A Qualitative study on Innovation and Dimensional aspects of Omnichannel Retail Business Model

The purpose of the study is to elucidate linkage of Omnichannel retail business model with innovation and technological advancements. The study is exploratory and qualitative in nature, based on primary and secondary data sources collected from varied retail sectors such as fashion, furniture, eyecare and electronics . The study has used Business Model Canvass (BMC) as a tool for strategic analysis. The study presents findings about business model and strategies in Omnichannel context from Indian retailers. The findings of the study posits four main dimensions resultant of digitalization and technological advancements in Omnichannel retail, namely Omnichannel Intensity, Organizational Structure Integration, Operations and Supply Chain Management Innovation, Data Analytics and Intelligence. Cross-channel Integration and Data Analytics & Intelligence have been found to be contributing enormously towards the strategic growth of Omnichannel retailers, thus emerging as the prominent managerial implications of the study.

Building Business Analytic Tool using Dark Data and Big Data

The data is turning into the fundamental resource in the present science and innovation. Tragically, a lot of accessible and put away information isn't utilized today. This information is known as dull information. Big data is said to offer not just phenomenal degrees of business knowledge concerning the propensities for buyers and opponents, yet in addition to proclaim an upset in the manner by which business are coordinated and run. Organizations strive to achieve a competitive edge through, big data and business analytics tool. In this paper we have discussed about how dark data is used in organizations and the technologies evolved in business model. We have explored awareness in dark data and how we can implement them in business model. Keywords: Big Data, Dark Data, Business Intelligence (BI), etc.

Carpooling in Connected and Autonomous Vehicles: Current Solutions and Future Directions

Owing to the advancements in communication and computation technologies, the dream of commercialized connected and autonomous cars is becoming a reality. However, among other challenges such as environmental pollution, cost, maintenance, security, and privacy, the ownership of vehicles (especially for Autonomous Vehicles (AV)) is the major obstacle in the realization of this technology at the commercial level. Furthermore, the business model of pay-as-you-go type services further attracts the consumer because there is no need for upfront investment. In this vein, the idea of car-sharing ( aka carpooling) is getting ground due to, at least in part, its simplicity, cost-effectiveness, and affordable choice of transportation. Carpooling systems are still in their infancy and face challenges such as scheduling, matching passengers interests, business model, security, privacy, and communication. To date, a plethora of research work has already been done covering different aspects of carpooling services (ranging from applications to communication and technologies); however, there is still a lack of a holistic, comprehensive survey that can be a one-stop-shop for the researchers in this area to, i) find all the relevant information, and ii) identify the future research directions. To fill these research challenges, this paper provides a comprehensive survey on carpooling in autonomous and connected vehicles and covers architecture, components, and solutions, including scheduling, matching, mobility, pricing models of carpooling. We also discuss the current challenges in carpooling and identify future research directions. This survey is aimed to spur further discussion among the research community for the effective realization of carpooling.