Analysing the Adoption of Intelligent Agent Technology in Food Supply Chain Management: An Empirical Evidence

Abstract

This article aims to identify and analyse the factors that impact the adoption of intelligent agent technology (IAT) in the food supply chain (FSC). The research was conducted based on 329 respondents from various hotels and the theoretical framework adopted in this study, that is, technological, organizational and environmental (TOE) framework. The findings indicated that multiple factors in TOE contribute significantly to the adoption of IAT. We have validated the proposed framework by structural equation modelling utilizing AMOS 22.0. This research offers a new and vital paradigm for adopting this innovation in the FSC, thereby increasing the overall efficiency of a hotel. The proposed TOE framework has identified several factors like relative advantage, reliability, complexity, cost, innovation adoption, top management support, skilled employees, IT awareness, environmental uncertainty, competitive pressure, information intensity and supplier’s pressure, which helps in the adoption process of IAT in the FSC. It also provides a foundation for future research and significant insights to adopt this new technology in the hotel industry.

Keywords

Technological-organizational-environmental intelligent agent technology structural equation modelling food supply chain

Introduction

Implementation of information technology (IT) in the food supply chain (FSC) will help the industry reduce its operational price and add an advantage. The current technologies used in the FSC, like radio frequency identification (RFID), electronic resource planning (ERP) and electronic data interchange (EDI), has many drawbacks; as a result, managers main focus is to adopt the latest innovative technology (Alsetoohy et al., 2019). Artificial intelligence (AI) can be specified as the science and engineering of creating intelligent machines, showcasing knowledge, reasoning, learning, planning, perception, communication and moving and manipulating items.

Intelligent agent technology (IAT) is helping to transform the hospitality industry. One can draw many instances from the global travel brands; for example, Uber, Marriot and Hilton utilize AI-based solutions to understand customer behaviour, such as client preferences, journey patterns, travel choices and payment methods. By all these observations, they boost revenues and clients’ experiences. For instance, many hotels worldwide use AI applications in various areas of their operations (Alsetoohy & Ayoun, 2018).

According to Noodle.ai CEO Stephen Pratt, ‘there is a need to find out the gaps that exist in the hotel’s operational efficiencies, such as inventory management’. Many hotels use IAT to automate, integrate, coordinate and make virtualization and visualization Food and Beverage operations, which help reduce cost and increase profit. Characteristics of IAT have many resemblances with the FSC process that drives extensive possible use in hotels. IAT is treated as a suitable technology to restructure the FSC because of IAT characteristics such as anxiety, collaboration, autonomy and intelligence for the FSC (Di Vaio et al., 2020). Therefore, the implementation of IAT in FSC may lead to various benefits such as integration of enterprises, information sharing, dynamic conﬁguration and joint operation. There will be a reduction in manual intervention, lead-time, variability, increased repeatability, reliability (R) and pace of response to factors (Di Vaio et al., 2020). IAT utilization in the FSC on both food acquisition practices and execution is a positive effect.

This study will help the hotel authorities for taking decisions for the adoption of this advanced technology. Several researchers have researched hospitality IT developments and IAT. However, there were few research studies conducted by using IAT adoption in FSC. Hence, in this study, we have explored the use of IAT in FSC and identified the factors which will implement the adoption process based on technological, organizational and environmental (TOE) perspectives and the hotel administrators’ approach towards utilization of IAT in FSC; and ﬁnally, the relationship between hotel manager approach towards selection of IAT in FSC and their expectation for future acceptance. The article measures the intention of IAT adoption in the hotel FSC. Previous studies measured the intention to adopt IAT (Alsetoohy et al., 2019), AI and robotics in the hotels of Dubai (Nam et al., 2020) and cloud computing in the healthcare supply chain (SC) (Baral & Verma, 2021). Drastic developments in technology, change in the mindset of the customers and competition existing in the hotel industry forcing the luxury hotels to adopt the latest innovative technology in the process. Hence, this study has been conducted in developing nations’ context, which supports adopting the latest and innovative technologies. It will help in increasing the efficiency of the FSC.

Further, the article is discussed in the following section. The second section describes the literature review and the advancement of the hypothesis. The third section discusses the research methodology for the study. The fourth section gives the result and discussion. The fifth section has an implication for the study. The sixth section provides the conclusion of the study.

Literature Review and Advancement of Hypothesis

Intelligent Agents Technology in the Food Supply Chain

Intelligent agents can make decisions or perform a service based on its environment, experiences and user input. Its primary function is to provide a better use for the user and manage and interact with a computer-based application (Lai & Hung, 2018). IAT is used for real-time pricing strategies in an SC network (Mangina & Vlachos, 2005). Jafari (2002) defined an agent as a software tool linked to other applications or databases running on one or more computers. The intelligent agent’s primary function is to manage, use and interact with computer applications (Yang et al., 2021). IAT gathers information or performs some tasks without any human intervention. The difference between IAT and traditional software is autonomous, adaptive, personalized and proactive (Shirazi & Soroor, 2007).

IAT’s features are to re-engineering FSC, which includes operating without any human interventions, that is, automatic; interaction with other humans, that is, social interaction; reacting promptly to environmental changes, that is, responsiveness; proactiveness and mobility. Advances in IT and the rise in competition in the food industry have forced them to go into structural and organizational changes. It will help to overcome inefficiencies and remaining competitive (Samara et al., 2020). Also, due to the internet, FSC functions have built scopes for incorporating data into the choice-building process along with the SC partners (Jalilvand et al., 2019; Samara et al., 2020). IAT will overcome the inefficiencies of poor information flow in FSC (Mangina & Vlachos, 2005). IAT will share real-time information and decision-making, which will create an effective and efficient response system for the FSC. There will be an improved lead-time, manual intervention, delivery performance, variability and repeatability (Barton & Thomas, 2009).

IAT will improve food tracing systems and implement autonomous operations (Zhang & Xie, 2006). IAT will help search the suppliers automatically and then order the products (Shirazi & Soroor, 2007). IAT could perform better coordination in the procurement decisions of FSC (Sujatha, 2011). Grey et al. (2005) suggested that there will be cost reduction, improvement in sharing of the information, increased resource allocation efficiency and the ability to handle risk management with IAT usage.

The challenges which are faced by the hotels currently due to the use of existing technologies (e.g., EDI, ERP, RFID) can switch to IAT, which will undoubtedly help in overcoming the inefficiencies of the FSC because of insufficient flow of information and also adding flexibility and visibility to the FSC (Kittipanyangam & Tan, 2020). IAT has applications in service and manufacturing industries, for instance, enterprise integration and manufacturing planning, scheduling and stock control. IAT has been responsible for performing food procurement practices (Kim et al., 2021). Due to the properties of IAT, there is an exponential increase in opportunities in e-business.

Theoretical Foundation

Technological-Organizational-Environmental Framework

Tornatzky et al. (1990) stated the acceptance of innovation consists of three dimensions technological-organizational-environmental, which is popularly known as the TOE framework. Individual adoption research deals with the behaviour and intention of a particular person working in a firm to adopt the latest technology, on the other hand, organizational adoption research analyses the factors that lead to the adoption of the latest technology by the companies (Hiran & Henten, 2020; Rogers, 1995; Skafi et al., 2020). These perspectives influence technology adoption in the organization’s decision-making. The technological perspective includes compatibility, complexity (COMP) and relative advantage (RA) (Cruz-Jesus et al., 2019). As per Tornatzky et al. (1990), the organization’s perspective has ‘ﬁrm size and capabilities; the centralization, formalization, COMP of its managerial structure and the quality of its human resource’. The environmental context comprises the organization’s environment, such as the industry’s structure, competitive advantage and government support (Tornatzky & Klein, 1982).

