IoT in the Agricultural Industry: History
View Latest Version
Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor:
Abu Bakkar Siddik
,
Yan Shi
,
Mohammad Masukujjaman
,
guangwen zheng
,
Muhammad Hamayun
,
Abdullah Ibrahim

Agriculture resulted in the birth of sedentary human civilization and remains one of the world’s most significant sectors. However, despite its importance, a vast majority of agricultural techniques remain conventional. With a predicted worldwide population of 9.6 billion by 2050, a 70% increase in global food production is required to fulfill the ever-rising demand.  Internet of Things (IoT) is a term that has become increasingly popular in recent years in this context. The capacity to deal with dynamic workflow environments enables the IoT to address many current agricultural concerns. The IoT and its accompanying technologies have huge potential to boost agricultural productivity and can play a critical role in reshaping the agriculture industry.

  • agriculture
  • internet of things
  • Willingness to Pay
  • Emerging Economy
  • Bangladesh

1. IoT and Its Application in Agriculture

The IoT is a relatively new paradigm that “connects real-world objects to the Internet, allowing objects to collect, process, and communicate data without human intervention” [1]. Users can get smarter services from IoT technology applications due to their utilization of ubiquitous computing and real-time processing. The IoT is a massive network that links people, data, and applications, and enables digital services management and control [2] (Table 1). The IoT’s underlying network architecture can connect a wide range of smart devices—ranging from microsensors to huge agricultural tractors—over the Internet. Smart farms, intelligent greenhouses, scientific diseases, pest monitoring, livestock movement monitoring, controlled fertilizer usage, irrigation management, and asset tracking may all benefit from the IoT [3]. The IoT equips farmers with automated technologies and decision-making tools that seamlessly integrate information, services, and goods for higher quality, productivity, and profitability in farming [4]. A multitude of papers illustrating the uses of the IoT in agriculture now exists [3][4][5][6][7][8][9].
Table 1. Usage of IoT for various agricultural purposes.
Application Functions
Analysis of yield data Composite layers are created by condensing numerous years’ worth of yield data [10]
Variable-rate technologies Allow the application of site-specific agricultural inputs [11]
Field mapping using GPS Achieve accurate acreage measurement for fields and roads using maps created by farmers [11]
Weather forecasts at the field-level Measurements made by sensors aid in the forecasting of local weather and precipitation [12].
Autosteer technology Allows precision farming machinery to operate on autopilot, improving accuracy and production for farmers [11].
Optimization tools for machines Agriculture inputs are reduced via the use of precise Global Positioning Systems (GPS) and sensors to record agricultural activities [13].
Services measuring productivity Yield monitoring systems collect data from harvesting trucks and sensors on soil conditions, moisture, and crop yields [11].
It is primarily through the IoT that conventional agriculture can benefit from the increased sensing and monitoring of production processes, improved understanding of specific farming conditions (e.g., weather, environmental conditions, and pest and disease management), concise and remote control of farm operations such as application of fertilizers and pesticides, and autonomous weeding [14][15] (Table 2).
Table 2. Research on the adoption of IoT in the agricultural industry.
Sources Research
Method/Sample Size/Country		 Analysis Tools Theoretical Framework/
Models		 Factors Limitations
[16] Empirical
/Farmers’ interview/220/
India		 PLS-SEM Behavioral reasoning theory Attitude, reason for,
reason against, and value of openness to change		 Personal innovations and risk-taking ability could be used as the moderator
[17] Literature
Review		 MICMAC methods Modified total interpretive structural
modeling		 Crop management,
government initiative, soil quality management, and irrigation management		 Results are based on a literature review and are not an empirical research
[18] Empirical/
Farm owners and managers’ interview/395/
Thailand		 SPSS/
Multiple regression		 Technology acceptance model IoT readiness, e-learning,
and institutional support
perceived usefulness.		 Results are based on only small farms and did not consider government support as a construct
[11] Empirical/
Questionnaire survey/492/USA		 SEM-STATA None Perceived risk, perceived value, trust, age, farm size Did not consider contextual factors such as price value and other constructs like trust.
[19] Empirical/
Face-to-face farmers’ interview/400/Tanzania		 Structural Equation Modeling (SEM) (AMOS) Innovation diffusion
theory		 Awareness, relative advantages,
ease of use, compatibility, visibility		 Trust and perceived risk factors could be included to enhance the explanatory power
[20] Empirical/company experts/35/China Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis Interpretive structural modeling Cost savings, perceived benefit, external pressure, technical knowledge, executive support, trust, technological compatibility, complexity, scale of the enterprise, and government support. The study is limited to its analysis method as no robustness was tested using other latest statistical methods
Current study Empirical/
Farmer’s interview/345/
Bangladesh		 SEM-AMOS UTAUT 2 Personal innovativeness, social influence, effort expectancy, willingness to adopt, facilitating condition, performance expectancy, hedonic motivation, price value, government support, and trust.

