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The milk is an indispensible requirement of human life, as it cannot be perfectly substituted. Milk serves as a base raw material for other products of dairy like ice cream, yoghurt, cheese, cottage cheese, butter, buttermilk, etc. These are obtained by processing the milk at different stages and at different forms (Muehlhoff et al., 2013). Due to the ubiquity of milk and related products across the globe, the dairy industry has been an important research area for the researchers from veterinary (science), engineering and management background. The more interest inclination has been observed since the intervention of technology in the dairy processing. With the beginning of 21st century, technology adoption took the focus of the studies, as a major future prospect for dairy operations. Since then, with the help of surveys, pilot studies, case studies, many researchers tried to systematically analyse and prove technology adoption as a benchmark tactic for enhancing productivity, management and overall efficiency of the dairy farm. Later, parallel progress of technology involvement in dairy industry has been observed with the technology advancements. Several new terms and concepts in technology including some general and some customized ones for dairy technology were introduced, discussed and experimented to prove the positive results. Therefore this paper is an attempt to showcase the global development in the research area of technology adoption in dairy industry.

As the largest producer and consumer of milk in the world, India consumes almost its whole milk production. Dairy sector in India has been a significant contributor to the gross domestic product (GDP) with a value of approximately 26,000 million USD (Deshmukh, 2014). The dairy market review of March 2020 (assessing 2018-19 production) reported significant growth of 1.4% producing 852 million tonnes of milk production across the globe with India achieving rank 1. In Asian context, over 90% of total output is coming from India and Pakistan that is compensating the decline in other Asian countries. India has shown a positive growth of 4.5% from 2017-18 levels. Initially in 1970, milk production in India was only limited to 20 million tonnes. In 2018-19, the country produced 187.7 with per capita availability of 394 gms/day as compared to 176.4 million tonnes in 2017-18 and 165.4 million tonnes in 2016-17. This made India continue to become the largest milk producing country followed by European Union. The World dairy situation report 2019 by International Dairy Federation (IDF) indicated science-based expertise as an improvement area where plays an important role. Dairy products quality, healthy animals, farm productivity and sustainability are the dairy industry key drivers. There is a need of continuous improvement in order to provide nutritious, safe and sustainable dairy. More governments are also required to contribute and positively promote dairy at various International platforms like W.H.O., FAO, UN and Codex meetings. But the rise in the milk production level is at a slower pace in comparison with the rise in the demand Nozaki (2017). The milk co-operatives society system of milk supply in India is a popular across the globe. Various studies have pointed out the need for technology adoption in dairy farming methods from procurement to end consumers covering every supply chain level. National Dairy Development Board (NDDB) promotes the farmers training related to technology adoption. Genetic technology is discussed and Internet based dairy information system (i-DIS) is promoted by NDDB for milk co-operatives in order to provide mutual benefits (NDDB Annual report, 16-17). At global level, primarily in U.S.A., the dairy sector talks about technology involvement in the form of a concept called precision dairy farming (Schroeder, 2015). It involves a fully mechanized process of milking the animal till the task of procurement, testing, manufacturing and packaging. It has been indicated in that the future of the dairy sector lies in the hands of technology to bring out operational efficiency and value deliverance and the sector is driven effectively by the technology (Shah, 2001). Nozaki (2017) felt that in order to meet the constant increase in the milk demand, India has to channelize its unorganized milk sector into the organized one. As per the National Dairy Plan by NDDB, India is aiming to reach 64% of its milk production through organized sector by the end of 2023. The most common issue in the dairy sector is the milk procurement due to animal productivity i.e. yield of the bovines (Milking animals). NDDB with the help of Ministry of Animal husbandry and fisheries GoI is working to improve the health related and breeding related issues of animals with the help of Radio frequency identification (RFID) tags and artificial insemination technologies respectively. Jadawala and Patel (2018) suggested the need and pointed the importance of disruptive ICT (Information and Communication technology) based ERP (Enterprise Resource Planning) in the dairy sector especially at the cattle farming stage. Similarly, Vaughan J G Higgins (2007) analysed the dairy sector issues and suggested to implement the computer based dairy decision support system. Various researches across the globe have emphasized upon the technology adoption in the dairy industry. There are papers and articles related to technology adoption in dairy farms and dairy industry as a whole. Some concerning with technology adoption or role of technology in dairy sector and some papers were focusing only upon the factors affecting technology adoption and management. There are several concepts, terms and factors related to the technology adoption came out till now that are discussed in the form of numerous case studies, success stories, expert reviews, exploratory and conclusive researches. In order to get till-date developments in the area of technology adoption in dairy industry, there is a need to get such concepts and factors at one place. This paper helps to get such studies that show recent developments and can suggest future scope to the researchers working in the area of technology adoption in dairy industry.

