Although big data has become an integral part of businesses and society, there is still concern about the quality aspects of big data. Past research has focused on identifying various dimensions of big data. However, the research is scattered and there is a need to synthesize the ever involving phenomenon of big data. This research aims at providing a systematic literature review of the quality dimension of big data. Based on a review of 17 articles from academic research, we have presented a set of key quality dimensions of big data.

Increasing the fuel efficiency of industrial machines through digitalization can enable the transport and logistics sector to overcome challenges such as low productivity growth and increasing CO₂ emissions. Modern digitalized machines with embedded sensors that collect and transmit operational data have opened up new avenues for the identification of more efficient machine use. While existing studies of industrial machines have mostly focused on one or a few conditioning factors at a time, this study took a complementary approach, using a large set of known factors that simultaneously conditioned both the fuel consumption and productivity of medium-range forklifts (n = 285) that operated in a natural industrial setting for one full year. The results confirm the importance of a set of factors, including aspects related to the vehicles’ travels, drivers, operations, workload spectra, and contextual factors, such as industry and country. As a novel contribution, this study shows that the key conditioning factors interact with each other in a non-linear and non-additive manner. This means that addressing one factor at a time might not provide optimal fuel consumption, and instead all factors need to be addressed simultaneously as a system.

Due to the advent of digitalization, the retail business has seen major changes in recent decades, including e-commerce and multichannel retailing. Customers have witnessed the development of new and digitized channels, as well as solutions such as digital marketplaces, social media touchpoints, and augmented reality applications. During the COVID-19 pandemic, as most of the physical brick-and-mortar retailers were forced to close, more consumers turned to digital channels such as apps, websites, social media platforms, etc. Small retail businesses, which account for a sizable share of the retail industry, have also faced challenge of seamless retailing in the era of digitization. This concept of seamless retailing is called omnichannel retailing, and it aims to integrate all the retail channels and touchpoints. The goal for small retailers is to make the transformation to omnichannel. The purpose of this study is to investigate how small retail businesses are changing their business models in the age of omnichannel transformation. Through a qualitative study of small retailers, this research provides empirical insights on business model changes and adaptive skills. The findings show that small businesses can implement complex changes in their business models through adaptive behaviors. This study also identifies areas in their business models and adaptive capabilities that require additional consideration and improvement to support the ongoing omnichannel transformation.

The current business environment demands the enablement of organization-wide use of analytics to support a fact-based decision making. Such movement within the organization require employees to take advantage of the self-service business analytics tools to independently fulfil their needs. However, assuming independence in data analytics requires employees to make sense of several elements which collectively contribute to the generation of required insights. Building on sense-making, self-service business analytics, and institutions literature, this paper explores the relationship between sense-making and self-service business analytics and how institutions influence and shape such relationship. By adopting a qualitative perspective and using 22 interviews, we have empirically investigated a model developed through our literature review and provided more understanding of the sense-making concept in a self-service business analytics context.

With the advent of digitalization, e-services are now enabled by embedded digital technologies in physical products such as vehicles, elevators, construction equipment. In spite of numerous instances of e-services enabled by the digitalization of physical products, little research has been carried out to investigate the characteristics of co-creation of such e-services. This paper attempts to fill the gap by reporting from a three-year long research project with a vehicle manufacturing company. Using the translation phases from the Actor-Network theory (ANT) as a theoretical lens, this paper presents three propositions that characterize the co-creation of e-services enabled by the digitalization of physical products. The propositions highlight the role of physical products, establishment of trust and setting priorities about digitalization.