2. UTAUT 2 and Hypothesis Formulation

According to the first UTAUT, only extrinsically motivating elements are considered, and a heavy focus is placed on the utilitarian value of the technology. This is represented by the construct of performance expectancy in the sense of utility, which also represents the strongest influencing factor for the intention to use in the UTAUT [21]. This extrinsic motivation component, according to Venkatesh et al. [22], is augmented by the inner motivation component and hedonic motivation, in the UTAUT2. Along with the existing construct (Personal innovativeness, social influence, effort expectancy, willingness to adopt, facilitating condition, performance expectancy, hedonic motivation, price value, except habit) of the UTAUT2, this study experiments with three additional constructs, including trust, government support, and willingness to pay (Figure 1). The constructs are subsequently explained.
Sustainability 14 06640 g001
Figure 1. Conceptual framework.

2.1. Performance Expectancy (PE)

Following the basic concept of PE and its adaptation to the current situation, researchers defined this factor as the degree to which individuals feel that accessing the IoT will assist them in undertaking a certain activity [23][24][25]. In the agricultural industry, PE is defined as the degree to which a device can assist users in monitoring their daily yield data analysis, GPS field mapping, productivity measurement, and field-level weather forecasts [11]. If farmers’ perceptions of improved harvest management, easier access to agricultural services, more efficient use of resources, and higher overall productivity enhance their PE for connected crop care equipment, then their willingness to use and pay for these devices will rise accordingly. The BI to utilize healthcare technology has been strongly associated with PE in previous studies [23][26][27], and according to Gu and Liu [28], PE is positively associated with the willingness to pay for Q&A platforms.

2.2. Government Support

The IoT business, particularly in the agriculture sector, relies heavily on government backing. The government’s willingness to engage in the business may help the agricultural business flourish via the enactment of policies that benefit both investors and service providers. According to research by Goo and Heo [29], government involvement has a favorable influence on the growth of Fintech owing to the lowering of uncertainties. According to research conducted by Chong and Ooi [30] on the acceptance of RosettaNet in Malaysia, the Malaysian government actively encourages the use of the standard by offering grants and tax exemptions. Following Marakarkandy et al.’s [31] findings, government support for new technologies has a favorable correlation with their adoption.

2.3. Facilitating Conditions

FC is the user’s belief that support and infrastructure are available to assist them in the usage of their desired technology [22]. Technical and infrastructure support for system utilization is usually classified under FC, as it has an impact on both user intent and actual use [21][22]. Technology or organizational assistance is required by farmers to employ crop care equipment. The government should encourage the development of the physical, network, and electrical infrastructure, as FC makes it possible for users to have a better understanding of the resources and facilities available to them, such as their talents, technical expertise, and the help and direction of IT experts [25][32]. Boontarig et al. [33] discovered that FCs have a favorable effect on behavioral intention in their investigation. FC is perceived as the most important factor in determining a consumer’s intention to utilize mHealth [34][35].