Technological Factors (TF)

Relative Advantage (RA): The degree to which the adopters prefer new technology more than the traditional or presently used technologies (Gangwar et al., 2015). It optimizes cost, time, economic profitability and increases operational excellence (Makena, 2013). It also improves client satisfaction and decreases data entry (Shee et al., 2018). Previous studies had used RA (Alsetoohy et al., 2019; Badi et al., 2021; Baral & Verma, 2021; Chen et al., 2021; Nam et al., 2020; Pillai & Sivathanu, 2020). Hence, the hypothesis proposed as follows:

H1: RA influences the adoption of IAT in the FSC.

Reliability (R): It refers to the ability of the system to function accurately, which leads to significant satisfactory service quality, low errors and faster recovery rates (Alkhater et al., 2018; Siddik et al., 2021). It is a critical element for adopting this technology. IAT can reveal product prices, check lead times, track order status, generate statistical reports for upper management and make purchasing decisions without the need for human intervention. One of the most critical components is the strategic partnership in the whole SC process, which leads to the essential sharing of information. ‘Reliability’ has been discussed in many previous studies (Alharbi et al., 2016; Alkhater et al., 2018; Alshamaila et al., 2013; Ooi et al., 2018). So, the proposed hypothesis is as follows:

H2: R influences the adoption of IAT in the FSC.

Complexity (COMP): COMP is an innovation that is relatively difficult to utilize and understand (Kumar & Krishnamoorthy, 2020). Due to a lack of skills and knowledge (Senyo et al., 2016). There is a need to combine the existing hotels’ IT infrastructure and FSC database and connect with the vendors (Wong et al., 2020). Various researchers have used this component in their studies (Ahmadi et al., 2017; Alsetoohy et al., 2019; Badi et al., 2021; Chen et al., 2021; Gökalp et al., 2020). As a result, the following hypothesis can be proposed:

H3: COMP influences the adoption of IAT in the FSC.

Cost (C): It refers to the expense for adopting and implementing the new advanced technologies, which are required for firms’ restructuring and re-engineering of the methods of FSC along with costs for other supporting hardware, installation, integration, consultancy support (Stjepić et al., 2021). Hence, the costs have been identified as a critical element for adopting IAT in the FSC. Previous studies (Ahmadi et al., 2017; Alsetoohy et al., 2019; Johnson & Diman, 2017; Oliveira et al., 2014) elaborated cost as a critical element. As a result, the hypothesis that has been proposed is as follows:

H4: C influences the adoption of IAT in the FSC.

Organizational Factors (OF)

Innovation Adoption (IA): IA refers to firms’ technological and financial capabilities (Lin, 2014). There are factors like technological readiness and financial readiness. Financial readiness means how much an organization can spend on its latest technology adoption (Tashkandi & Al-Jabri, 2015). Meanwhile, technological readiness means how an organization can adopt the latest technology and go against the change resistance (Kumar & Krishnamoorthy, 2020). Previous studies (Alsetoohy et al., 2019; Pillai & Sivathanu, 2020; Priyadarshinee et al., 2017) talked about this factor. As a result, the hypothesis that has been proposed is as follows:

H5: IA influences the adoption of IAT in the FSC.

Top Management Support (TMS): TMS help in adopting the latest technology by motivating and supporting the employees. They also provide the money for the implementation of the technology. They also create an organizational strategy for implementing the latest technology (Abed, 2020; Alharbi et al., 2016). Previous studies (Alharbi et al., 2017; Alsetoohy et al., 2019; Badi et al., 2021; Johnson & Diman, 2017; Senyo et al., 2016; Stjepić et al., 2021) had used TMS and found it to be an essential component. As a result, the proposed hypothesis is as follows:

H6: TMS influences the adoption of IAT in the FSC.

IT Awareness (ITA): The staff or manager needs to understand new and advanced IT, which will depict what technology the firm will be adopting (Low et al., 2011). ITA is considered an essential driver for IAT adoption for SC operations, considering IT resources’ human component (Priyadarshinee et al., 2017). Most luxury hotels are being worked through worldwide administration organizations that execute state-of-the-art innovation to oversee the FSC. This will be beneficial for awareness of new technologies of the hotel managers and accelerate the IAT adoption in the FSC. Previous studies (Alsetoohy et al., 2019; Priyadarshinee et al., 2017) had identified ITA. So, the proposed hypothesis is as follows:

H7: ITA influences the adoption of IAT in the FSC.

Skilled Employees (SE): It is an essential factor that will impact the IAT adoption process. The employees need to have IT skills, knowledge and a positive attitude towards the IAT adoption. When the staff lacks a basic understanding of IT, they will not change the current working style, creating a conflict with top management (Narmetta & Krishnan, 2020). Previous studies (Alharbi et al., 2017; Alsetoohy et al., 2019; Johnson & Diman, 2017) discussed SE as an essential component. Hence, the hypothesis can be proposed as follows:

H8: SE influences the adoption of IAT in the FSC.

Environmental Factors (EF)

Environmental Uncertainty (EU): It refers to various environmental issues related to technologies, competitors, suppliers and customers (Hiran & Henten, 2020). There has been a positive relationship between the company’s structure and innovation because uncertainty leads to the unavailability of information (Latan et al., 2018). There is a requirement of information accuracy to respond as conditions necessitate. Experts uncovered that organizations could oversee vulnerabilities in the business climate when they moved up to refined innovation (Pateli et al., 2020). Previous studies had used EUs (Alsetoohy et al., 2019; Senyo et al., 2016). As a result, the proposed hypothesis is as follows:

H9: EF influence the adoption of IAT in the FSC.

Competitive Pressure (CP): CP means the firms’ pressure from their next competitors in the market in the same industry. It is defined as the extent of the competitive atmosphere within the industry in which the companies operate. This pressure, in turn, helps firms accept the latest technologies (Tashkandi & Al-Jabri, 2015). It has its effect on a firm’s incentives to undertake product and process innovations. Previous studies (Alsetoohy et al., 2019; Amron et al., 2019; Badi et al., 2021; Chen et al., 2021; Stjepić et al., 2021) had talked regarding this component. As a result, the hypothesis that has been proposed is as follows:

H10: CP influences the adoption of IAT in the FSC.

Information Intensity (II): It means the shared information present in the firm’s products or services (Alshamaila et al., 2013). II products are more complex than other products, and more information is there to identify their attributes (Alkhater et al., 2018; Priyadarshinee et al., 2017). The firms related to the service sector are likely to contain more information in their product and services than other manufacturing products (Latan et al., 2018). Previous studies had used II (Alsetoohy et al., 2019; Priyadarshinee et al., 2017). So, the proposed hypothesis is as follows:

H11: II influences the adoption of IAT in the FSC.

Supplier’s Pressure (SP): To maximize the benefits among the enterprises, SC management technologies should be adopted by the multiple firms involved (Narmetta & Krishnan, 2020; Stjepić et al., 2021). A trading partner should use e-business applications only when many trading firms utilize them for business purposes. Demands from prevailing accomplices are a significant factor in another innovation reception, and associations may receive the innovation to demonstrate their quality as a firm trading partner (Kamble et al., 2019). Hence, hotels need to preserve a beneficial supplier–buyer relationship if the supplier is dominant and has adopted or aims to adopt IAT. Previous studies (Alsetoohy et al., 2019; Chen et al., 2021; Kouhizadeh et al., 2021; Priyadarshinee et al., 2017) had identified SP as a critical component. Hence the hypothesis can be proposed as follows:

H12: SP influences the adoption of IAT in the FSC.