The target papers for this structured review were identified through Google and Google scholar database as the key source. The resources like journal, conference papers, articles, and reports for the chosen time period of 2000 to 2019, were explored using search strings by limiting the selection criteria like publication year, dairy industry as the research area and technology adoption/intervention in the dairy industry. Some search strings used for gathering the literature for this review are- Technology adoption in dairy farming, Use of IT/ICT in dairy, Computer enabled dairy management With this, 253 records were found and considered for pre-screening as indicated in screening algorithm (Figure 1). As such, a sample size of 41 papers was taken for the purpose of review. 34 variables/concepts/terms only related to technology in dairy industry were taken out from each paper and reflected in the table with frequency of each term/variable/concept used. After this, the findings of each paper were discussed by dividing all the record into three categories of research- Descriptive, case studies and analytical papers/articles.

Figure 1: Screening process algorithm.

Table 1 show all the selected research papers, articles, and reviews for this study in the ascending order of year published/posted and it also show various types of methods used for data analysis including quantitative methods, qualitative studies and mixture of both methods.

Table 1 List of studies reviewed with methodologies (in ascending order of year published)

Author(s)	Year	Method/Tools	Results Validated
Akkeren and Cavaye	2000	Case Study Using Cross Case Analysis	NO
El-Osta and Morehart	2000	Multinomial logit model	YES
Mathur	2000	Analytical Review	NO
Rangnekar and Thorpe	2001	Workshop Report (including several analytical papers)	___
Shah	2001	Review Article	NO
Millar et al.	2002	Survey based	YES
B. Bowonder et al.	2005	Case Study	NO
Alvarez and Nuthall	2005	Survey with Structural equation model	YES
Higgins	2007	Analytical Review + Case Study	NO
Hyde et al.	2007	Case Study	NO
Padfield	2007	Article	NO
Mitchell	2008	Review Article	NO
**Part 1: Bracke et al. Part 2: Bakker et al.	2009	Assessment Report	NO
Joshi et al.	2009	Case Study with FISM	NO
Quddus	2012	Survey with Standard Linear Regression Model (Logit Model) and Chi-Square	YES
Prasad and Satsangi	2013	Case Study	NO
Ayag et al.	2013	Fuzzy QFD, Multi-objective mathematical programming using survey + Case study (for illustration)	YES
Keyserlingk et al.	2013	Invited Review	NO
Petare	2013	Review Article	NO
Gillespie et al.	2014	Survey With Logit Model	YES
Nalla	2014	Personal Reflections	NO
Subburaj et al.	2015	Survey based	NO
McDonald et al.	2015	Survey with Multivariate analysis	YES
Deshmukh et al.	2015	Analytical Review	NO
Schroeder	2015	Article	NO
Bhardwaj et al.	2016	Pilot Study	NO
Galstyan and Harutyunyan	2016	Survey with Cross-Sectional Qualitative Research	YES
Poranki	2016	Case Study	NO
Kongo	2016	Conference Presentation	NO
Randhawa	2016	Conference Presentation	NO
Samaranayake and Laosirihongthong	2016	Unitary structure model and fuzzy approach Fuzzy-Based Decision Support System	YES
Ghadge et al.	2017	Survey with AHP And Sensitivity Analysis	YES
Suhaimi et al.	2017	Descriptive With Multi-Directional Efficiency Analysis And Single-Bootstrap Truncated Regression Model	YES
Nehring et al.	2017	Translog Stochastic Production Frontier (SPF)	YES
Vate-U-Lan et al.	2017	Case Study	NO
Jadawala and Patel	2018	Personal experience from Field study	NO
Mor, et al.	2018a	ISM Based Study (Exploratory study)	NO
Mor, et al.	2018b	Structured Literature Review	NO
Gargiulo et al.	2018	Survey based	YES
Husain	2018	Analytical	NO
Burkitbayeva et al.	2019	Survey with time series data, Interviews	YES

 ** There are two parts in this single report by different set of authors. Although as a record of this study the report is treated as a single sample but in identification of concepts/terms both parts within it are taken separately.
