Abstract
Purpose Technological advances in the last decade have caused both business and economic sectors to seek for new ways to adapt their business models to a connected data-centric era. Family businesses have also been forced to leave behind traditional strategies rooted in family stimuli and ties and to adapt their actions in digital environments. In this context, this study aims to identify major online marketing strategies, business models and technology applications developed to date by family firms. Methodology: Upon a systematic literature review, we develop a multiple correspondence analysis (MCA) under the homogeneity analysis of variance by means of alternating least squares (HOMALS) framework programmed in the R language. Based on the results, the analyzed contributions are visually analyzed in clusters. Design/methodology/approach Upon a systematic literature review, we develop an MCA under the HOMALS framework programmed in the R language. Based on the results, the analyzed contributions are visually analyzed in clusters. Findings Relevant indicators are identified for the successful development of digital family businesses classified in the following three categories: (1) digital business models, (2) digital marketing techniques and (3) technology applications. The first category consists of four digital business models: mobile marketing, e-commerce, cost per click, cost per mile and cost per acquisition. The second category includes six digital marketing techniques: search marketing (search engine optimization and search engine marketing (SEM) strategies), social media marketing, social ads, social selling, websites and online reputation optimization. Finally, the third category consists of the following aspects: digital innovation, digital tools, innovative marketing, knowledge discovery and online decision making. In addition, five research propositions are developed for further discussion and future research. Originality/value To the best of our knowledge, this study is the first to cover this research topic applying the emerging programming language R for the development of an MCA under the HOMALS framework.

Abstract
Globalisation, urbanisation and the recent COVID-19 pandemic has been raising the demand for logistic activities. This change is affecting the entire supply chain, especially the last-mile step. This step is considered the most expensive and ineffective part of the supply chain and a source of negative economic, environmental and social externalities. This article aims to characterise the sustainable urban last-mile logistics research field through a systematic literature review (N = 102). This wide and holistic review was organised into six thematic clusters that identified the main concepts addressed in the different areas of the last-mile research and the existence of 14 solutions, grouped into three types (vehicular, operational, and organisational solutions). The major findings are that there are no ideal last-mile solutions as their limitations should be further explored by considering the so-called triple bottom line of sustainability; the integration and combination of multiple last-mile alternative concepts; or by establishing collaboration schemes that minimise the stakeholders' conflicting interests.

Abstract
When compared with traditional local shops where the customer has a personalised service, in large retail departments, the client has to make his purchase decisions independently, mostly supported by the information available in the package. Additionally, people are becoming more aware of the importance of the food ingredients and demanding about the type of products they buy and the information provided in the package, despite it often being hard to interpret. Big shops such as supermarkets have also introduced important challenges for the retailer due to the large number of different products in the store, heterogeneous affluence and the daily needs of item repositioning. In this scenario, the automatic detection and recognition of products on the shelves or off the shelves has gained increased interest as the application of these technologies may improve the shopping experience through self-assisted shopping apps and autonomous shopping, or even benefit stock management with real-time inventory, automatic shelf monitoring and product tracking. These solutions can also have an important impact on customers with visual impairments. Despite recent developments in computer vision, automatic grocery product recognition is still very challenging, with most works focusing on the detection or recognition of a small number of products, often under controlled conditions. This paper discusses the challenges related to this problem and presents a review of proposed methods for retail product label processing, with a special focus on assisted analysis for customer support, including for the visually impaired. Moreover, it details the public datasets used in this topic and identifies their limitations, and discusses future research directions of related fields.

Abstract

Abstract
Root cause analysis (RCA) is the process through which we find the true cause of a problem. It is a crucial process in manufacturing, as only after finding the root cause and addressing it, it is possible to improve the manufacturing operation. However, this is a very time-consuming process, especially if the amount of data about the manufacturing operation is considerable. With the increase in automation and the advent of Industry 4.0, sensorization of manufacturing environments has expanded, increasing with it the data available. The conjuncture described gives rise to the challenge and the opportunity of automatizing root cause analysis (at least partially), making this process more efficient, using tools from data mining and machine learning to help the analyst find the root cause of a problem. This paper presents an overview of the literature that has been published in the last 17 years on developing automatic root cause analysis (ARCA) solutions in manufacturing. The literature on the topic is disperse and it is currently lacking a connecting thread. As such, this study analyzes how previous studies developed the different elements of an ARCA solution for manufacturing: the types of data used, the methodologies, and the evaluation measures of the methods proposed. The proposed conceptualization establishes the base on which future studies on ARCA can develop results from this analysis, identifying gaps in the literature and future research opportunities.

Abstract