2.4. Social Influence

As highlighted in several studies, SI has a multifaceted role in the acceptance of new technology [36][37]. To put it another way, SI is the degree to which people believe that important individuals agree with their actions or technological choices [21][25][34]. Specifically, SI has demonstrated an excellent predictive power in the context of health-related technology use [37][38][39], wireless devices, smartphones [40], and mobile diet applications [38].

2.5. Hedonic Motivation

Hedonic Motivation (HM) has been defined as the degree to which the usage of new technology results in satisfaction or pleasure, and it has been identified as playing a critical role in the adoption of new technology and its use [22]. Joy, fun, entertainment, playfulness, and other intangible benefits are all included in HM (usefulness, efficiency, performance, etc.). To encourage clients to accept new technology, it is important to show them how the use of the new technology will provide them joy and happiness [41][42]. If a person thinks that the usage of mobile applications for healthcare services is amusing, interesting, and pleasant, he or she is more likely to use the application [34][43]. Consequently, a customer’s desire for new technologies such as the IoT employing GPS-based mapping or yield control systems could infuse fun or enjoyment in its usage, which in turn increases the user’s behavioral intention towards the IoT.

2.6. Effort Expectancy

People’s willingness to utilize a new technology is influenced by its ease of usage [23]. EE refers to “the ease with which a system may be used” [21]. Farmers who utilize IoT devices are more likely to perceive technology as good and useful when EE is present. As a result, the amount of energy required to run the system increases [44]. Innovation in technologies that are quick and easy to implement with minimal effort is often viewed as a sign of progress by users [45][46]. Researchers [26][32][47] discovered that EE was an excellent indicator of behavioral intention. Thus, because farmers, in many cases, are less educated and less technology conscious, they are likely to adopt the IoT if they perceive or discover that it is not a complex system to operate, or requires less effort to learn. 

2.7. Trust

Trust is the reliance or confidence in a particular service. The IoT will be more widely adopted if people are confident in its ability to deliver on its promises, and in the company that provides it. The IoT application’s capacity to function during an emergency may be hampered if users harbor mistrust in the service provider. Although most studies have indicated a favorable association between trust and BI, some have revealed otherwise [45][48][49]. According to Alalwan et al. [45] and Chao [50], trust is a key factor in the adoption of mobile technology. Similarly, the researchers [47][48] discovered trust to have a significant impact on students’ BIs toward the utilization of mobile learning; however, Kabra et al. [48] revealed the non-existence of an association between trust and BI. According to Jiang et al. [51], a positive association exists between technological trust and the willingness to purchase and adopt autonomous cars.

2.8. Price Value

Price value is another component that has been included in the UTAUT 2 [22]. Price Value (PV) is defined as a consumer’s cognitive tradeoff between the perceived system value and the cost of acquiring or utilizing a new technology [22]. With the usage of new technology, end-users are constantly comparing the cost incurred with the resulting savings that they might derive from the new technology [41][52][53]. With the use of information technology such as the IoT, the agricultural production process may benefit from the delivery of crop care information services at a more affordable rate as compared to the traditional method of physical inspection for pest control or irrigation. By assuming that a product’s benefits surpass its expenses, its pricing is considered to be positive [22].

2.9. Personal Innovativeness

Personal innovativeness was defined by Lu et al. [54] as the willingness of an individual to try out new technologies. Adoption of new technologies is mostly driven by a person’s willingness to tolerate the existence of the technology. According to this study, user innovativeness is characterized as the readiness to experiment with IoT services and eagerness to explore new technologies. Earlier studies have revealed a correlation between user inventiveness and technological acceptance [55][56][57][58][59], and similarly, Leckie et al. [60] demonstrated the impact of service innovation in the generation of total service value appraisal, customer engagement, and loyalty. O’Cass and Carlson [61] discovered that perceived website innovation predicts a customer’s trust. The perceived innovativeness of a website is related to the notions of originality and utility—according to the authors—and these underlying characteristics encourage people to have faith in a website.