 Concepts and terms associated with technology adoption in dairy industry
Table 2 show the list of all the introduced concepts and terms been attached to the issue of technology adoption in dairy industry. 

Table 2 Technology related concepts discussed in various sources

The studies are divided into three broad categories namely descriptive studies that include certain sample size based studies, case studies with reference to a geographical zone, firm or industry and analytical studies that essentially involve research articles, personal reflections of various domain experts, industry reports, annual reports, workshop reports, etc.
Table 3 Distribution of Studies

Paper category	Number of studies (Frequency)	Percentage
Descriptive studies*	13	31.70%
Case Studies	10	24.40%
Analytical studies	18	43.90%
Total	41	100%

Discussion about Descriptive Studies 
*Also includes exploratory studies or pilot surveys.
Millar et al. (2002) conducted a survey to know the consumer attitudes towards using two different types of understudied technologies viz. automatic milking systems (AMS) and Bovine somatotrophin (bST). The interesting outcomes came out, as there were fewer responses related to general opinions about technology and its contribution/role but the answers were in the form of “ethically acceptable” and “unethical/ethically unacceptable” technologies. AMS was considered ethically acceptable over bST technology.
A survey of 180 small dairy cattle farm holders in Bangladesh revealed the unsatisfactory practices and high constraints in adoption of dairy farming technologies. The researcher took suggestions from farmers in order to make the better adoption rate and therefore training, technical assistance, timely receiving of medicines, increasing AI facilities, developing extension services were the main suggestions received from farmers (Quddus, 2012).
Alvarez and Nuthall (2005) studied the dairy farmers attributes regarding adoption of computer based information system. The paper proves that farmer characteristics are crucial for successful computer use and suggested that since each farmer will have different characteristics, the differences can be minimized through the proper software designing and providing support and training.
Ayag et al. (2013) used fuzzy quality function deployment (QFD) approach for the determination of supply chain management strategies based on literature review and interview with industrial customers of dairy and listed variables under logistics and supply chain management strategies and customer requirements and conducted a case study of Turkish dairy industry in order to illustrate the proposed approach.
Subburaj et al. (2015) conducted literature surveys, field study and included their own experiences in order to study about strengthening the operational efficiency of Tamil Nadu state’s dairy supply chain. On the basis of comparison with Gujarat Co-operative Milk Marketing Federation (AMUL), the paper suggested five key focus areas for improvement in Tamil Nadu Co-operative Milk Producers Federation- strengthening cooperative societies, making special dairy zones, establishing dynamic milk procurement method, increasing fodder productivity and creating of feed banks and, an integrated animal health plan by using information technology.
McDonald et al. (2015) measured the perception of usefulness and perception to technology ease-of-use in three types of technologies viz-financial management technology, grassland measurement technology and artificial insemination. The main objective of this paper was to analyse the variables affecting the technology adoption decision by newly entering farms in the dairy business. The artificial insemination and farm-financial management technology have shown larger scores as compared to grassland measurement.
A pilot study conducted by Bhardwaj et al. (2016) investigated the supply chain dynamics in Indian dairy industry. The major finding of the study reveals that the dairy sector needs some significant improvements in the areas of information systems, innovations, development of the infrastructure, traceability and technology-process integration in order to improve consistency and quality and safety standards to achieve the long-term co-operative goal.
Galstyan and Harutyunyan (2016) specifically focused on the adoption of hazard analysis critical control point based food safety management system (HACCP FSMS) technology in the dairy processing companies of Armenia using cross-sectional qualitative research design. The research reported that most frequent drivers of adoption were found to be increased export opportunities, broader accountability, enhanced traceability and improved organizational image. The barriers were high investment costs, excessive documentation, value incompatibility, and insufficient physical and technological infrastructures.
Suhaimi et al. (2017) used a multi-directional efficiency analysis for measuring the technical inefficiency score of 200 farms under Malaysian dairy industry and also used single-bootstrap truncated regression model to explain the factors leading to technical inefficiency. The study found less number of intensive and semi-intensive farms with full efficiency. The suggestion was made to government for providing technical assistance and finances.
Ghadge et al. (2017) identified the drivers and barriers for improving the environmental sustainability performance with reference to Small and Medium Enterprises of Greek dairy supply chain. The paper further uses analytical hierarchy process (AHP) and sensitivity analysis for understanding the complex nature of the affecting factors. The paper highlights that competition has led the interest of players to move for eco-friendly technologies in transportation as well as production for gaining market share and differentiate from others.