Research Methodology

The responses are collected using primary and secondary sources. Secondary sources include a literature review and other reports, and the primary source consists of a collection of data through a structured questionnaire. The questionnaire method was previously used in many studies (Aslam et al., 2021; Baral & Verma, 2021; Wong et al., 2020). The latent variable indicators were adopted from previous studies in different segments mentioned in the tabular form in the appendices. All the latent variables were measured using three to seven statements (indicators) for confirmation during the survey. The components were assessed utilizing a seven-point Likert scale, that is, ‘strongly disagree = 1’ to ‘strongly agree = 7’. Seven-point Likert scale had been used in previous studies (Baral & Verma, 2021; Wong et al., 2020). Structural equation modelling (SEM) has been utilized in this analysis, and for this 200 and above sample size is considered adequate (Gerbing & Anderson, 1988; Hair et al., 2012; Kline, 2012).

The questionnaire was developed in the English language. An online survey is used to collect data as it saves time and expenses required for travelling (Kamble et al., 2019). The responses from the target population are collected through emails. This research’s responses were taken from employees who make IT decisions for luxury hotels in India. It was found that the clients of luxury hotels were less price-sensitive. Also, these hotels’ staff was considered highly technology-oriented because of their higher financial capability. A simple random sampling method has been used to generalize the results more appropriately and allow the presence of various firms (Hair et al., 2010). The Cronbach alpha values for the measurement items were found to be reliable and valid, with values higher than 0.65 (Nunnally, 1994). We sent the questionnaires to 650 respondents in the final survey, including IT managers, procurement managers and top executives in five-star hotels. However, we have received only 427 responses returned, with a response rate of 65.69 per cent. After data screening, we found only 329 valid responses which can be used for the analysis. To check the biasness of the collected data, Exploratory factor analysis (EFA) was performed, and the results show that the first factor explains maximum covariance (9.677%), which is below the recommended value of 50 per cent (Podsakoff, 2003).

Demographics of Respondents

Table 1 shows the demographics of the respondents. In total, 54.10 per cent of the respondents who participated in the survey were male and 45.90 per cent were female. In total, 48.94 per cent of the respondents who participated in the survey were IT managers, 29.18 per cent of the respondents who participated were procurement managers and 21.88 per cent were top executives. The age of the respondents were 18–25 years (12.77%), 26–35 years (34.04%), 36–45 years (29.48%) and above 46 years (23.71%). In total, 53.50 per cent of the respondents who participated in the survey were having an educational degree of MSC/MA/MBA/M.tech/ME, 40.73 per cent of the respondents who participated in the survey are having educational degree BSC/BA/BBA/B.tech/BE and 5.78 per cent of the respondents participated in the survey are having educational degree PhD. In total, 57.45 per cent of the respondents who participated in the survey are having industry experience between 6 to 10 years, 27.05 per cent of the respondents who participated in the survey have industry experience between 0 and 5 years and 15.50 per cent of the respondents who participated in the survey are having industry experience above 10 years.

Table 1.

Demographics Characteristics

Item	Frequency	Percentage
Gender
Male	178	54.10%
Female	151	45.90%
Participants’ position
IT managers	161	48.94%
Procurement managers	96	29.18%
Top executives	72	21.88%
Age of the respondents
18–25 years	42	12.77%
26–35 years	112	34.04%
36–45 years	97	29.48%
Above 46 years	78	23.71%
Educational degree of the respondents
BSC/BA/BBA/B.tech/BE	134	40.73%
MSC/MA/MBA/M.tech/ME	176	53.50%
PhD	19	5.78%
Industry experience of the respondents
0–5 years	89	27.05%
6–10 years	189	57.45%
Above 10 years	51	15.50%

Source: Author’s own (output obtained from survey done).

Results and Discussion

Reliability and Validity

Cronbach’s Alpha

Assessment of R helps examine the degree of internal consistency between variable measurement items and its freedom of error at any point in time (Kline, 2015). Table 2 shows Cronbach’s alpha values for all variables. It is used to measure the R of the data (Hair et al., 2012). The values should be greater than 0.70, that is, the recommended level (Nunnally, 1994).

Table 2.

Cronbach Alpha, Factor Loadings

Variables	Items	Cronbach’s Alpha	Factor Loading
Top management support	Top management support 1	0.937	.926
	Top management support 2		.948
	Top management support 3		.922
	Top management support 4		.860
Supplier’s pressure	Supplier’s pressure 1	0.863	.808
	Supplier’s pressure 2		.852
	Supplier’s pressure 3		.866
	Supplier’s pressure 4		.717
Relative advantage	Relative advantage 1	0.866	.856
	Relative advantage 2		.873
	Relative advantage 3		.895
	Relative advantage 4		.716
Cost	Cost 1	0.849	.800
	Cost 2		.811
	Cost 3		.774
	Cost 4		.855
IT awareness	IT awareness 1	0.918	.872
	IT awareness 2		.924
	IT awareness 3		.939
Skilled employees	Skilled employees 1	0.902	.941
	Skilled employees 2		.940
	Skilled employees 3		.820
Information intensity	Information intensity 1	0.889	.935
	Information intensity 2		.924
	Information intensity 3		.845
Complexity	Complexity 1	0.846	.817
	Complexity 2		.897
	Complexity 3		.786
Innovation adoption	Innovation adoption 1	0.833	.824
	Innovation adoption 2		.839
	Innovation adoption 3		.827
Competitive pressure	Competitive pressure 1	0.796	.835
	Competitive pressure 2		.877
	Competitive pressure 3		.790
Environmental uncertainty	Environmental uncertainty 1	0.744	.787
	Environmental uncertainty 2		.850
	Environmental uncertainty 3		.799
Reliability	Reliability 1	0.733	.804
	Reliability 2		.823
	Reliability 3		.685

Source: Author’s own (output obtained from SPSS 20.0. authors’ results).

Table 3.

Model Fit Measures for the Confirmatory Factor Analysis for the Factors

Goodness-of-Fit Indices	Default Model for the CFA Model	Accepted Parameters
Absolute goodness-of-fit measure
χ²/df (CMIN/DF)	1.203	Lower Limit:1.0; Upper Limit 2.0/3.0 or 5.0
GFI	0.949	>0.90
Incremental fit measure
CFI	0.982	>0.90
IFI	0.982	>0.90
TLI	0.979	>0.90
Parsimony fit measure
PCFI	0.848	>0.50
PNFI	0.78	>0.50
Absolute badness of fit measure
RMSEA	0.025	within 0.08

Source: Author’s own (output obtained from AMOS 22.0. authors’ results).

Exploratory Factor Analysis (EFA)

The second step is to perform EFA using SPSS 20.0. Kaiser-Meyer-Olkin (KMO) value for the current study is 0.742, greater than the 0.60 minimum level (Hair et al., 2010). The extraction method is used to group the components. All the components are being grouped into twelve components using the principal component analysis method. For the component 1 extracted total variance is 11.262 per cent, followed by component 2 extracted 10.533 per cent, component 3 extracted 8.747 per cent, component 4 extracted 7.723 per cent, component 5 extracted 6.464 per cent, component 6 extracted 5.910 per cent, component 7 extracted 5.725 per cent, component 8 extracted 5.314 per cent, component 9 extracted 4.465 per cent, component 10 extracted 3.657 per cent, component 11 extracted 3.392 per cent and component 12 extracted 3.097 per cent. So, the total variance explained by all the 12 components is 76.198 per cent. The next step is to perform the rotated component matrix, which helps group the items in a particular group. The method used is the varimax rotation method. Table 2 shows the factor loading for the rotated component matrix.

