Resurgence of small eateries– The successful business model of online Food Apps in major cities of Kerala
Bibliographic record
Abstract
The country’s GDP grew at a modest 4.5 per cent in the September quarter 2019, and the official data released showed a sixth straight fall in quarterly GDP growth and also the first time fall below the psychologically important 5 per cent mark in almost seven years. It is in this context that the festive sales hosted by the ecommerce sector ended first week of October 2019 where the e-tailers in India, mainly Amazon and Flipkart, achieved a record $3 billion (about Rs 19,000 crore) of Gross Merchandise Value (GMV) during the period as per a report by consulting firm RedSeer has to be evaluated. The success of business models, whether it be in e-tailing (amazon, flip-kart etc.), transportation (Uber, Ola Cabs etc..) or online ordering from eatery apps (Ubereats, Swiggy, Zomato etc.) despite the reverse trend in GDP growth and sustained recession, needs to be evaluated in the context of innovation applied and technology adoption. It is in the backdrop of above said upsurge of business model innovations that can combat the challenges in downfalls of an economy and/ or ever-increasing competition on a global platform, the effectiveness of business models assumes \nsignificance. A laggard manager clinging on to his age-old business model is now forced to look forward to articulate their existing business model, since the core enabler of a firm’s performance is an effective business model. Understanding the possibilities for innovating through theoretical insight and practical guidelines needs identification of types and the development of a typology of business model innovations. The online eatery business of restaurants, with key partners such as payment processors, mapping data providers and delivery bike drivers through channels such as mobile apps and telephone ensures customer relations by providing convenience in the form of wide choice of sourcing and menu as well as easy payments has found its own way into urban and semi-urban centres of almost all the states in India, Kerala being no exception. The proposed study intends to identify how successful is the business model adopted by medium and small restaurants in providing its customers a wide choice of menu coupled with timely and prompt delivery through online ordering apps such as Uber Eats, Swiggy, Zomato etc across the major cities in Kerala. \nThe study relies on structural equation modelling to identify the impact of constructs namely customer (eater) satisfaction and delivery partner (biker) benefit on the success of business model through evaluation of benefits to the eateries (restaurants). These constructs or latent variables were predicted using 8 measured variables for customer satisfaction, 4 measured variables for job potential and 4 measured variables for eatery benefits. The structural equation model will evaluate the predictability capability of each measured variables. The hypothesis whether the customer satisfaction and employee benefits directly impact success of the online business model will be tested. Data collected from 120 regular users of online food apps and 120 delivery boys as well as 120 restaurant partners from Thiruvananthapuram and Ernakulam cities, using separate questionnaire were analysed. The responses to measured variables were obtained on a 5-point scale and the parameters of model were tested for internal reliability, convergent and discriminant validity, fitness indices and probabilities of standardised regression weights. The results of analysis revealed that all the dimensions of customer satisfaction and job potential significantly predicts to success of business model and \nsuccess of business model directly impacts the benefits derived by eateries through the business model of online ordering and delivery of food
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".