A comparative study of two breeding technologies namely crossbred and non-crossbred across US dairy farms reveal that highly efficient and managed crossbred herds are better on a financial basis as compared to the one with non-crossbred technologies. This paper entirely talks about the insemination related technology and analysis various dimension with respect to it (Nehring, 2017).
Gargiulo et al. (2018) proved the positive relationship between large herd size and adoption of precision dairy farming technologies. The present precision technology adoption status and future perception of this adoption were taken as important dimensions of this study.
With the belief that technology adoption do contribute to the dairy productivity and enhance the welfare of poor farmers, Burkitbayeva et al. (2019) compared the technology adoption level in dairy farms of Punjab state at two different time periods (2008 and 2015). The paper concludes that there was a significant improvement in the technology adoption at lower level farmers but no improvements in expanding the usage of technology by middle and high level players. With the adoption of modern technologies like machinery-operated process, the productivity and satisfaction of farmers showed a positive growth. Despite of technology facilitating the value-chain integration, a gap was found in the vertical coordination in the value-chain and it displays a room for improvement for further technology adoption. 
Discussion about Case Studies
The enablers and challenges in adoption of technology differ from business to business. The impact of variables is not same in large and small-scale businesses (Akkeren and Cavaye, 2000). This paper uses three case studies to show how Australian small businesses (SME) are slow in IT adoption due to distrust, lack of awareness to business benefit and lack of understanding Internet technologies. Finally the paper sums up with a model indicating that how various variables are affecting the technology adoption by SMEs.
A case study on adoption of robotic milking system (RMS) technology was conducted in order to reduce the laborers from Mason Dixon dairy farm at Pennsylvania. The adoption decision was compared with purchasing carousel-style parlors using capital budgeting analysis (Hyde et al., 2007).
On the basis of a case study, Higgins (2007) analysed the relevance of a computer-based dairy decision support system software in the performance building and improving decision-making approach to boost profits. In order to map the actual effects of the technology implementation, the paper highlighted the importance of pre-existing knowledge of users about existing farm management and adopting or using technology.
The ICT application in rural areas of Gujarat state by AMUL has contributed to the performance efficiency from testing milk to maintaining data to cash payment given to the farmers. This case paper indicates that like AMUL, if ICT is properly designed, implemented and managed, the rural people as well as the dairy business will be benefited (Bowonder et al., 2003). Another case study based on the success story of AMUL by Prasad and Satsangi (2013) analysed the relationship between the organizational design and operational efficiency achieved by AMUL on the basis of surveying farmers and employees associated with AMUL. The paper suggests that more the policy makers are open and adaptive to changing technology more the efficiency is achieved. 
Kongo (2016) explained that smallholders in the dairy industry are the major income contributor for the local economy and achieving sustainability with socio-economic challenges is a big concern for such industry. This case paper shows that how the research is trying to help this industry in order to strive and survive to socio-economic challenges, adopt technology and innovation, product development and eco-friendly practices for sustainability.
Poranki (2016) conducted a case study of a dairy foods company for the purpose of illustrating the issues and suggestions for building efficient supply chain practices in the Indian dairy industry. The study suggested that with the adoption of IT-based technologies and EDI in supply chains could bring the efficiency.
Another paper in Indian context reveals that as sustainability aspect in dairy farming is emerging in India with expected high demands for dairy products, the significant opportunities through technological innovations should be further created to enhance the income of rural-based dairy systems for reducing poverty and to bring down deterioration of soil health. Thus, technological innovations and ability to transfer technology from labs to fields in dairy farming system is must to bring down the production costs for greater economic returns to the farmers (Randhawa, 2016).
Vate-u-lan et al. (2017) reported a case study of farmers who adopted smart dairy farming in Ontario (Canada) that uses the IoT technology in its operations like digital cow tracking, sensor enabled fodder crop management, digitally signalled births, genomic testing, etc. The technology aids in data gathering, enhance quality levels production and also environment protection.
Mor et al. (2018a) aimed at identifying barriers in dairy operational supply chain using ISM framework. The major barrier is at the base of the model and it was found to be out dated technology and lack of automation in the processes.
Discussion about Analytical studies (including articles, personal reviews/reflections, etc.)
