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

Get full access to this article

View all access options for this article.

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