Confirmatory Factor Analysis (CFA)

In this step, CFA is being measured. A model is developed using AMOS 22.0. The developed model has 12 latent variables. Three parameters are measured composite reliability (CR), convergent validity and discriminant validity. The developed model has only independent variables. All the parameters are within the threshold level (Byrne, 2010). The goodness of fit indices was χ² = 811.087 with df =674, RMSEA = 0.025, IFI = 0.982, CFI = 0.982, TLI = 0.979 and GFI = 0.949, which were within the threshold values (Hair et al., 2010).

Construct Validity (CV)

A significant logical idea to assess the validity of a measure to develop a CV. CV is the degree to which a test quantifies the idea or development that it is expected to quantify. CV is generally tried by estimating the relationship in appraisals got from a few scales. There is no cut-off that characterizes CV (DeVellis et al., 2003).

Composite Reliability (CR)

CR was also measured for all the components. It is calculated for internal consistency reliability because of its ability to provide better results (Henseler et al., 2009). The CR of the constructs should be greater than 0.70, which is the accepted threshold level (Hair et al., 2014). Table 4 shows the values of CR.

Table 4.

Composite Reliability and Average Variance Extracted for the Factors

Variables	Composite Reliability	Average Variance Extracted
Relative advantage	0.904	0.835
Reliability	0.815	0.770
Complexity	0.873	0.833
Cost	0.884	0.810
Innovation adoption	0.869	0.830
Top management support	0.800	0.914
IT awareness	0.937	0.912
Skilled employees	0.929	0.900
Environmental uncertainty	0.854	0.812
Competitive pressure	0.873	0.834
Supplier’s pressure	0.886	0.811
Information intensity	0.929	0.901

Source: Author’s own (output obtained from AMOS 22.0. authors’ results).

Convergent Validity

Convergent validity exists when the theoretical construct developed for the study has a strong correlation with the items used to test it. In other words, the variance of the indicators of a given structure must be highly shared. It is measured with the help of the average variance extracted (AVE). AVE for the constructs must not be greater than 0.50, which is the accepted threshold level (Fornell & Larcker, 1981). Table 4 shows the values of AVE for the factors.

Divergent or Discriminant Validity

Discriminant validity investigates how distinct the constructs in a proposed model are from one another. To assess discriminant validity, the square roots of the AVEs were compared to the correlation for each construct. The selected construct’s square root AVE should be greater than the correlations between the specific construct and all the other constructs in the model (Fornell & Larcker, 1981). As the within-construct variance exceeded the between-construct variance, discriminant validity was supported. As a result, divergent or discriminant validity is satisfied. Table 5 shows the matrix of discriminant validity for the technological factors.

Table 5.

Discriminant Validity for the Factors

Variance Extracted between Factors
	TMS	SP	RA	C	ITA	SE	II	COMP	IA	CP	EU	R
TMS	1
SP	0.746	1
RA	0.766	0.677	1
C	0.746	0.657	0.677	1
ITA	0.833	0.745	0.764	0.744	1
SE	0.823	0.734	0.754	0.650	0.663	1
II	0.824	0.735	0.755	0.734	0.822	0.811	1
COMP	0.765	0.676	0.696	0.675	0.763	0.752	0.753	1
IA	0.762	0.673	0.693	0.673	0.760	0.749	0.750	0.691	1
CP	0.765	0.677	0.696	0.676	0.764	0.753	0.754	0.695	0.692	1
EU	0.748	0.659	0.678	0.658	0.746	0.735	0.736	0.677	0.674	0.677	1
R	0.714	0.625	0.645	0.625	0.712	0.701	0.702	0.643	0.641	0.644	0.626	1

Source: Author’s own (output obtained from AMOS 22.0. authors’ results).

Note: RA = relative advantage; R = reliability; COMP = complexity; C = cost; IA = innovation adoption; TMS = top management support; SE = skilled employees; ITA = IT awareness; EU = environmental uncertainty; CP = competitive pressure; II = information intensity; SP = supplier’s pressure.

Structural Model and Testing of Hypothesis

For testing, the hypothesis SEM is performed using AMOS 22.0. Figure 1 represents the structural model for the IAT adoption model for the factors. All the model fit parameters are shown in Table 6, which satisfies all the threshold levels (Byrne, 2010). The goodness of fit indices was χ² = 1,363.244 with df =890, RMSEA = 0.04, IFI = 0.944, CFI = 0.943, TLI = 0.940 and GFI = 0.921, which were within the threshold values suggested by Hair et al. (2010). For all the constructs in our model, the fit indices are acceptable.

Figure 1.

Source: Author’s own (output obtained from AMOS 22.0. authors’ results).

Table 6.

Model Fit Parameters of the Structural Model for the Factors

Goodness-of-Fit Indices	Default Model for the Factors	Benchmark
Absolute goodness-of-fit measure
χ²/df (CMIN/DF)	1.532	Lower limit:1.0; Upper limit 2.0/3.0 or 5.0
GFI	0.921	>0.90
Incremental fit measure
CFI	0.943	>0.90
IFI	0.944	>0.90
TLI	0.940	>0.90
Parsimony fit measure
PCFI	0.887	>0.50
PNFI	0.803	>0.50
Absolute badness of fit measure
RMSEA	0.04	within 0.08

Source: Author’s own (output obtained from AMOS 22.0. authors’ results).

Table 7 shows the path analysis result of the structural model for the factors. The result demonstrates that all the 12 hypotheses are being accepted. In total, 12 hypotheses support the p-value (Hair et al., 2012). The loadings of the estimates are within the range of 0.50 (Byrne, 2010). The values of the standard errors are in the range of –2.5 to +2.5. As per Hair et al. (2013), the values of critical ratios are greater than 1.96. Hence, it can be stated that the 12 factors have a positive impact on IAT. The structural model explains 52.5 per cent of IAT variance for the factors.

Table 7.

Path Analysis Result of the Structural Model for the Factors

	Estimate	Standard Errors	Critical Ratios	P	Hypothesis
Intelligent agent technology <--- Relative advantage	0.128	0.063	2.03	.00	Supported
Intelligent agent technology <--- Reliability	0.313	0.068	4.60	.00	Supported
Intelligent agent technology <--- Complexity	0.186	0.041	4.54	.00	Supported
Intelligent agent technology <--- Cost	0.517	0.062	8.34	.00	Supported
Intelligent agent technology <--- Innovation adoption	0.163	0.067	2.43	.00	Supported
Intelligent agent technology <--- Top management support	0.159	0.047	3.38	.00	Supported
Intelligent agent technology <--- IT awareness	0.400	0.048	8.33	.00	Supported
Intelligent agent technology <--- Skilled employees	0.294	0.07	4.20	.00	Supported
Intelligent agent technology <--- Environmental uncertainty	0.178	0.09	1.98	.00	Supported
Intelligent agent technology <--- Competitive pressure	0.111	0.047	2.36	.00	Supported
Intelligent agent technology <--- Information intensity	0.431	0.066	6.53	.00	Supported
Intelligent agent technology <--- Supplier’s pressure	0.609	0.078	7.81	.00	Supported

Source: Author’s own (output obtained from AMOS 22.0. authors’ results).

H1 tested the influence of RA on IAT in the FSC. The final structural model showed that RA and IAT in the FSC were supported (β = .128, p = .000). Previous studies on AI for talent acquisition in IT firms supported this study (Pillai & Sivathanu, 2020). Nam et al. (2020) conducted interview-based research in the hotel industry of Dubai to study the adoption of AI and robotics and found that AI provides RA to hotels. IAT will help forecast accurately and plan for banquets, room services and labour cost reduction. IAT’s positive impact on cost reduction, increase in lead time for performing operations like cleaning and improved delivery performance (Alsetoohy et al., 2019).