On the basis of USDA’s farm costs and returns survey data, El-Osta and Morehart (2000) [9] analysed the decisions related to technology adoption in the production of dairy and related products with the role of herd expansion. The results revealed that size of operation, usage of hired labour, education, age, credit reserves and specialization in dairy production are crucial for increasing the chances and decision making related to adopting a capital and management-intense technology.
Since the beginning of 21st century, there entered a period of technology revolution. The technology was almost entering into most of the businesses. Likewise for the Indian Dairy Industry, it was realized by Mathur (2000)  that the efforts should be made for adoption of modern technologies such as UHT, AI, computerized operations and establishing automated plants in order to enhance productivity, cost reduction, quality management.
An NDDB workshop report (Rangnekar and Thorpe, 2001) on the opportunities and constraints of smallholder dairy production and marketing, included papers from across the globe about the existing scenario, upcoming trends, prospects and issues in dairy farming. The technology adoption (specially AI), landholdings, funds availability and healthcare management were some of the highlighted constraints.
Shah (2001) analysed that scientific production techniques and farm management led to a positive transformation in Indian dairy industry and has a long way to go for further opportunities.
The article by Padfield (2007) discusses the application of the Interherd herd management software in the farm and discussed that how it resulted in the impressive performance statistics. Mitchell (2008) emphasized on the data based facts of US farms that installing RFID tags, pedometers and using sensor-based technologies have enabled to keep a track on bovine health such as heart rate, stomach pH, temperature and thus, leading to better heath monitoring and ultimately achieving better farm output.
Joshi et al. (2009) used fuzzy ISM (Interpretive structural modelling) approach to model the barriers/inhibitors in the cold chain management (dairy logistics is included under the domain of cold chain) in Indian context. The lack of inadequate infrastructure, top management commitment and high implementation costs came out to be major barriers.
Part 1 of animal welfare report (European context) by Bracke et al. (2009), concluded that there should be appropriate animal welfare policies, intensive farm husbandry systems, scientific and technological state of the art as the operational efficiency and cost reduction both have become crucial drivers for developing housing and management systems for farm animals. Part 2 (Bakker, et al., 2009) carried forward the farm welfare approach and its assessment by considering various threats and opportunities while adopting welfare methods in the farms. It concludes with a suggestion that adopting animal-welfare assessment system in the farms will be a competitive advantage and a long run cost cutting measure for EU member states.
Petare (2013) focused on the issues and challenges at every dairy supply chain level like- smallholder, collection level, processing, storage & logistic, co-operative level and marketing level. At the base level i.e. the smallholder level, he identified some issues that occur due to non-adoption of technology like lack of chilling capacities, health monitoring problems, low genetic potential, and payment delays.
The application of modern technologies such as ICT adoption, artificial insemination, RFID tagging, ERP, online payments, procurement, marketing; record keeping, installing automated milk collection units, GIS enabled tracking in the logistics part of the supply chain could help the Indian dairy industry to redefine and reshape the value chain. Time-to-time innovation is necessary for continuous improvement (Nalla, 2014).
Gillespie et al. (2014) estimated the adoption rates of nineteen dairy based technologies, management practices, and production systems (TMPPS) in U.S. TMPPS has experienced the greatest increase in adoption like automatic computerized take-offs, internet and breeding technologies.
Deshmukh et al. (2015) analysed the feasibility and application of various computer based technologies in modern dairy industry starting from installation of RFID tags at farms to procurement stage, automation, milk testing, MIS, electronic payments, price determination and GPS based tracking. The article concluded that customization of IT platforms is a major opportunity for change in dairy industry in order to achieve desired goals.
Precision dairy farming is the technology that measures and analyses behavioural, physiological and production indicators in animals. It involves automation in processes like feeding and milking, data updating and record keeping and health monitoring of milching units. It offers better ways of monitoring and improving the animal well-being in order to save labour dependence, cost and better life expectancy of milching units. Thus, precision technologies will be driving the dairy industry progress in the future (Schroeder, 2015).
Samaranayake and Laosirihongthong (2016) state that the supply chain process quality (including inventories to customer deliveries) and delivery performance is essential for the industries like dairy due perishable aspect of commodities. Therefore innovative technologies such as efficient ERP with performance management and monitoring methods can serve the purpose of ensuring supply chain delivery performance.