2.10. Willingness to Adopt

The model’s main idea is hinged around people’s willingness to try new things, which is referred to as Behavioral Intention. Many researchers focus on the intention to utilize technology rather than its actual utilization. A close examination of people’s (and managers’) willingness to pay for the IoT reveals their ability to price it when the technology is launched. The willingness of consumers to pay more for a product or service is influenced by their attitudes toward, and intentions to adopt, newer technology such as green information and communication technologies, renewable heating systems, and electric automobiles [62][63][64].

2.11. Willingness to Pay

Willingness to pay (WTP) is defined as ‘the highest price that a consumer is willing to pay for a product or service’ [65]. Managers must realize that because the utmost number of consumers are willing to pay, they must be ready to create an effective pricing strategy that maximizes profits while also satisfying consumers’ needs and expectations [66]. Individuals’ buying intents and WTP are influenced by product, service, and technology values. While the ‘desire to pay more’ measures the influence of monetary units on values, purchase intention measures consumers’ intention to purchase a service or technology [65]. Although consumers could expect an amazing experience from the use of IoT services, it is crucial to grasp the value–price tradeoff in the market. The value–price tradeoff can be better grasped when included in the ‘desire to pay more’ variables.

2.12. The Moderation Effect of Facilitating Condition

As earlier stated, facilitating condition refers to the necessary facility such as knowledge, technological awareness, and technical and infrastructural support. Various scholars have applied a variety of support facilities as a moderation tool. For instance, Li and Zhao [67] studied the connected classroom climate as a moderating effect in UTAUT constructs such as performance expectancy and behavioral intention and discovered a significant relationship. In their research, Abubakar and Ahmad [68] hypothesized that technological awareness moderates the link between performance expectancy and behavioral intention, while others directly applied the FC as a moderator. For example, Humida et al. [69] assessed the moderation effect of facilitating conditions between the perceived usefulness (same as performance expectancy) and behavioral intention towards the e-learning system in Bangladesh, albeit its infrastructural deficiency among the Bangladeshi students. They concluded that no significant moderation of facilitating conditions was observed, owing to the respondents being students who were knowledgeable about technology.