H2 tested the influence of R on IAT in the FSC. The final structural model showed that R and IAT in the FSC were supported (β = .313, p = .000). The effect of IAT’s benefits and R on the administrators of hotels towards its acceptance rate is theoretically constant with Alkhater et al. (2018) and Alharbi et al. (2016). H3 tested the influence of COMP on IAT in the FSC. The final structural model showed that COMP and IAT in the FSC were supported (β = .186, p = .000). The hotels planning to adopt IAT first need to check the complications associated with the IAT mechanism, its applications and the benefits gained through IAT. Prior research conducted by Alsetoohy et al. (2019) supported this study. Alsetoohy et al. (2019) showed that COMP negatively affects hotel managers’ attitudes in adopting IAT in the FSC. Nam et al. (2020) found that integrating AI with the existing technology will be very difficult.

H4 tested the influence of cost on IAT in the FSC. The final structural model showed that cost and IAT in the FSC were supported (β = .517, p = .000). Adopting and utilizing IAT in the FSC is often considered an additional cost, which could be a barrier to its adoption. Pillai and Sivathanu (2020) researched AI for talent acquisition in IT firms and showed that cost plays a vital role in adopting AI. Alsetoohy et al. (2019) showed that cost negatively affected hotel managers’ attitudes in adopting IAT in the FSC. H5 tested the influence of IA on IAT in the FSC. The final structural model showed that IA and IAT in the FSC were supported (β = .163, p = .000). A prior study conducted by Alsetoohy et al. (2019) showed that the organizational readiness to adopt IAT for the FSC did not support their research. Pillai and Sivathanu (2020) studied the factor of HR readiness in adopting AI and showed how HR readiness would help employees overcome the change resistance. H6 tested the influence of TMS on IAT in the FSC. The final structural model showed that TMS and IAT in the FSC were supported (β = .159, p = .000). Alsetoohy et al. (2019) showed that the management encouragement comprised of TMS and knowledge of IAT in the adoption of IAT in the FSC did not support their study. Pillai and Sivathanu (2020) found that TMS plays a vital role in the adoption of AI.

H7 tested the influence of ITA on IAT in the FSC. The final structural model showed that ITA and IAT in the FSC were supported (β = .400, p = .000). Most luxury hotels are being worked through worldwide administration organizations that execute state-of-the-art innovation to oversee the FSC. This will be beneficial for awareness for new technologies of the hotel managers and accelerate the IAT adoption in the FSC (Alsetoohy et al., 2019). H8 tested the influence of SE on IAT in the FSC. The final structural model showed that SE and IAT in the FSC were supported (β = .294, p = .000). Alsetoohy et al. (2019) estimated the quality of human resources and found a positive relationship for adopting IAT in the FSC. In past research, it was found that employees who know IT remain more motivated and ready to embrace the latest innovative technologies (Johnson & Diman, 2017). The hotel staff with IT knowledge showed positive results in adopting the newest technology in the FSC. H9 tested the influence of the EU on IAT in the FSC. Alsetoohy et al. (2019) showed that the EU adopting IAT in the FSC did not support their study. In prior studies (Alsetoohy & Ayoun, 2018; Patterson et al., 2003), EUs positively impacted firm structure and innovation. As uncertainty lacks information or data, there is a need to adopt these latest innovative technologies (Patterson et al., 2003). It will help in enhancing the performance of the FSC. The final structural model supported EU and IAT in the FSC (β = .178, p = .000).

H10 tested the influence of CP on IAT in the FSC. The final structural model showed that CP and IAT in the FSC were supported (β = .111, p = .000). Advanced technologies can strengthen the firms’ competitive edge by adopting IAT, which will also help them perform better than their competitors. When RFID was introduced, companies like Kmart and Wal-Mart pressured their vendors to adopt these technologies or lose the business. Alsetoohy et al. (2019) showed that the CP in adopting IAT in the FSC did not support their study. Pillai and Sivathanu (2020) showed that CP from the revival companies influences the firms in adopting AI.

H11 tested the influence of II on IAT in the FSC. The final structural model showed that II and IAT in the FSC were supported (β = .431, p = .000). Also, Alsetoohy et al. (2019) showed a positive relationship of II in adopting IAT in the FSC. H12 tested the influence of SP on IAT in the FSC. The final structural model showed that SP and IAT in the FSC were supported (β = .609, p = .000). Pillai and Sivathanu (2020) showed a positive relationship for the support of AI in their research. Demands from prevailing accomplices are a significant factor in another innovation reception, and associations may receive the innovation to demonstrate their quality as a firm trading partner (Kamble et al., 2019; Kouhizadeh et al., 2021).

Implication of the Study

Managerial Implications

This study will help the hotel managers and top management for taking decision towards the adoption of IAT in their hotels. IAT benefits traditional systems by developing a relationship between the clients and partners. It will encourage the managers to frame the organizational strategies for the future. IAT helps make purchasing decisions without humans’ interference. It provides a dedicated search for checking the lead time, item prices, monitoring the status of orders and provides statistical reports for top management. As a result, before migrating or adopting IAT, they must consider all relevant factors. IAT will help the hotel management to do its task without much human intervention. Managers will be able to perform efficiently and effectively with the adoption of IAT. Managers make decisions for their companies; they must weigh the benefits and drawbacks of the IAT.

Theoretical Implications

The current study has significantly contributed to the theoretical research and practices. This research has contributed considerably to the usage of IAT in the FSC. This research validates the TOE framework, which had been used in many IAs in the past (Abed, 2020; Ahmadi et al., 2017; Alazab et al., 2021; Alsetoohy et al., 2019; Braunscheidel & Suresh, 2009; Chong & Chan, 2012; Ghode et al., 2020). This study identified 12 factors RA, R, COMP, C, IA, TMS, ITA, SE, EU, CP, II and SP. All the 12 hypotheses are accepted, and previous studies support them. This study on IAT adoption in the luxury hotels of India is a unique one as such studies are not available. This study will help the researcher and professional to understand the impact of IAT in the FSC.

Conclusions

The current research determined the factors affecting the hotel’s acceptance of IAT in the FSC with a conceptual framework derived from the TOE framework and diffusion of innovation theory. IAT usage in FSC and its relation between TOE components and intention towards IAT adoption in FSC were researched empirically regarding the Indian perspective. The primary aim of this research was to decide whether the TOE framework’s elements impact the IAT in India’s hotel industry. A significant number of studies have been conducted on hospitality, its developments, and IAT; however, little research has been directed to decide the IAT selection in the FSC. The study’s findings are that the technology is not mature enough, so few managers may think adopting such technology may waste time and cost. Empirically, the upgradation to new technology from the current one should be made very carefully to enjoy the success. As a result of all the above circumstances, some managers may prefer to wait until proven success. This study also found that most hotels’ top management is ready to adopt the latest innovation in their hotels and create a competitive advantage.

Thus, this research experimentally explored: the usage of IAT in FSC and its implementation using TOE elements and the in administrators’ approach towards utilization of IAT in FSC; and ﬁnally, the relationship between inn controllers’ approach towards selection of IAT in FSC and their expectation for future acceptance. This research encourages the hotels to adopt IAT or impede them in moving to it. The gathered information was studied in two phases. At the measurement level, validity and R were used to confirm the research’s estimations. Also, at the structural level, the connections between the components and firms’ aim to adopt IAT in luxury hotels were studied to investigate factors that were decidedly connected with IAT in India; along these lines, the proposed hypothesis was evaluated in this stage. The final model helps hotel decision-makers considering IAT adoption in the FSC.