New disruptive ICT technologies like RFID tags, internet of things (IoT), mobile applications, payment gateways for ensuring timely and fair remuneration to farmers and using GIS for land management, weather prediction, etc. with ERP in collecting real time data in cattle farms helps in reducing cost, improving milk production, animal breeding, animal health and overall efficiency. It also has capability of performing technological inclusion of the cattle farms and it can modernize the whole Indian dairy industry by starting new growth era (Jadawala et al., 2018).
The SLR of published articles for the past eleven years was taken by focusing on dairy supply chain management. The paper concluded that the quality, safety and attached economic benefits in dairy industry could be achieved with the help of technological innovation, uncertainties removal and introducing the global SCM practices into the lean and green initiatives (Mor et al., 2018b)
Husain (2018) suggested that Indian dairy should promote strategic partnerships with giant players in order to improve technological assistance mainly IoT in its operations in order to transform itself to a better situation and lead towards innovation.
In an invited review, Keyserlingk, et al. (2013) presented several major factors that affect the future of sustainability of the US dairy industry like climate change, rapid scientific and technological innovation and advances, globalization and lack of multidisciplinary research initiatives. There is a requirement of the quality of life of workers and the animals in dairy farms and public input for the acceptability of new technologies. 

1. Subburaj, M., Babu, T.R. and Subramonian, B.S. (2015), “A Study on Strengthening the Operational Efficiency of Dairy Supply Chain in Tamil Nadu, India”, XVIII Annual International Conference of the Society of Operations Management (SOM-14), Vol. 189, pp. 285-291. 2. Bhardwaj, A., Mor, R.S., Singh, S., and Dev, M. (2016), “An investigation into the dynamics of supply chain practices in Dairy industry: A Pilot Study”, Proceedings of the 2016 International Conference on Industrial Engineering and Operations Management Detroit (pp.1360-1365), Michigan, U.S.A. 3. Prasad, R. and Satsangi, R. (2013), “A case study of AMUL co-operative in India in relation to organizational design and operational efficiency”, International Journal of Scientific & Engineering Research Vol. 4, Issue: 1, pp.1-9. 4. Jadawala, R. and Patel, S. (2018), “Implications of disruptive ICT base ERP in dairy industry
(in the aspects of milk co-operatives and cattle farms)”, Journal of Emerging Technologies and Innovative Research, Vol. 5, Issue: 10, pp.331-336. 5. Mor, R.S., Bhardwaj, A. and Singh, A. (2018a), “Benchmarking the interactions among barriers in dairy supply chain: An ISM approach”, International Journal for quality research, Vol. 12 Issue: 2, pp.385-404. 6. Akkeren, J.K.V., Cavaye, A.L.M. (2000), "Factors affecting entry‐level Internet technology adoption by small business in Australia ‐ evidence from three cases", Journal of Systems and Information Technology, Vol. 3 Issue: 2, pp.33-48. 7. Ayag ̆, Z., Samanlioglu, F. and Büyüközkan, G. (2013), “A fuzzy QFD approach to determine supply chain management strategies in the dairy industry”, Journal of Intelligent Manufacturing, 24, pp.1111–1122. 8. Mor, R.S., Bhardwaj, A. and Singh, S. (2018b), “A structured-literature-review of the supply chain practices in dairy industry”, Journal of Operations and Supply Chain Management, Vol. 11, Issue: 1, pp.12-25. 9. El-Osta, H.S. and Morehart, M. J. (2000), “Technology Adoption and Its Impact on Production Performance of Dairy Operations”, Review of Agricultural Economics, Vol. 22, pp.84-95 10. Quddus, M.A. (2012), “Adoption of dairy farming technologies by small farm holders: practices and constraints”, Bangladesh journal of Animal Science, Vol. 41, Issue: 2, pp.124-135. 11. Galstyan, S.H., Harutyunyan, T.L. (2016), "Barriers and facilitators of HACCP adoption in the Armenian dairy industry", British Food Journal, Vol. 118 Issue: 11, pp.2676-2691. 