The Indian dairy industry accounts 23 percent of global milk production and contribute 5 percent to national economy (IBEF, 2022). The Indian milk production was 230.58 million tonnes and per capita availability was 459 grams per day per person in the year 2022-23 (PIB, 2025). Milk is complete food due to its nutrient content and had a wide range of consumers (Elango et al., 2023).  Milk and milk products are used daily in the Indian kitchens. They are consumed directly or ingredients in various dishes. The most preferred place for purchasing packed milk is Kirana store (Pai et al., 2018). 

Artificial Intelligence (AIS) is one of the most powerful technologies of today, helping to improve innovation, efficiency, and growth in many fields (Lal et al., 2025). The rise in internet usage, higher Smartphone adoption and evolving consumer preferences in favour of convenient and time-efficient shopping option are providing strong growth of online grocery adoption (Business wire, 2025). However, the ease of order and convenience played important role in online shopping adoption. The discounts, trust and brand loyalty play significant role in online grocery purchase (Kaur, 2022). E-grocery allows customers to fulfil their daily needs without sacrificing time and routine activities (Wijayanto, 2024). In the present era of information centricity, mobile phones (ICT tools) might be pretty helpful for farmers to fetch real-time need-based information (Shukla et al., 2022). Sensors, drones, and precision farming software especially artificial intelligence and machine learning are rapidly being used in agriculture to increase efficiency, production, and sustainability (Lingireddy et al., 2023; Saikia and Saikia 2023). Technologies highlighting smart farming tools such as sensors, drones, robots, IoT, UAVs, GPS and GIS offer the potential for increased productivity and sustainable practices, their field implementation poses significant challenges (Rakhi and Radhakrishnan 2024). The integration of Artificial Intelligence (AI) in farm mechanization represents a transformative shift in modern agriculture, offering unprecedented opportunities for enhancing productivity, efficiency, and sustainability (Kailashkumar and Vijayakumar 2024).

Thus, nowadays the consumers preferred online shopping over offline shopping (Pal & Kumari 2023).  According to IMARC report, the Indian online grocery market size reached USD 11.4 billion in 2024 and will reach to 96.3 billion by 2033 with 25.38 percent CAGR. Mobile app has emerged as most promising tool for the marketers and the study of consumer behaviour towards this technology advancement is essential (Malik et al., 2017) and smartphone applications are referred to as marketing vehicles because they fundamentally alter the customer firm relationships (Abbas et al., 2022). Nguyen et al. (2023) found the Technology Acceptance Model factors (perceived usefulness and perceived ease of use), perceived value and perceived enjoyment had significant effects on attitude towards online purchase. Nonis et al. (2024) found that Perceived ease of use positively affects to attitude whereas perceived useful does not. Further, the Attitude, Subjective norms and perceived behavioural control positively affects purchase intension. The good number of studies related to grocery purchase were found in literature but there were a smaller number of studies which address milk specifically. Since, milk is everyday used product in Indian household the gap was identified to study factors affecting mobile app adoption for online milk purchase. The present study tries to investigate factors influencing mobile app adoption for online milk purchase.

The present study was conducted with the objectives to study factors influencing mobile app adoption for online milk purchase. To fulfill the objectives descriptive research design was adopted. The primary data were collected structured questionnaire. The questionnaire was prepared after surveying available literature and transfer to online data collection platform. The questionnaire was contained research questions and demographic profile of respondents. The respondents were exposed to 11 statements related to mobile app usage for milk purchase on five-point Likert type scale where 5- Strongly Agree, 4- Agree, 3- Neutral, 2- Disagree and 1- Strongly Disagree. The link for the data collection was share to contacts using social media. Further, the respondents were also told to share link with their contact. Thus, the snowball sampling was applied. The sample size was kept 150. The collected data were analyzed with the help of computer software. The exploratory factor analysis (EFA) was carried out for the perception towards milk app. 

Demographic profile and Milk purchase Behaviour

The demographic profile such as gender, occupation, education, family size, monthly income and milk purchase details like expenditure on milk and milk purchase frequency, the frequency of the profile is displayed below in Table 1.

For the present study responses from 150 respondents were collected and the profile of 150 respondents shown in Table 1.  Out of 150 respondents 58.7 percent respondents stay in joint family whereas 41.3 percent stay in nuclear family. The average age of the respondents was found 36.52 years (Minimum 18 years and maximum 72 years). In case of monthly family income, it was found that 47.3 percent respondents have above Rs. 50000 monthly family income followed by 34.7 percent have Rs. 30000-50000 monthly family income, and 18 percent respondents have below Rs.30000 monthly family income.  Out of 150 respondents 114 respondents (76 percent) were purchasing packed milk whereas 36 respondents (24 percent) were purchasing loose milk. 