Limitations and Future Research

This research put light mainly on the luxury hotel segment in India. This research can be done to compare the outcomes within specific states of a country. This research primarily focused on the essential components of IAT acceptance in the FSC. Future research may also streamline research findings with various hotels nationwide to demonstrate more precise outcomes. Henceforth, comparative investigations should be possible for different nations and states. Further examination should be possible in other sectors.

Footnotes

Appendix

Appendix B.

Research Model Variables and Sources

Factors	Sub-Factor	Sources
Technological factors	Relative advantage	Alharbi et al. (2016), Alkhater et al. (2018), Alsetoohy et al. (2019), Badi et al. (2021), Chen et al. (2021), Gangwar et al. (2015), Makena (2013), Senyo et al. (2016)
	Complexity	Ahmadi et al. (2017), Badi et al. (2021), Chen et al. (2021), Gangwar et al. (2015), Gökalp et al. (2020)
	Cost	Alsetoohy et al. (2019), Alshamaila et al. (2013), Amron et al. (2019), Mrhaouarh et al. (2018)
	Reliability	Alharbi et al. (2017), Alkhater et al. (2018), Alsetoohy et al. (2019), Alshamaila et al. (2013)
Organizational factors	Innovation adoption	Pillai & Sivathanu (2020), Priyadarshinee et al. (2017)
	Top management support	Abed (2020), Alazab et al. (2021), Alsetoohy et al. (2019), Clohessy et al. (2019), Senyo et al. (2016)
	Skilled employees	Alharbi et al. (2017), Alsetoohy et al. (2019)
	IT awareness	Alsetoohy et al. (2019), Priyadarshinee et al. (2017), Raut et al. (2018)
Environmental factors	Environmental uncertainty	Alsetoohy et al. (2019), Kumar & Krishnamoorthy (2020), Senyo et al. (2016), Sharma & Sehrawat (2020), Skafi et al. (2020)
	Competitive pressure	Alharbi et al. (2017), Cruz-Jesus et al. (2019), Oliveira et al. (2014), Senyo et al. (2016), Sharma & Sehrawat (2020)
	Information intensity	Alsetoohy et al. (2019), Priyadarshinee et al. (2017)
	Supplier’s pressure	Alsetoohy et al. (2019), Kouhizadeh et al. (2021), Priyadarshinee et al. (2017)

Source: From literature review.

References

Abed

S. S.

(2020). Social commerce adoption using TOE framework: An empirical investigation of Saudi Arabian SMEs. International Journal of Information Management, 53(1), 102118. https://doi.org/10.1016/j.ijinfomgt.2020.102118

Ahmadi

, Nilashi

, Shahmoradi

, & Ibrahim

(2017). Hospital information system adoption: Expert perspectives on an adoption framework for Malaysian public hospitals. Computers in Human Behavior, 67, 161–189. https://doi.org/10.1016/j.chb.2016.10.023

Alazab

, Alhyari

, Awajan

, & Abdallah

A. B.

(2021). Blockchain technology in supply chain management: An empirical study of the factors affecting user adoption/acceptance. Cluster Computing, 24(1), 83–101.

Alharbi

, Atkins

, & Stanier

(2016). Understanding the determinants of cloud computing adoption in Saudi healthcare organisations. Complex & Intelligent Systems, 2(3), 155–171. https://doi.org/10.1007/s40747-016-0021-9

Alharbi

, Atkins

, & Stanier

(2017). Cloud computing adoption in healthcare organisations: A qualitative study in Saudi Arabia. In hameurlain

, Kung

, Wagner

, Sakr

, Razzak

, & Riyad

(Eds.), Transactions on large-scale data- and knowledge-centered systems XXXV (pp. 96–131). Springer. https://doi.org/10.1007/978-3-662-56121-8_5

Alkhater

, Walters

, & Wills

(2018). An empirical study of factors influencing cloud adoption among private sector organisations. Telematics and Informatics, 35(1), 38–54. https://doi.org/10.1016/j.tele.2017.09.017

Alsetoohy

, & Ayoun

(2018). Intelligent agent technology: The relationships with hotel food procurement practices and performance. Journal of Hospitality and Tourism Technology, 9(1), 109–124. https://doi.org/10.1108/JHTT-04-2017-0028

Alsetoohy

, Ayoun

, Arous

, Megahed

, & Nabil

(2019). Intelligent agent technology: What affects its adoption in hotel food supply chain management? Journal of Hospitality and Tourism Technology, 10(3), 286–310. https://doi.org/10.1108/JHTT-01-2018-0005

Alshamaila

, Papagiannidis

, & Li

(2013). Cloud computing adoption by SMEs in the north east of England: A multi-perspective framework. Journal of Enterprise Information Management, 26(3), 250–275. https://doi.org/10.1108/17410391311325225

10.

Amron

M. T.

, Ibrahim

, Abu Bakar

N. A.

, & Chuprat

(2019). Determining factors influencing the acceptance of cloud computing implementation. Procedia Computer Science, 161, 1055–1063. https://doi.org/10.1016/j.procs.2019.11.216

11.

Aslam

, Saleem

, Khan

N. T.

, & Kim

Y. B.

(2021). Factors influencing blockchain adoption in supply chain management practices: A study based on the oil industry. Journal of Innovation & Knowledge, 6(2), 124–134. https://doi.org/10.1016/j.jik.2021.01.002

12.

Badi

, Ochieng

, Nasaj

, & Papadaki

(2021). Technological, organisational and environmental determinants of smart contracts adoption: UK construction sector viewpoint. Construction Management and Economics, 39(1), 36–54. https://doi.org/10.1080/01446193.2020.1819549

13.

Baral

M. M.

, & Verma

(2021). Cloud computing adoption for healthcare: An empirical study using SEM approach. FIIB Business Review, 10(3), 255–275. https://doi.org/10.1177/23197145211012505

14.

Barton

, & Thomas

(2009). Implementation of intelligent systems, enabling integration of SMEs to high-value supply chain networks. Engineering Applications of Artificial Intelligence, 22(6), 929–938. https://doi.org/10.1016/J.ENGAPPAI.2008.10.016

15.

Braunscheidel

M. J.

, & Suresh

N. C.

(2009). The organizational antecedents of a firm’s supply chain agility for risk mitigation and response. Journal of Operations Management, 27(2), 119–140. https://doi.org/10.1016/J.JOM.2008.09.006

16.

Byrne

B. M.

(2010). Structural equation modeling with AMOS: basic concepts, applications, and programming (multivariate applications series). Taylor & Francis Group.

17.

Chen

, Li

, & Chen

(2021). Explore success factors that impact artificial intelligence adoption on telecom industry in China. Journal of Management Analytics, 8(1), 36–68. https://doi.org/10.1080/23270012.2020.1852895

18.

Chong

A. Y. L.

, & Chan

F. T. S.

(2012). Structural equation modeling for multi-stage analysis on radio frequency identification (RFID) diffusion in the health care industry. Expert Systems with Applications, 39(10), 8645–8654. https://doi.org/10.1016/j.eswa.2012.01.201

19.

Clohessy

, Acton

, & Rogers

(2019). Blockchain adoption: Technological, organisational and environmental considerations. In Trieblmaier

& Beck

(Eds.), Business transformation through blockchain (pp. 47–76). Palgrave Macmillan. https://doi.org/10.1007/978-3-319-98911-2_2

20.

Cruz-Jesus

, Pinheiro

, & Oliveira

(2019). Understanding CRM adoption stages: Empirical analysis building on the TOE framework. Computers in Industry, 109, 1–13. https://doi.org/10.1016/j.compind.2019.03.007

21.