This entry is adapted from the peer-reviewed paper 10.3390/su14116640

References

  1. Pattar, S.; Buyya, R.; Venugopal, K.R.; Iyengar, S.S.; Patnaik, L.M. Searching for the IoT Resources: Fundamentals, Requirements, Comprehensive Review, and Future Directions. IEEE Commun. Surv. Tutor. 2018, 20, 2101–2132.
  2. Chaudhary, R.; Pandey, J.R.; Pandey, P.; Chaudhary, P. Case Study of Internet of Things in Area of Agriculture, AGCO’s Fuse Technology’s ‘Connected Farm Services’. In Proceedings of the 2015 International Conference on Green Computing and Internet of Things (ICGCIoT), Delhi, India, 8–10 October 2015; pp. 148–153.
  3. Ahmed, N.; De, D.; Hussain, I. Internet of Things (IoT) for Smart Precision Agriculture and Farming in Rural Areas. IEEE Internet Things J. 2018, 5, 4890–4899.
  4. Elijah, O.; Rahman, T.A.; Orikumhi, I.; Leow, C.Y.; Hindia, M.H.D.N. An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges. IEEE Internet Things J. 2018, 5, 3758–3773.
  5. Patil, K.A.; Kale, N.R. A Model for Smart Agriculture Using IoT. In Proceedings of the 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), Jalgaon, India, 22–24 December 2016; pp. 543–545.
  6. Arshad, J.; Aziz, M.; Al-Huqail, A.A.; Zaman, M.H.U.; Husnain, M.; Rehman, A.U.; Shafiq, M. Implementation of a LoRaWAN Based Smart Agriculture Decision Support System for Optimum Crop Yield. Sustainability 2022, 14, 827.
  7. Hassan, A.; Asif, R.M.; Rehman, A.U.; Nishtar, Z.; Kaabar, M.K.A.; Afsar, K. Design and Development of an Irrigation Mobile Robot. IAES Int. J. Robot. Autom. 2021, 10, 75.
  8. Rehman, A.U.; Asif, R.M.; Tariq, R.; Javed, A. Gsm Based Solar Automatic Irrigation System Using Moisture, Temperature and Humidity Sensors. In Proceedings of the 2017 International Conference on Engineering Technology and Technopreneurship, ICE2T 2017, Kuala Lumpur, Malaysia, 1–4 January 2017.
  9. Arshad, J.; Tariq, R.; Saleem, S.; Rehman, A.U.; Munir, H.; Amiri Golilarz, N.; Saleem, A. Intelligent Greenhouse Monitoring and Control Scheme: An Arrangement of Sensors Raspberry Pi Based Embedded System and IoT Platform. Indian J. Sci. Technol. 2020, 13, 2811–2822.
  10. Vinson Joshua, S.; Selwin Mich Priyadharson, A.; Kannadasan, R.; Ahmad Khan, A.; Lawanont, W.; Ahmed Khan, F.; Ur Rehman, A.; Junaid Ali, M. Crop Yield Prediction Using Machine Learning Approaches on a Wide Spectrum. Comput. Mater. Contin. 2022, 72, 5663–5679.
  11. Jayashankar, P.; Nilakanta, S.; Johnston, W.J.; Gill, P.; Burres, R. IoT Adoption in Agriculture: The Role of Trust, Perceived Value and Risk. J. Bus. Ind. Mark. 2018, 33, 804–821.
  12. Mehta, Y. Agriculture Internet of Things (IoT) Technology. Applications. Available online: http://iotworm.com/agricultureinternet-of-things-iot-technology-applications (accessed on 24 May 2022).
  13. Scriber, S. Smart Agriculture Sensors: Helping Small Farmers and Positively Impacting Global Issues, Too. Available online: www.mouser.com/applications/smart-agriculture-sensors (accessed on 24 May 2022).
  14. Verdouw, C.; Wolfert, S.; Tekinerdogan, B. Internet of Things in Agriculture. CAB Rev. Perspect. Agric. Vet. Sci. Nutr. Nat. Resour. 2016, 11, 1–12.
  15. Sundmaeker, H.; Verdouw, C.; Wolfert, S.; Freire, L.P.; Vermesan, O.; Friess, P. Internet of Food and Farm 2020. In Digitising the Industry-Internet of Things Connecting Physical, Digital and Virtual Worlds; River Publishers: Gistrup, Denmark, 2016; Volume 129, p. 4.
