Resurgence of small eateries– The successful business model of online Food Apps in major cities of Kerala
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».