DeVellis

R. F.

, Lewis

M. A.

, & Sterba

K. R.

(2003). Interpersonal emotional processes in adjustment to chronic illness. In Suls

& Wallston

K. A.

(Eds.), Social psychological foundations of health and illness. Blackwell Publishing.

22.

Di Vaio

, Boccia

, Landriani

, & Palladino

(2020). Artificial intelligence in the agri-food system: Rethinking sustainable business models in the COVID-19 scenario. Sustainability (Switzerland), 12(12). https://doi.org/10.3390/SU12124851

23.

Fornell

, & Larcker

D. F.

(1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104

24.

Gangwar

, Date

, & Ramaswamy

(2015). Understanding determinants of cloud computing adoption using an integrated TAM-TOE model. Journal of Enterprise Information Management, 28(1), 107–130. https://doi.org/10.1108/JEIM-08-2013-0065

25.

Gerbing

D. W.

, & Anderson

J. C.

(1988). An updated paradigm for scale development incorporating unidimensionality and its assessment. Journal of Marketing Research, 25(2), 186–192. https://doi.org/10.1177/002224378802500207

26.

Ghode

, Yadav

, Jain

, & Soni

(2020). Adoption of blockchain in supply chain: An analysis of influencing factors. Journal of Enterprise Information Management, 33(3), 437–456. https://doi.org/10.1108/JEIM-07-2019-0186

27.

Gökalp

, Gökalp

M. O.

, & Çoban

(2020). Blockchain-based supply chain management: Understanding the determinants of adoption in the context of organizations. Information Systems Management, 1–22. https://doi.org/10.1080/10580530.2020.1812014

28.

Grey

, Olavson

, & Shi

(2005). The role of e-marketplaces in relationship-based supply chains: A survey. IBM Systems Journal, 44(1). https://ieeexplore.ieee.org/abstract/document/5386714/

29.

Hair

J. F.

, Black

W. C.

, Babin

B. J.

, & Anderson

R. E.

(2010). Multivariate data analysis (7th ed.). Pearson, Prentice Hall.

30.

Hair

J. F.

, Sarstedt

, Hopkins

, & Kuppelwieser

V. G.

(2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106–121. https://doi.org/10.1108/EBR-10-2013-0128

31.

Hair

J. F.

, Sarstedt

, Ringle

C. M.

, & Mena

J. A.

(2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414–433. https://doi.org/10.1007/s11747-011-0261-6

32.

Hair

J. F.

, Ringle

C. M.

, & Sarstedt

(2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1–2), 1–12.

33.

Henseler

, Ringle

C. M.

, & Sinkovics

R. R.

(2009). The use of partial least squares path modeling in international marketing. In Sinkovics

R. R.

& Ghauri

P. N.

(Eds.), New challenges to international marketing (Advances in international marketing, Vol. 20, pp. 277–319). Emerald Group Publishing Ltd. https://doi.org/10.1108/S1474-7979(2009)0000020014

34.

Hiran

K. K.

, & Henten

(2020). An integrated TOE-DoI framework for cloud computing adoption in higher education: The case of Sub-Saharan Africa, Ethiopia. In Pant

, Sharma

T. K.

, Verma

O. P.

, Singla

, & Sikander

(Eds.), Soft computing: Theories and applications (Vol. 1053, pp. 1281–1290). Springers. https://doi.org/10.1007/978-981-15-0751-9_117

35.

Jafari

(2002). Conceptualizing intelligent agents for teaching and learning. Educause Quarterly, 25(3), 28–34. https://www.learntechlib.org/p/95298/

36.

Jalilvand

M. R.

, Khazaei Pool

, Khodadadi

, & Sharifi

(2019). Information technology competency and knowledge management in the hospitality industry service supply chain. Tourism Review, 74(4), 872–884. https://doi.org/10.1108/TR-04-2018-0054

37.

Johnson

R. D.

, & Diman

(2017). An investigation of the factors driving the adoption of cloud-based human resource information systems by small- and medium-sized businesses. In Bandarouk

, Ruel

H. J. M.

, & Perry

(Eds.), Electronic HRM in the smart era. Emerald Publishing Ltd. https://doi.org/10.1108/978-1-78714-315-920161001

38.

Kamble

, Gunasekaran

, & Arha

(2019). Understanding the blockchain technology adoption in supply chains-Indian context. International Journal of Production Research, 57(7), 2009–2033. https://doi.org/10.1080/00207543.2018.1518610

39.

Kim

, Kim

, Badu-Baiden

, Giroux

, & Choi

(2021). Preference for robot service or human service in hotels? Impacts of the COVID-19 pandemic. International Journal of Hospitality Management, 93, 102795. https://doi.org/10.1016/j.ijhm.2020.102795

40.

Kittipanyangam

, & Tan

K. H.

(2020). A framework for food supply chain digitalization: Lessons from Thailand. Production Planning and Control, 31(2–3), 158–172. https://doi.org/10.1080/09537287.2019.1631462

41.

Kline

R. B.

(2012). Assumptions in structural equation modeling. In Hoyle

R. H.

(Ed.), Handbook of structural equation modeling (pp. 111–125). The Guilford Press. https://psycnet.apa.org/record/2012-16551-007

42.

Kline

R. B.

(2015). Principles and practice of structural equation modeling. Guilford publications.

43.

Kouhizadeh

, Saberi

, & Sarkis

(2021). Blockchain technology and the sustainable supply chain: Theoretically exploring adoption barriers. International Journal of Production Economics, 231, 107831. https://doi.org/10.1016/j.ijpe.2020.107831

44.

Kumar

, & Krishnamoorthy

(2020). Business analytics adoption in firms: A qualitative study elaborating TOE framework in India. International Journal of Global Business and Competitiveness, 15(2), 80–93. https://doi.org/10.1007/s42943-020-00013-5

45.

Lai

W.C.

, & Hung

W. H.

(2018, December 2–6). A framework of cloud and AI based intelligent hotel [Paper Presentation]. 18th International Conference on Electronic Business (ICEB) (pp. 36–43), Guilin, China. iceb.johogo.com/proceedings/2018/ICEB2018_paper_81_full.pdf

46.

Latan

, Jabbour

C. J. C.

, Lopes de Sousa Jabbour

A. B.

, Wamba

S. F.

, & Shahbaz

(2018). Effects of environmental strategy, environmental uncertainty and top management’s commitment on corporate environmental performance: The role of environmental management accounting. Journal of Cleaner Production, 180, 297–306. https://doi.org/10.1016/j.jclepro.2018.01.106

47.

Lin

H. F.

(2014). Understanding the determinants of electronic supply chain management system adoption: Using the technology-organization-environment framework. Technological Forecasting and Social Change, 86, 80–92. https://doi.org/10.1016/j.techfore.2013.09.001

48.

Low

, Chen

, & Wu

(2011). Understanding the determinants of cloud computing adoption. Industrial Management and Data Systems, 111(7), 1006–1023. https://doi.org/10.1108/02635571111161262

49.

Makena

J. N.

(2013). Factors that affect cloud computing adoption by small and medium enterprises in Kenya. International Journal of Computer Applications Technology and Research, 2(5), 517–521. https://doi.org/10.7753/ijcatr0205.1003

50.

Mangina

, & Vlachos

I. P.

(2005). The changing role of information technology in food and beverage logistics management: Beverage network optimisation using intelligent agent technology. Journal of Food Engineering, 70(3), 403–420. https://doi.org/10.1016/J.JFOODENG.2004.02.044

51.

