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While there are several readiness assessments regarding digital transformation (DT) and Industry 4.0 in extant literature, this study aims to contribute to (a) a better understanding of digital readiness in supply chain (SC) aspects and (b) elaborate on differences between small and medium-sized enterprises (SMEs) and large enterprises.

The study is based on 409 companies that participated in the Digital Readiness Check (DRC) in the region of Salzburg (Austria) and Bavaria (Germany) – an online assessment for self-evaluating the digital readiness of companies.

The study's results provide insights for the categories of strategy, employees, initiation of business transactions and SC. These are further differentiated for SMEs and large enterprises.

The study is limited to two regions in Austria and Germany, based on a self-evaluation of companies in a single point of time perspective. For future research, the results of this study should be expanded for different regions. Further, the results could be validated regarding external observations and measuring results at a later point of time.

The DRC may help companies in benchmarking themselves and gaining a better understanding about categories that must be improved, especially regarding SC aspects of DT.

The DRC extends extant literature regarding the differentiation between SMEs and large enterprises as well as focussing on SC aspects of DT.

The purpose of this paper is to systematically review and analyse the critical challenges and barriers of e‐government adoption. Such review aims to suggest the salient facts about the issues of successful implementation or adoption of the e‐government services under different circumstances to the researchers.

A total of 78 relevant research papers reviewing and analysing the challenges, barriers, and critical success factors were selected from a set of overall 448 articles on e‐government adoption research. These studies were comprehensively reviewed to examine some of the most significant supply and demand‐side challenges, barriers, and critical success factors explored by different studies in this context.

The findings indicated that technological barriers, lack of security and privacy, lack of trust, lack of resources, digital divide, poor management and infrastructure, lack of awareness, legal barriers, lack of IT infrastructure, and resilience were among some of the most commonly experienced challenges and barriers across the relevant studies. Moreover, it was also found that challenges and barriers associated with supply‐side (i.e. implementation) (C=53) were almost three times to the one applied to the demand‐side (i.e. adoption) (C=18). Furthermore, it was also found that citizen's satisfaction, information accuracy, security, and privacy were some of the critical factors for the success of e‐government initiatives.

This research only reviews the challenges, barriers and critical success factors and leaving apart many other research themes such as impact, digital divide, security, privacy, trust, and risk of e‐government adoption. Moreover, the theoretical and methodological paradigm of this research have not been explored.

This paper presents a comprehensive review of the challenges, barriers, and critical success factors of the e‐government adoption research both with regard to supply as well as demand side. Such review allows us to provide not only a brief account of the issues experienced in the e‐government research, but also prescribes the guidelines for the governments to consider certain facts before successfully implement their e‐government initiatives. Such a comprehensive review of e‐government adoption literature has not been performed earlier.

The aim of this paper is to gain knowledge in podcast mining as an additional source for Web-based horizon scanning (HS). The paper presents theoretical insights on the potential of podcast mining by exploring topics, which may be relevant in the future, and by reflecting the results against a background of HS approaches. The study provides a preliminary overview by presenting an exemplary list of podcast shows for further research.

The paper uses an exploratory quantitative content analysis, which was conducted on the basis of 30 topics deemed to be relevant in the future and which were identified in the field of applied science. Based on these topics, podcasts and episodes were identified which address future-oriented topics and were discussed in terms of range of content.

The findings indicate that future-oriented topics are addressed in podcasts. However, differences in dynamics and range of content of the podcasts concerned highlight the necessity of identifying a list of suitable podcasts according to the specific scanning focus and the dynamics of each future-oriented topics.

While a growing number of podcast studies have already noted the importance of podcasts as a key medium, for example, educational processes and media sciences, no detailed explanation of podcast mining as a tool for the purposes of HS has been published. The review therefore makes an original contribution to this field, highlighting areas where future research is needed.

With the intensification of market competition, there is a growing demand for weak signal identification and evolutionary analysis for enterprise foresight. For decades, many scholars have conducted relevant research. However, the existing research only cuts in from a single angle and lacks a systematic and comprehensive overview. In this paper, the authors summarize the articles related to weak signal recognition and evolutionary analysis, in an attempt to make contributions to relevant research.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. Framework comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.

The research results show that it is necessary to improve the automation level in the process of weak signal recognition and analysis and transfer valuable human resources to the decision-making stage. In addition, it is necessary to coordinate multiple types of data sources, expand research subfields and optimize weak signal recognition and interpretation methods, with a view to expanding weak signal future research, making theoretical and practical contributions to enterprise foresight, and providing reference for the government to establish weak signal technology monitoring, evaluation and early warning mechanisms.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. It comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.