  16. Pillai, R.; Sivathanu, B. Adoption of Internet of Things (IoT) in the Agriculture Industry Deploying the BRT Framework. Benchmark. Int. J. 2020, 27, 1341–1368.
  17. Singh, S.; Haneef, F.; Kumar, S.; Ongsakul, V. A Framework for Successful IoT Adoption in Agriculture Sector: A Total Interpretive Structural Modelling Approach. J. Glob. Bus. Adv. 2020, 13, 382–403.
  18. Duang-Ek-Anong, S.; Pibulcharoensit, S.; Phongsatha, T. Technology Readiness for Internet of Things (IoT) Adoption in Smart Farming in Thailand. Int. J. Simul. Syst. Sci. Technol. 2019, 20, 1–6.
  19. Mandari, H.E.; Chong, Y.-L.; Wye, C.-K. The Influence of Government Support and Awareness on Rural Farmers’ Intention to Adopt Mobile Government Services in Tanzania. J. Syst. Inf. Technol. 2017, 19, 42–64.
  20. Lin, D.; Lee, C.K.M.; Tai, W.C. Application of Interpretive Structural Modelling for Analyzing the Factors of IoT Adoption on Supply Chains in the Chinese Agricultural Industry. In Proceedings of the 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore, 10–13 December 2017; pp. 1347–1351.
  21. Venkatesh, V.; Morris, M.G.; Davis, G.B.; Davis, F.D. User Acceptance of Information Technology: Toward A Unified View. MIS Q. 2003, 27, 425–478.
  22. Venkatesh, V.; Thong, J.Y.L.; Xu, X. Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Q. 2012, 36, 157–178.
  23. Cimperman, M.; Brenčič, M.M.; Trkman, P. Analyzing Older Users’ Home Telehealth Services Acceptance Behavior—Applying an Extended UTAUT Model. Int. J. Med. Inform. 2016, 90, 22–31.
  24. de Veer, A.J.E.; Peeters, J.M.; Brabers, A.E.M.; Schellevis, F.G.; Rademakers, J.J.D.J.M.; Francke, A.L. Determinants of the Intention to Use E-Health by Community Dwelling Older People. BMC Health Serv. Res. 2015, 15, 103.
  25. Wang, H.; Tao, D.; Yu, N.; Qu, X. Understanding Consumer Acceptance of Healthcare Wearable Devices: An Integrated Model of UTAUT and TTF. Int. J. Med. Inform. 2020, 139, 104156.
  26. Quaosar, G.M.A.A.; Hoque, M.R.; Bao, Y. Investigating Factors Affecting Elderly’s Intention to Use m-Health Services: An Empirical Study. Telemed. e-Health 2018, 24, 309–314.
  27. Gansser, O.A.; Reich, C.S. A New Acceptance Model for Artificial Intelligence with Extensions to UTAUT2: An Empirical Study in Three Segments of Application. Technol. Soc. 2021, 65, 101535.
  28. Gu, J.; Liu, L. Investigating the Determinants of Users’ Willingness to Pay for Answers on Q&A Platforms. Commun. Comput. Inf. Sci. 2019, 1034, 13–20.
  29. Goo, J.J.; Heo, J.-Y. The Impact of the Regulatory Sandbox on the Fintech Industry, with a Discussion on the Relation between Regulatory Sandboxes and Open Innovation. J. Open Innov. Technol. Mark. Complex. 2020, 6, 43.
  30. Chong, A.Y.; Ooi, K. Adoption of Interorganizational System Standards in Supply Chains: An Empirical Analysis of RosettaNet Standards. Ind. Manag. Data Syst. 2008, 108, 529–547.
  31. Marakarkandy, B.; Yajnik, N.; Dasgupta, C. Enabling Internet Banking Adoption: An Empirical Examination with an Augmented Technology Acceptance Model (TAM). J. Enterp. Inf. Manag. 2017, 30, 263–294.
  32. Alaiad, A.; Zhou, L.; Koru, G. An Exploratory Study of Home Healthcare Robots Adoption Applying the UTAUT Model. Int. J. Healthc. Inf. Syst. Inform. IJHISI 2014, 9, 44–59.
  33. Boontarig, W.; Chutimaskul, W.; Chongsuphajaisiddhi, V.; Papasratorn, B. Factors Influencing the Thai Elderly Intention to Use Smartphone for E-Health Services. In Proceedings of the 2012 IEEE Symposium on Humanities, Science and Engineering Research, Kuala Lumpur, Malaysia, 24–27 June 2012; pp. 479–483.