Mrhaouarh

, Okar

, Namir

, & Chafiq

(2018). Cloud Computing adoption in developing countries: A systematic literature review [Paper Presentation]. 2018 IEEE International Conference on Technology Management, Operations and Decisions, ICTMOD 2018, Marrakech, Morocco, pp. 73–79,. https://doi.org/10.1109/ITMC.2018.8691295

52.

Nam

, Dutt

C. S.

, Chathoth

, Daghfous

, & Khan

M. S.

(2020). The adoption of artificial intelligence and robotics in the hotel industry: Prospects and challenges. Electronic Markets. https://doi.org/10.1007/s12525-020-00442-3

53.

Narmetta

, & Krishnan

(2020). Competitiveness, change readiness, and ICT development: An empirical investigation of TOE framework for poverty alleviation. In Sharma

S. K.

, Dwivedi

Y. K.

, Metri

, & Rana

N. P.

(Eds.), Re-imagining diffusion and adoption of information technology and systems: A Continuing conversation (IFIP Advances in Information and Communication Technology, Vol. 618, pp. 638–649). Springer. https://doi.org/10.1007/978-3-030-64861-9_55

54.

Nunnally

J. C.

(1994). Psychometric theory 3E. Tata McGraw-Hill Education.

55.

Oliveira

, Thomas

, & Espadanal

(2014). Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors. Information and Management, 51(5), 497–510. https://doi.org/10.1016/j.im.2014.03.006

56.

Ooi

K.-B.

, Lee

V.-H.

, Tan

G. W.-H.

, Hew

T.-S.

, & Hew

J.-J.

(2018). Cloud computing in manufacturing: The next industrial revolution in Malaysia? Expert Systems with Applications, 93, 376–394. https://doi.org/10.1016/j.eswa.2017.10.009

57.

Pateli

, Mylonas

, & Spyrou

(2020). Organizational adoption of social media in the hospitality industry: An integrated approach based on DIT and TOE frameworks. Sustainability, 12(17), 7132. https://doi.org/10.3390/su12177132

58.

Patterson

K. A.

, Grimm

C. M.

, & Corsi

T. M.

(2003). Adopting new technologies for supply chain management. Transportation Research Part E: Logistics and Transportation Review, 39(2), 95–121.

59.

Pillai

, & Sivathanu

(2020). Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations. Benchmarking, 27(9), 2599–2629. https://doi.org/10.1108/BIJ-04-2020-0186

60.

Podsakoff

N. P.

(2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.

61.

Priyadarshinee

, Raut

R. D.

, Jha

M. K.

, & Kamble

S. S.

(2017). A cloud computing adoption in Indian SMEs: Scale development and validation approach. Journal of High Technology Management Research, 28(2), 221–245. https://doi.org/10.1016/j.hitech.2017.10.010

62.

Puklavec

, Oliveira

, & Popovič

(2018). Understanding the determinants of business intelligence system adoption stages an empirical study of SMEs. Industrial Management and Data Systems, 118(1), 236–261. https://doi.org/10.1108/IMDS-05-2017-0170

63.

Raut

R. D.

, Priyadarshinee

, Gardas

B. B.

, & Jha

M. K.

(2018). Analyzing the factors influencing cloud computing adoption using three stage hybrid SEM-ANN-ISM (SEANIS) approach. Technological Forecasting and Social Change, 134, 98–123. https://doi.org/10.1016/J.TECHFORE.2018.05.020

64.

Rogers

E. M.

(1995). Diffusion of innovations: Modifications of a model for telecommunications. In Die Diffusion von innovationen in der telekommunikation (pp. 25–38). Springer. https://doi.org/10.1007/978-3-642-79868-9_2

65.

Samara

, Magnisalis

, & Peristeras

(2020). Artificial intelligence and big data in tourism: A systematic literature review. Journal of Hospitality and Tourism Technology, 11(2), 343–367. https://doi.org/10.1108/JHTT-12-2018-0118

66.

Senyo

P. K.

, Effah

, & Addae

(2016). Preliminary insight into cloud computing adoption in a developing country. Journal of Enterprise Information Management, 29(4), 505–524. https://doi.org/10.1108/JEIM-09-2014-0094

67.

Sharma

, & Sehrawat

(2020). A hybrid multi-criteria decision-making method for cloud adoption: Evidence from the healthcare sector. Technology in Society, 61, 101258. https://doi.org/10.1016/j.techsoc.2020.101258

68.

Shee

, Miah

S. J.

, Fairfield

, & Pujawan

(2018). The impact of cloud-enabled process integration on supply chain performance and firm sustainability: The moderating role of top management. Supply Chain Management, 23(6), 500–517. https://doi.org/10.1108/SCM-09-2017-0309

69.

Shirazi

M. A.

, & Soroor

(2007). An intelligent agent-based architecture for strategic information system applications. Knowledge-Based Systems, 20(8), 726–735. https://doi.org/10.1016/J.KNOSYS.2006.10.004

70.

Siddik

M. N. A.

, Kabiraj

, Hosen

M. E.

, & Miah

M. F.

(2021). Blockchain technology and facilitation of international trade: An empirical analysis. FIIB Business Review, 10(3), 232–241. https://doi.org/10.1177/2319714520968297

71.

Skafi

, Yunis

M. M.

, & Zekri

(2020). Factors influencing SMEs’ adoption of cloud computing services in Lebanon: An empirical analysis using TOE and contextual theory. IEEE Access, 8, 79169–79181. https://doi.org/10.1109/ACCESS.2020.2987331

72.

Stjepić

A.-M.

, Bach

M. P.

, & Vukšić

V. B.

(2021). Exploring risks in the adoption of business intelligence in SMEs using the TOE framework. Journal of Risk and Financial Management, 14(2), 58. https://doi.org/10.3390/jrfm14020058

73.

Sujatha

(2011). Role of intelligent agents in facilitating information flow in supply chain management. International Journal of Logistics Systems and Management, 9(2), 229–237. https://doi.org/10.1504/IJLSM.2011.041508

74.

Tashkandi

A. A.

, & Al-Jabri

(2015). Cloud computing adoption by higher education institutions in Saudi Arabia: Analysis based on TOE [Paper Presentation]. 2015 International Conference on Cloud Computing, ICCC, Riyadh, Saudi Arabia, pp. 1–8. https://doi.org/10.1109/CLOUDCOMP.2015.7149634

75.

Tornatzky

, Fleischer

, & Chakrabarti

(1990). Processes of technological innovation. Food and Agricultural Organization of the United Nations. https://agris.fao.org/agris-search/search.do?recordID=US201300694725

76.

Tornatzky

L. G.

, & Klein

K. J.

(1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, EM- 29(1), 28–45. https://doi.org/10.1109/TEM.1982.6447463

77.

Wong

L. W.

, Leong

L. Y.

, Hew

J. J.

, Tan

G. W. H.

, & Ooi

K. B.

(2020). Time to seize the digital evolution: Adoption of blockchain in operations and supply chain management among Malaysian SMEs. International Journal of Information Management, 52, 101997. https://doi.org/10.1016/j.ijinfomgt.2019.08.005

78.

Yang

, Song

, Cheung

, & Guan

(2021). How to enhance hotel guests’ acceptance and experience of smart hotel technology: An examination of visiting intentions. International Journal of Hospitality Management, 97, 103000. https://doi.org/10.1016/J.IJHM.2021.103000

79.

Zhang

W. J.

, & Xie

S. Q.

(2006). Agent technology for collaborative process planning: A review. The International Journal of Advanced Manufacturing Technology, 32(3), 315–325. https://doi.org/10.1007/S00170-005-0345-X