The preferred time for purchase of milk was investigated and found that 64.7 percent respondents prefer morning time followed by 29.3 percent and 6 percent anytime for milk purchase. The respondents were also asked for preferred place for the purchase of milk and found that the 60.7 percent respondents prefer milk parlour followed by 26.7 percent kirana store and 12.7 percent other option, the findings are in line with (Kalaivani et al., 2023). Out of 150 respondents 82 percent prefer home delivery. Out of 150 respondents 68 percent respondents do not get home delivery.  The 57.3 percent respondents prefer online mode for the purchase of grocery items whereas 42.7 percent prefer offline mode, the findings are in line with Chettiar (2024) who found that respondents prefer online grocery shopping, and also with the findings of Scaria et al. (2021) that online shopping is increasing. Further it also supports findings of Wijayanto (2024) who found that for Time saving, effectiveness and ease of use and learning of applications are several factors that influence consumers so that they form a positive attitude towards shopping through e-grocery applications. Perceived usefulness and attitude are able to encourage consumers’ behavioural intensions to use e-grocery applications to shop for daily necessities.  The benefits and positive attitude felt by consumers influence consumer intension to use e-grocery applications.

The respondents were asked for their preference for home delivery of milk and status of actually enjoying home delivery of milk and results were shown in Table 2 As table depicts 82 percent respondents of total respondents prefer the home delivery of milk and 32 percent respondents actually get the home delivery. The 50 percent respondents do not get home delivery of milk that prefer the home delivery of milk. 

  Table 1: Demographic Profile of respondents.

Table 2: Cross tabulation for actual home delivery of milk and Preference for home delivery of milk.

Reliability Test

In order to check the check reliability of the scale used to understand factors influencing mobile app adoption for online milk purchase, Cronbach’s Alpha was applied. The Cronbach's Alpha for 11 items was found 0.942. The Cronbach's Alpha used to check reliability of scale, the value ranges from 0 to 1. As per rule of thumb, the value of alpha greater than 0.7 is consider as good and acceptable for further analysis (Tavakol & Dennick 2011).

Exploratory Factor Analysis

The Exploratory factor analysis is a multivariate data analysis technique which is used for data reduction (Malhotra and Dash 2007).

It is method to identify the factors underling the variables by means of clubbing related variables in the same factor (Verma and Abdel-Salam 2019). To check the appropriateness of data two tests were conducted.  i. Bartlett’s test of sphericity and ii. Kaiser- Meyer- Olkin (KMO) measures of sampling adequacy. The outcome is presented in below Table 4. The value of KMO measures of sampling adequacy falls between 0.5 to 1.0, which indicates factor analysis is appropriate and value below 0.5 indicates inappropriateness of the analysis (Malhotra and Dash 2007). For the present study the Kaiser- Meyer- Olkin measures of Sampling Adequacy value obtained is 0.873. 

So, it can be inferred that present data are appropriate for factor analysis. Further the approximate chi-square value was found 1437.552 at 55 degree of freedom for Bartlett’s test of Sphericity which is significant at the 0.05 level (value= 0.00). So, it can be inferred that the variables in population are correlated.

To carryout factor analysis 6 to 7 methods are available out of them principal axis factoring analysis was adopted in the present study. The Equamax with Kaiser Normalization was adopted as rotation method. The rotated component matrix was presented using sorted by size and the coefficients were suppressed having value below 0.4 and four factors were extracted.  The four explained 77.916 percent of total variance (Table 5).

Table 3: Reliability Test.

Table 4: KMO and Bartlett's Test.

Table 5: Total Variance Explained.

The factor analysis extracted 4 factors shown in Table 6, out of four factors the first factor termed as “Attitude towards mobile app usage” due to the high loading to the statements like, “Ordering milk through mobile app is Convenient (0.766), Ordering milk through mobile app is good idea (0.723) and Ordering milk through mobile app is pleasant (0.699)”. The second factor termed as “subjective norms” due to the high loading to the statements like, “People whose opinions my family/I value would prefer to Order milk through mobile app (0.820), My friends think technology of Ordering milk through mobile app is useful (0.711), Most people who are important to me and my family think that we should incorporate Ordering milk through (0.620)”. The third factor termed as “perceived ease of use” due to loading to the statements like “Overall, Ordering milk through mobile app will be useful (0.819), Ordering milk through mobile app will be easy (0.596), Ordering milk through mobile app will save times (0.573)” and forth factor termed as “perceived usefulness” due to statements like, “It will be easy for me to learn how to order milk online (0.885), and It is easy for me to remember how to order online (0.610)”. The study findings are in line with the outcome of Carfora et al., (2019) who found that the attitude, subjective norms, perceived ease of use and perceived usefulness influence to milk purchase intention. Further, it also supports findings of Primaroni et al. (2024) found that behavioural intension was significantly influenced by attitude, subjective norms and perceived behaviour of control. The perceived ease of use and perceived usefulness (both) have notable impact on attitude.  The findings of the present study are partially in line with Jasti and Syed (2019) that perceived ease of use has positive impact on perceived usefulness and the perceived usefulness has positive impact on purchase intention by mediating attitude.  Further, Arora et al. (2022) identified intention and attitude are highly correlated followed by offers and attitude for grocery apps in India and the present study partially in line with it. Bauerová and Klepek (2018) found the perceived ease of use has a positive effect on Perceived Usefulness and Perceived Usefulness consequently affects the Behavioural intention to buy groceries online, the outcome of the present study also support it. 

Table 6: Rotated Factor Matrix.

The future research can be carried out on the aspects of need of various features expected in mobile apps for milk. Further constraints faced for mobile app can also be studied. The service quality of mobile app can be studies. 

Urja Kotadia and Gautam Parmar (2025). Factors Influencing the Mobile app Adoption for Online Milk Purchase in Surat City. International Journal on Emerging Technologies, 16(2): 158–163.