The digital transformation of manufacturing (a phenomenon also known as “Industry 4.0” or “Smart Manufacturing”) is finding a growing interest both at practitioner and academic levels, but is still in its infancy and needs deeper investigation. Even though current and potential advantages of digital manufacturing are remarkable, in terms of improved efficiency, sustainability, customization, and flexibility, only a limited number of companies has already developed ad hoc strategies necessary to achieve a superior performance. Through a systematic review, this study aims at assessing the current state of the art of the academic literature regarding the paradigm shift occurring in the manufacturing settings, in order to provide definitions as well as point out recurring patterns and gaps to be addressed by future research. For the literature search, the most representative keywords, strict criteria, and classification schemes based on authoritative reference studies were used. The final sample of 156 primary publications was analyzed through a systematic coding process to identify theoretical and methodological approaches, together with other significant elements. This analysis allowed a mapping of the literature based on clusters of critical themes to synthesize the developments of different research streams and provide the most representative picture of its current state. Research areas, insights, and gaps resulting from this analysis contributed to create a schematic research agenda, which clearly indicates the space for future evolutions of the state of knowledge in this field.

Purpose: Retail customers are now omnichannel both for attitude and behavior. Since individuals interact with multiple touchpoints throughout their shopping journey, retailers must consider all channels holistically as consumers do, in order to provide them with a unified shopping experience. Thus, recently retailers are showing an increasing interest in the deployment of advanced in-store systems aimed at enhancing the shopping experience by providing customers with innovative tools able to engage them for obtaining deeper insights, saving time and feeling more independent while shopping. Such technologies can help retailers to better react to those emerging settings by: acquiring a positive image and reputation, being perceived as innovator; reducing management costs; maintaining and acquiring loyal consumers; making imitation strategies as difficult as possible for competitors; and reaching a unique and differentiated positioning. The purpose of this paper is to explore whether and how store technology positively affects the customer experience in an omnichannel retail environment and if the early adoption of in-store technology by retailers produces a sustainable competitive advantage. Design/methodology/approach: To achieve the research aim and deeply explore the phenomenon of interest, a multiple-case study involving 15 retailers, operating in different industries of the Italian market, was carried through 80 semi-structured in-depth interviews with store managers and employees. Findings: Results of the content analysis show that customers are increasingly willing to use the in-store innovations in order to obtain richer shopping experiences while retailers achieve a stronger brand loyalty as well as a better and updated brand image. Originality/value: This paper contributes to the relevant literature by proposing two conceptual frameworks. It provides a deeper understanding of possible advantages and threats resulting from early technology adoption by retailers, which is becoming essential for improving the omnichannel customer management and increasing business performance.

Different and profound are the consequences of the further development of artificial intelligence (AI) on society. One of the manifestations of the upcoming changes is the plethora of novel ways in which companies may organize labor and capital. So far, the influence of AI on organizational structure has been mainly studied from either a technological or a broader decision-making perspective. Our paper provides a unique take on the topic, emphasizing the distinctive role of human agency and its function in the upcoming AI-driven organizational changes. Relying on the existing academic literature, we theorized a set of hypotheses concerning best fits to the AI contingency on both macro and meso-organizational levels. To test whether the hypothesized changes might encounter organizational inertia or resistance from jobholders, multiple correspondence analysis (MCA) was applied to the online survey results. Four types of current and potential jobholders were distinguished according to their explicit attitudes towards hypothesized organizational changes: skeptics, doubtful skeptics, optimists, and doubtful optimists, the latter consisting primarily of emerging adults. Finally, we developed a model of intraorganizational response to the AI contingency based on four theoretical groups of individuals, as determined by the analysis. Our findings showed doubtful optimists to be the most important group, able to set organizational trends and positively influence skeptics and doubtful skeptics. Accordingly, promoters of AI-driven organizational changes are advised to design their communication efforts around emerging adults. As the survey was conducted amid the COVID-19 pandemic, its sociological and managerial implications are relevant to the looming reality of the postpandemic world.

As food loss and food waste continue to be social, economic, and environmental issues, governments, private companies, and nonprofit organizations have started initiatives in order to raise awareness about this topic. Food waste occurs mainly at the consumer level, so information campaigns have been carried out in the media, in shops, or online, in order to point out the extent of the problem, its effects, and to educate consumers as to how to generate less waste. Although some awareness campaigns were conducted in Romania, mostly by the private sector, but also by governmental institutions, these efforts are still few in comparison with other European countries. Research of this topic has also started to increase, but the effects of the campaigns conducted in Romania have not been thoroughly assessed. In an effort to evaluate the effectiveness of these campaigns on consumer food-wasting behavior, we have carried out a study among Romanian citizens, based on a survey. The data were analyzed using the chi-square test and the probit regression model. The results indicate that most respondents already self-assessed their food waste as small to moderate, regardless of their familiarity with food waste campaigns, and it might be the case that only their additional actions to reduce food waste are linked with them coming across food waste reduction campaigns.

Drinking water quality assessment is a major issue today, as it is crucial to supply safe drinking water to ensure the well-being of society. Predicting drinking water quality helps strengthen water management and fight water pollution; technologies and practices for drinking water quality assessment are continuously improving; artificial intelligence methods prove their efficiency in this domain. This research effort seeks a hierarchical fuzzy model for predicting drinking water quality in Rome (Italy). The Mamdani fuzzy inference system is applied with different defuzzification methods. The proposed model includes three fuzzy intermediate models and one fuzzy final model. Each model consists of three input parameters and 27 fuzzy rules. A water quality assessment model is developed with a dataset that considers nine parameters (alkalinity, hardness, pH, Ca, Mg, fluoride, sulphate, nitrates, and iron). These nine parameters of drinking water are anticipated to be within the acceptable limits set to protect human health. Fuzzy-logic-based methods have been demonstrated to be appropriate to address uncertainty and subjectivity in drinking water quality assessment; they are an effective method for managing complicated, uncertain water systems and predicting drinking water quality. The proposed method can provide an effective solution for complex systems; this method can be modified easily to improve performance.