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The authors explore the potential of machine learning, computers employ that an algorithm to sort data, make decisions and then continuously assess and improve their functionality. They suggest that it be used to power a radical redesign of company processes that they call machine reengineering.

The authors interpret a survey of more than a thousand corporate public agency IT professionals on their use of artificial intelligence and machine learning.

Companies that embrace machine learning find that it adds value to the work product of their employees and provides companies with new capabilities.

Working together with an intelligent machine, workers become custodians of powerfully smart tools, tools that personalize work to maximize their most productive ways of working.

A guide to establishing a culture that empowers employees to thrive alongside intelligent machines.

The proliferation of ride-hailing businesses brings significant considerations for improving the driver's operational performance. Informed by the literature on sharing economy, general deterrence theory and protection motivation theory this research investigates the behavioral factors impacting ride-hailing drivers' operational performance.

The authors empirically test the antecedents impacting a ride-hailing driver's operational performance using an online survey dataset comprising 513 ride-hailing drivers working for Uber and Lyft in the United States.

Ride-hailing drivers' intention to comply with the ride-hailing company guidelines results in better operational performance for the driver. Moreover, drivers believe that ride-hailing companies have effective penalties to deter drivers from violating company guidelines. However, drivers also believe that the chances of being caught while ignoring the company guidelines are low.

The results of this research support the decision-making processes of ride-hailing company managers and offer insights on how managers can enhance the operational performance of their drivers.

This study provides unique contributions to emerging research at the intersection of peer-to-peer asset sharing, behavioral studies and technology management. This research is one of the first to explore the role of behavioral factors such as coping mechanisms on the operational performance of sharing economy workers.

The paper aims to examine the nature and scale of the sustainability value of car sharing and to identify, through consumer analysis, the contextual and consumer factors of success of car subscription as a business model.

The study evaluates the car sharing model against the sustainable development goals defined by the United Nations in 2019. Individual interviews were performed for preliminary understanding of the factors affecting consumers' choices. Subsequently, through two phases of data collection, factor analysis and path model analysis were performed to identify and confirm latent factors. Consumer market segmentation was performed using cluster analysis.

Car sharing was found to have an overall positive net impact, with certain potential negative dimensions. Willingness, financial affordability, location and experience were identified as the key factors of consumers opting for car subscriptions. The findings further highlight the significant business potentialities of car subscription in India, consequent also to consumers' attitudes toward car ownership.

The research has substantial implications for both society and business, with the former being presented with an innovative sustainable means of transportation, and the latter with the elements of success of an entrepreneurial business model to support the former.

The study is a pioneer in objectively evaluating and prescribing positive social and business value creation for and through car subscription in India, based on consumer analysis.

The aim of this paper is to present systematically a variety of benefits from innovating in response to an economic crisis.

This is a conceptual paper that draws on prior conceptual and empirical studies, especially the innovation-based view of firm performance.

Many firms have cut their innovation expenditures in response to the COVID-19 crisis. However, the financial crisis in 2008/2009 has shown that these short-term cost savings may have severely negative consequences on competitiveness. Several innovation examples from the crisis due to the COVID-19 pandemic further indicate that companies may benefit from a variety of first-order innovations, which go considerably beyond product development. Consequently, researchers and executives need to consider the variety of innovation types beyond product development, and they further need to emphasize second-order innovations, which describe the dynamic transformation of innovation management.

Academics and practitioners need to put specific attention to the transformation of innovation processes at the beginning of a crisis because firms may have to redesign their innovation processes. This dynamic reconfiguration and realignment of innovation processes further is essential again if a crisis continues for a longer period, such as the crisis due to the coronavirus pandemic. For example, if a company currently does not respond to relevant trends, its competitive position will deteriorate because other companies will enter the market. If firms can afford to continue or strengthen the innovation activities, they may profit substantially after the crisis.