  34. Alam, M.Z.; Hu, W.; Kaium, M.A.; Hoque, M.R.; Alam, M.M.D. Understanding the Determinants of MHealth Apps Adoption in Bangladesh: A SEM-Neural Network Approach. Technol. Soc. 2020, 61, 101255.
  35. Dwivedi, Y.K.; Shareef, M.A.; Simintiras, A.C.; Lal, B.; Weerakkody, V. A Generalised Adoption Model for Services: A Cross-Country Comparison of Mobile Health (m-Health). Gov. Inf. Q. 2016, 33, 174–187.
  36. Mital, M.; Chang, V.; Choudhary, P.; Papa, A.; Pani, A.K. Adoption of Internet of Things in India: A Test of Competing Models Using a Structured Equation Modeling Approach. Technol. Forecast. Soc. Chang. 2018, 136, 339–346.
  37. Bozan, K.; Parker, K.; Davey, B. A Closer Look at the Social Influence Construct in the UTAUT Model: An Institutional Theory Based Approach to Investigate Health IT Adoption Patterns of the Elderly. In Proceedings of the 2016 49th Hawaii International Conference on System Sciences (HICSS), Koloa, HI, USA, 5–8 January 2016; pp. 3105–3114.
  38. Okumus, B.; Ali, F.; Bilgihan, A.; Ozturk, A.B. Psychological Factors Influencing Customers’ Acceptance of Smartphone Diet Apps When Ordering Food at Restaurants. Int. J. Hosp. Manag. 2018, 72, 67–77.
  39. Hoque, R.; Sorwar, G. Understanding Factors Influencing the Adoption of MHealth by the Elderly: An Extension of the UTAUT Model. Int. J. Med. Inform. 2017, 101, 75–84.
  40. Kim, D.; Chun, H.; Lee, H. Determining the Factors That Influence College Students’ Adoption of Smartphones. J. Assoc. Inf. Sci. Technol. 2014, 65, 578–588.
  41. Baabdullah, A.M. Consumer Adoption of Mobile Social Network Games (M-SNGs) in Saudi Arabia: The Role of Social Influence, Hedonic Motivation and Trust. Technol. Soc. 2018, 53, 91–102.
  42. Alalwan, A.A.; Dwivedi, Y.K.; Rana, N.P.P.; Williams, M.D. Consumer Adoption of Mobile Banking in Jordan. J. Enterp. Inf. Manag. 2016, 29, 118–139.
  43. Hew, J.-J.; Lee, V.-H.; Ooi, K.-B.; Wei, J. What Catalyses Mobile Apps Usage Intention: An Empirical Analysis. Ind. Manag. Data Syst. 2015, 115, 1269–1291.
  44. Or, C.K.L.; Karsh, B.-T.; Severtson, D.J.; Burke, L.J.; Brown, R.L.; Brennan, P.F. Factors Affecting Home Care Patients’ Acceptance of a Web-Based Interactive Self-Management Technology. J. Am. Med. Inform. Assoc. 2011, 18, 51–59.
  45. Alalwan, A.A.; Dwivedi, Y.K.; Rana, N.P. Factors Influencing Adoption of Mobile Banking by Jordanian Bank Customers: Extending UTAUT2 with Trust. Int. J. Inf. Manag. 2017, 37, 99–110.
  46. Shareef, M.A.; Dwivedi, Y.K.; Kumar, V.; Kumar, U. Content Design of Advertisement for Consumer Exposure: Mobile Marketing through Short Messaging Service. Int. J. Inf. Manag. 2017, 37, 257–268.
  47. Zhang, X.; Lai, K.; Guo, X. Promoting China’s Mhealth Market: A Policy Perspective. Health Policy Technol. 2017, 6, 383–388.
  48. Kabra, G.; Ramesh, A.; Akhtar, P.; Dash, M.K. Understanding Behavioural Intention to Use Information Technology: Insights from Humanitarian Practitioners. Telemat. Inform. 2017, 34, 1250–1261.
  49. Khalilzadeh, J.; Ozturk, A.B.; Bilgihan, A. Security-Related Factors in Extended UTAUT Model for NFC Based Mobile Payment in the Restaurant Industry. Comput. Hum. Behav. 2017, 70, 460–474.
  50. Chao, C.-M. Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model. Front. Psychol. 2019, 10, 1652.
