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computer interaction. The data analytics maturity model can be used to evaluate the current stage of any hotel and adapt accordingly. This framework will take the industry to the next level of the digital revolution where real-time information simulates immediate alternate decision paths for ideal results. In spite of the fact that the framework is adaptable, it would be expensive for SME.
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provide their email address and, due to confidentiality agreement, the identity is not revealed.
Further details of the survey areas explored below.
All participants work for the hospitality industry. All participants are directly or indirectly involved in procurement processes except one. These details are important as the proposed integrated system is intended for the procurement system in the hospitality industry. The opinions of participants from this industry can be considered more valid than any other industry.
The company size varied for each participant. Half of the respondents work in large enterprises, which have more than 600 employees. The company size matters in adoption and perception towards a new system as it may not be cost-effective to invest in enormous changes for small medium enterprises. Participant responses to the company size question are shown in Figure 35. Majority of the respondents work in large enterprises.
Fig. 35: Response from participants for question 4
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Section two had four questions, each one evaluating the importance of additionally added agents in the AI integrated system. In the survey, each agent with its definition was given to the participants. Moreover, the researcher’s telephone number and email address were also provided so that respondents could seek clarification if necessary. These questions collected the participants’ inputs on the importance and a textbox to write their comments were also provided. Each of the obtained results is shown below.
Authorisation and security agents received an average of 5 ratings out of 5 in the survey.
Ninety-seven percent of the participants said the feature is very important, as depicted in Figure 36. This shows how important it is to have such an agent. The participants’ comments include that this agent will have a financial impact and it is important to have high security and ensure privacy for users in the procurement process.
Fig. 36: Response from participants for question 5
The catalogue agent received an average of 4.8 ratings out of 5. Eighty-six percent of the participants said this feature is very important, as shown in Figure 37. Participants find it useful and important. The participants’ comments include that the catalogue agent will ensure
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that they are ordering the correct product from the right supplier and notifying the concerned parties, which saves a lot of time.
Fig. 37: Response from participants for question 6
The risk assessment agent received an average of 4.9 ratings out of 5 from the survey. Ninety- five percent of the participants said this feature is very important, as displayed in Figure 38.
The participants’ comments include monitoring forex changes and ensuring the company’s interests are protected; moreover, risk management has always been an issue. Therefore, it is always beneficial to have an agent to monitor these elements.
Fig. 38: Response from participants for question 7
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The optimisation agent received an average of 4.8 ratings out of 5 from participants. Ninety percent of participants said this feature is very important, as depicted in Figure 39. The participants’ comments include that this element is a great addition that helps in making optimised decisions and is also beneficial as it may reduce the time taken for one workflow.
Fig. 39: Response from participants for question 8
The next two questions were to presented to acquire an understanding of the participants’
expertise in AI and if they would recommend the proposed integrated system to their top management. These questions are important because the researcher is keen to know the opinion that they have provided is based on their AI knowledge or if it is purely experience- based. It is also important to know if they are willing to adopt such a system which can become a motivational factor for the system development team.
The average expertise of participants was 45%, as shown in Figure 40. This implies the participants’ expertise is less than average. It is interesting to notice that even if their AI
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expertise level is not high, they all found these agents to be beneficial for their day-to-day work.
Fig. 40: Response from participants for question 9
Eighty-eight percent of the participants confirmed that they will definitely recommend this AI integrated system to their company, as shown in Figure 41. It is interesting to note that although the participants are not experts in AI, they still would recommend the integrated system to their top management.
Fig. 41: Response from participants for question 10
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The survey results show that the added agents are beneficial and add value to the business process as per the participant’s opinion. The extreme responses maybe because none of the participants are experts in this field. This again shows that there is a need for skilled personnel in the selected organisation.