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Within food industry several changes and innovations are affecting the management of the entire supply chain (production, logistics, etc.). As strategy for the survival and competition, digitalization has assumed a crucial role during the pandemic emergence by causing the reconfiguration of traditional chains and business models. Framed in these premises, the research analyses how digital technologies have innovated the sub-chains of bakery products and pasta within food industry with reference to customers' interactions, delivery and marketing during the COVID-19 pandemic emergence.

Moving from a critical literature review about the perspectives of digital technologies within the tradition of food industry, action research has been adopted to analyze in deep a case study of the start-up “ArteBianca Delivery” located in South Italy. Through this method, researchers have been deeply involved within the start-up to face the challenge of transforming the marketing and customer care into digital ones due to the COVID-19 restriction.

Findings provide empirical evidence about the reconfiguration of the traditional business model of a family firm in the food sector into a digital one with the start-up “ArteBianca Delivery”. The marketing, delivery, e-commerce and customer care components of the business models have been supported and enhanced through the adoption of digital tools, such as mobile applications and social technologies useful both for users and for a more urgent digitization of company.

Implications for practice can be identified into the pattern of digital transformation implemented as well as in the opportunity of replication and contextualization of the results to other companies looking for setting up a digital strategy.

Elements of original contribution can be identified into: (1) the exploration of digital transformation in food family firms caused by the pandemic emergence, (2) the contextualization of the digital transformation to the sub-chains of bakery and pasta and (3) the geographical location of the case.

This study examines the Robo-Advisors (RA) based on Artificial Intelligence (AI), a new service that digitises and automates investment decisions in the financial and banking industries to provide low-cost and personalised financial advice. The RAs use objective algorithms to select portfolios, reduce behavioural biases, and improve transactions. They are inexpensive, accessible, and transparent platforms. Objective algorithms improve the believability of portfolio selection.

This study adopts a qualitative approach consisting of an exploratory examination of seven different RA case studies and analyses the RA platforms used in the banking industry.

The findings provide two different approaches to running a business that are appropriate for either fully automated or hybrid RAs through the realisation of two platform model frameworks. The research reveals that relying solely on algorithms and not including any services involving human interaction in a company model is inadequate to meet the requirements of customers in decision-making.

This study emphasises key robo-advisory features, such as investor profiling, asset allocation, investment strategies, portfolio rebalancing, and performance evaluation. These features provide managers and practitioners with new information on enhancing client satisfaction, improving services, and adjusting to dynamic market demands.

This study fills the research gap related to the analysis of RA platform models by providing a meticulous analysis of two different types of RAs, namely, fully automated and hybrid, which have not received adequate attention in the literature.

Wind power is the most promising renewable energy source in China. The development of digital technologies has brought about unprecedented growth opportunities and prospects for wind power. However, the relationship between digital technology adoption and total factor productivity (TFP) in the wind power industry in China has not been empirically assessed. This study aims to clarify whether and how digital technology adoption affects the TFP of the wind power industry in China.

Based on the data of listed companies in the Chinese wind power industry from 2006 to 2021, this study proposes and verifies relevant hypotheses with two-way fixed effects regression models.

The empirical results indicate that digital technology adoption is the cornerstone of the TFP of China’s wind power industry. Reconfiguration capability and technological innovation serially mediate the above relationship. In addition, the incentive effect of digital technology adoption varies among wind power firms. The impact of digital technology adoption is more significant in firms that are old and located in economically undeveloped regions.

This study is one of the earliest attempts to investigate the relationship between digital technology adoption and TFP in the renewable energy sectors of emerging economies. By integrating dynamic capability theory and the analytical framework of “Capability-Behavior-Performance” into the digital context, this study offers the theoretical insights into how digital technology adoption can enhance organizational reconfiguration capability, thereby stimulating technological innovation and subsequent TFP. Additionally, the impacts of different digital technologies are estimated in entirety, rather than in isolation.