12. Deshmukh, M.A., Chopde, S.S., Kalyankar, S.D. and Kele, V.D. (2015), “Computer Applications in Dairy Industry”, Oriental journal of computer science & technology, vol. 8, Issue: 1, pp.24-34. 13. Higgins, V. (2007), “Performing Users: The Case of a Computer-Based Dairy Decision-Support System”, Science Technology & Human Values, Vol. 32, No. 3, pp.263-286. 14. Hyde, J., Dunn, J.W., Steward, A. and Hollabaugh, E.R. (2007), “Review of Agricultural Economics”, Vol. 29, No. 2, pp.366-380. 15. Alvarez, J. and Nuthall, P. (2006), “Adoption of computer based information systems: The case of dairy farmers in Canterbury, NZ, and Florida, Uruguay”, Computers and Electronics in Agriculture, 50(1), 48-60. 16. Rangnekar, D. and Thorpe, W. (March, 2001). Smallholder dairy production and marketing—Opportunities and constraints. Proceedings of a South–South workshop held at National Dairy Development Board (NDDB) Anand, India. 17. Mathur, B.N. (2000), “Current Problems and Challenges Confronting the Dairy Industry in India”, Asian-Australasian Journal of Animal Sciences, Vol. 13, pp.447-452. 18. Keyserlingk, M.A.G.V., Martin, N.P., Kebreab, E., Knowlton, K.F., Grant, R.J., Stephenson, M., Sniffen, C.J., Harner, J.P., Wright, A.D. and Smith, S.I. (2013), “Invited review: Sustainability of the US dairy industry”, Journal of Dairy Science, Vol. 96, pp.5405-5425. 19. Millar, K.M. et al., (2002), "Consumer attitudes to the use of two dairy technologies", British Food Journal, Vol. 104 Issue: 1, pp.31-44. 20. Gillespie, J., Nehring, R. and Sitienei, I. (2014), “The adoption of technologies, management practices and production systems in U.S. milk Production”, Journal of Agricultural and food economics, Vol. 2, Issue: 17, pp.1-24. 21. Mitchell, R.L. (2008), “Dairy farmers are milking wireless, RFID and sensor technologies to keep herds fat, happy and profitable”, Computer world magazine article, pp.22-27. 22. Science and Technology Options Assessment (2009), Animal-based Welfare Monitoring Final Report (Report No. IP/A/STOA/FWC2005-28/SC28/40). European Parliament. Retrieved from https://edepot.wur.nl/8825. 23. Ghadge, A., Kaklamanou, M., Choudhary, S. and Bourlakis, M. (2017), "Implementing environmental practices within the Greek dairy supply chain: Drivers and barriers for SMEs", Industrial Management & Data Systems, Vol. 117 Issue: 9, pp.1995-2014. 24. Joshi R, Banwet, D.K. and Shankar, R. (2009), "Indian cold chain: modeling the inhibitors", British Food Journal, Vol. 111 Issue: 11, pp.1260-1283. 25. Bowonder, B., Prasad, B.R.R. and Kotla, A. (2003), “ICT application in a dairy industry: The e-experience of AMUL”. Unpublished work. 26. Shah, D. (2001), “Indian dairy industry: present status and future prospects”, Article on productivity, Vol. 42, No. 3, pp.474-483. 27. Petare, P.A. (2013), “Issues and Challenges of Supply Chain Management with perspective to Indian Dairy Industry”, Proceedings of International Conference on Issues and Challenges in Current Global Economy: It’s Impact on Commerce, Engineering and Technology, Vol. 2. 28. Suhaimi, N.A.B.M., Mey, Y.D. and Lansin, A.O. (2017), “Measuring and explaining multi-directional inefficiency in the Malaysian dairy industry”, British Food Journal, Vol. 119 No. 12, 
pp. 2788-2803. 29. Schroeder, J.W. (2015, May 12), “Dairy Focus: Technology Will Drive Dairy Industry Progress”. Retrieved from https://www.agweb.com/article/dairy-focus-technology-will-drive-dairy-industry-progress-naa-university-news-release/. 30. Nalla, V. (2017, March 5), “Digital Dairy Management” Retrieved from https://in.linkedin.com/in/vivekanand-nalla-4b1a9728. 31. Padfield, E. (2007). Interherd keeps tabs on performance. Retrieved from https://www.fwi.co.uk/business/interherd-keeps-tabs-on-performance. 32. Gargiulo, J. I., Eastwood, C. R., Garcia, S. C. and Lyons, N. A. (2018), “Dairy farmers with larger herd sizes adopt more precision dairy technologies”, Journal of dairy science, 101(6), pp.5466-5473. 33. Nehring, R. Barton, R. and Charles Hallahan, (2017), "The economics and productivity of