  51. Jiang, X.; Yu, W.; Li, W.; Guo, J.; Chen, X.; Guo, H.; Wang, W.; Chen, T. Factors Affecting the Acceptance and Willingness-to-Pay of End-Users: A Survey Analysis on Automated Vehicles. Sustainability 2021, 13, 13272.
  52. Alalwan, A.A. Mobile Food Ordering Apps: An Empirical Study of the Factors Affecting Customer e-Satisfaction and Continued Intention to Reuse. Int. J. Inf. Manag. 2020, 50, 28–44.
  53. Shaw, N.; Sergueeva, K. The Non-Monetary Benefits of Mobile Commerce: Extending UTAUT2 with Perceived Value. Int. J. Inf. Manag. 2019, 45, 44–55.
  54. Lu, J.; Yao, J.E.; Yu, C.-S. Personal Innovativeness, Social Influences and Adoption of Wireless Internet Services via Mobile Technology. J. Strateg. Inf. Syst. 2005, 14, 245–268.
  55. Schweitzer, F.; van den Hende, E.A. To Be or Not to Be in Thrall to the March of Smart Products. Psychol. Mark. 2016, 33, 830–842.
  56. Ahn, M.; Kang, J.; Hustvedt, G. A Model of Sustainable Household Technology Acceptance. Int. J. Consum. Stud. 2016, 40, 83–91.
  57. Zhang, L.; Chen, L.; Wu, Z.; Zhang, S.; Song, H. Investigating Young Consumers’ Purchasing Intention of Green Housing in China. Sustainability 2018, 10, 1044.
  58. Morosan, C.; DeFranco, A. When Tradition Meets the New Technology: An Examination of the Antecedents of Attitudes and Intentions to Use Mobile Devices in Private Clubs. Int. J. Hosp. Manag. 2014, 42, 126–136.
  59. Hu, Z.; Ding, S.; Li, S.; Chen, L.; Yang, S. Adoption Intention of Fintech Services for Bank Users: An Empirical Examination with an Extended Technology Acceptance Model. Symmetry 2019, 11, 340.
  60. Leckie, C.; Nyadzayo, M.W.; Johnson, L.W. Promoting Brand Engagement Behaviors and Loyalty through Perceived Service Value and Innovativeness. J. Serv. Mark. 2017, 32, 70–82.
  61. O’cass, A.; Carlson, J. An E-Retailing Assessment of Perceived Website-Service Innovativeness: Implications for Website Quality Evaluations, Trust, Loyalty and Word of Mouth. Australas. Mark. J. AMJ 2012, 20, 28–36.
  62. Franceschinis, C.; Thiene, M.; Scarpa, R.; Rose, J.; Moretto, M.; Cavalli, R. Adoption of Renewable Heating Systems: An Empirical Test of the Diffusion of Innovation Theory. Energy 2017, 125, 313–326.
  63. Milovantseva, N. Are American Households Willing to Pay a Premium for Greening Consumption of Information and Communication Technologies? J. Clean Prod. 2016, 127, 282–288.
  64. Ramos-Real, F.J.; Ramírez-Díaz, A.; Marrero, G.A.; Perez, Y. Willingness to Pay for Electric Vehicles in Island Regions: The Case of Tenerife (Canary Islands). Renew. Sustain. Energy Rev. 2018, 98, 140–149.
  65. Homburg, C.; Koschate, N.; Hoyer, W.D. Do Satisfied Customers Really Pay More? A Study of the Relationship between Customer Satisfaction and Willingness to Pay. J. Mark. 2005, 69, 84–96.
  66. Sukhu, A.; Bilgihan, A.; Seo, S. Willingness to Pay in Negative Restaurant Service Encounters. Int. J. Hosp. Manag. 2017, 65, 11–19.
  67. Li, Y.; Zhao, M. A Study on the Influencing Factors of Continued Intention to Use MOOCs: UTAUT Model and CCC Moderating Effect. Front. Psychol. 2021, 12, 528259.
  68. Abu Bakar, A.R.; Ahmed, Z.U. Technology Motivation in E-Marketing Adoption among Malaysian Manufacturers. J. Transnatl. Manag. 2015, 20, 126–152.
  69. Humida, T.; al Mamun, M.H.; Keikhosrokiani, P. Predicting Behavioral Intention to Use E-Learning System: A Case-Study in Begum Rokeya University, Rangpur, Bangladesh. Educ. Inf. Technol. 2021, 27, 2241–2265.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
This entry is offline, you can click here to edit this entry!
Feedback