). Exploring disaster resilience within the hotel sector: A systematic review of literature. International Journal of Disaster Risk Reduction, 22, 362-370. Web.

Buhalis, D., & Leung, R. (2018). Smart hospitality – interconnectivity and interoperability towards and ecosystem. International Journal of Hospitality Management, 71, 41-50. Web.

Creswell, J.W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. SAGE Publications.

Gretzel, U., Sigala, M., Xiang, Z., & Koo, C. (2015). Smart tourism: Foundations and developments. Electronic Markets, 25(3), 179-188. Web.

Jasinskas, E., Streimikiene, D., Svagzdiene, B., & Simanavicius, A. (2016). Impact of hotel service quality on the loyalty of customers. Economic research-Ekonomska istraživanja, 29(1), 559-572. Web.

Khan, M.S., Woo, M., Nam, K., & Chathoth, P.K. (2017). Smart city and smart tourism: a case of Dubai, Sustainability, 9(12). Web.

Lam, C., & Law, R. (2019). Readiness of upscale and luxury-branded hotels for digital transformation. International Journal of Hospitality Management, 79, 60-69. Web.

Masiero, L., Yang, Y., & Qiu, R.T.R. (2019). Understanding hotel location preference of customers: Comparing random utility and random regret decision rules. Tourism Management, 73, 83-93. Web.

Moon, H., & Sprott, D.E. (2016). Ingredient branding for a luxury brand: the role of brand and product fit. Journal of Business Research, 69(12), 5768-5774. Web.

Samdantsoodol, A., Cang, S., Yu, H., Eardley, A., & Buyantsogt, A. (2017). Predicting the relationships between virtual enterprises and agility in supply chains. Expert Systems with Applications, 84, 58-73. Web.

Zaidan, E., Taillon, J., & Lee, S. (2016). Societal implication of UAE tourism development. Anatolia: An International Journal of Tourism and Hospitality Research, 27(4), 543-545. Web.

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