US dairy farms that use crossbred vs non-crossbred breeding technology (production systems)", Agricultural Finance Review, Vol. 77 Issue: 2, pp.275-294. 34. Poranki, K.R. (2016), “The efficient supply chain practices in Indian dairy industry”, Research journal of social sciences and management, Vol. 6, No. 2, pp.126-132. 35. Lan, P.V.U. (2017), “Internet of things in Agriculture: A case study of smart dairy farming in Ontario, Canada”, Proceedings of 15th International Conference on developing real life learning experiences: Smart education for sustainable development (pp.1-9), Bangkok, Thailand. 36. Husain, B. (2018, July 3), “How IoT can help transform India’s huge dairy market”. Retrieved from https://www.livemint.com/AI/7hcxugZ6qxMi6uJXPvwCwO/How-IoT-can-help-transform-Indias-huge-dairy-market.html. 37. Kongo, J.M. (2016), “Small dairy industry: Socio-economic challenges, sustainability and innovation – A study case”, Journal of Advanced Dairy Research, 4:2 (Suppl) 38. Randhawa, S.S. (2016), “Technological innovations in dairy farming as a tool to boost rural income in Punjab, India”, Journal of Advanced Dairy Research, 4:2 (Suppl) 39. Laosirihongthong, T. and Samaranayake, P. (2016), "Configuration of supply chain integration and delivery performance: Unitary structure model and fuzzy approach", Journal of Modelling in Management, Vol. 11 Issue: 1, pp.43-74. 40. Patel, H. (2007), “Factors Influencing Technology Adoption: A Review”, 8th International Business Information Management Conference, June 20, Dublin (Ireland). Information Management in the Networked Economy: Issues & Solutions. 41. McDonald, R., Heanue, K., Pierce, K. and Horan, B. (2016), “Factors influencing new entrant dairy farmer's decision-making process around technology adoption”. The Journal of Agricultural Education and Extension, 22(2), pp.163-177. 42. Bracke M., Riedstra, K.V., Schepers, F., Ursinus, N., Blokhuis, H., Gerritzen, M. and Gast, E.T. (2009), “Scientific And Technological State-Of-The-Art” 43. Bakker, M.B., Munnichs, G., Evers, A., Haan, M.D., Mil, E.V., Reenen, K.V. and Brom, F. (2009), “Impact Of Animal-Based Welfare Assessment” 44. Chau, P.Y.K. and Paul J.H.H (2001), “Information Technology Acceptance by Individual Professionals: A Model Comparison Approach”, Decision Sciences, Vol. 32, Issue: 4, pp.699-719. 45. Nozaki, Y. (2017), “Future trends of growing demand for milk and dairy products and milk supply in India”, Mitsui & Co. Global Strategic Studies Institute Monthly Report. 46. Burkitbayeva, S., Janssen, E. and Swinnen, J. (2019), “Technology Adoption and Value Chains in Developing Countries: Panel Evidence from Dairy in Punjab”, LICOS Discussion Papers, LICOS - Centre for Institutions and Economic Performance, KU Leuven. 47. Muehlhoff, E., Bennett, A. and McMahon, D. (2013). Milk and dairy products in human nutrition. Food and Agriculture Organization of the United Nations (FAO). 48. Ajzen, I. (1991), The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, pp.179-211. 49. Davis, F. D. (1989), “Perceived usefulness, perceived ease of use, and user acceptance of information technology”, MIS Quarterly, 13(3), 3 pp.19-339. 50. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989), “User acceptance of com- puter technology: A comparison of two theoretical models”, Management Science, 35(8), pp.982-1003. 51. Patel, H. and Connolly, R., (2007), “Factors influencing technology adoption: A review” Conference on Information Management in the Networked Economy: Issues & Solutions, pp.416-431. 52. Food and Agriculture Organization of the United Nations. (2020). Dairy market review 2019. 53. National Dairy Development Board (NDDB) (2019). Annual Report 2018-19. Retrieved from https://www.nddb.coop/sites/default/files/NDDB-AR-2019-ENGLISH-24022020.pdf 54. National Dairy Development Board (NDDB) (2018). Annual Report 2017-18. Retrieved from https://www.nddb.coop/sites/default/files/NDDB_AR_2017-18_eng_new.pdf 55. National Dairy Development Board (NDDB) (2017). Annual Report 2016-17. Retrieved from https://www.nddb.coop/sites/default/files/NDDB_AR_2016-17_Eng.pdf 56. ESADA Congress (2019). The World Dairy Situation. International Dairy Federation. Retrieved from https://agriprofocus.com/upload/post/CAROLINE-EMOND-min_%281%291573130127.pdf