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This study aims to explore how a start-up entering maritime logistics networks (MLNs) in the container shipping industry integrates resources underlying value cocreation patterns in these networks.

The paper is based on a single case study of a technological start-up, providing tracking, tracing and other information services to MLN members using internet-based software. An interorganizational theory perspective informs the case study to unveil the resource integration for value cocreation in the network.

The start-up holds multiple resource interaction roles and the start-up’s involvement enables the creation of new knowledge resources, which facilitate new revenue streams and manage resource dependencies. Hence, the findings indicate that the start-up changes value cocreation patterns in the network by reconfiguring and integrating existing resources so that the service is customized for various customers, including shippers and freight forwarders.

The results provide insights about how technological start-ups can unlock resources within MLNs.

The study extends previous studies on resource roles in business networks and shows how start-ups can perform multiple roles simultaneously within these networks. In addition, the study contributes to the literature by studying information and knowledge as resources configured in different ways in a unique network setting.

The purpose of this paper is to inquire into how conceptualising is done in the industrial network approach (INA).

The description and analysis of conceptualising is based on key INA references and an example illustrating the characteristics of conceptualising in individual studies.

The paper concludes that there is an open and interactive way of conceptualising in the INA. The empirical and theoretical grounding achieved through combining concepts in individual empirical studies interplays with conceptual development in the research community over time.

Three paradoxes are suggested for further discussion of conceptualising as a key element in theorising in the INA community.

By explicating how INA researchers engage in conceptualising both in individual empirical studies and as a community, the authors identify characteristics similar to the empirical phenomena in focus of the research: interaction, combining and heterogeneity of concepts.

The novel coronavirus (nCoV) pandemic, and the challenges of social distancing, proffer a unique opportunity to re-explore the role of social network support in entrepreneurship. Applying social support theory and gender schema theory, this study aims to examine the gender-based differences in prospective entrepreneurs' reliance on their social networks in their entrepreneurial journey amid social turmoil.

The authors collected two-stage primary survey data of prospective entrepreneurs within the pandemic's timeframe from Science and Technology Parks in Iran, one of the first countries to deal with the first, second and third waves of the 2019-nCoV virus.

The findings demonstrate that female entrepreneurs rely more strongly on their social network support for guidance and encouragement, which positively affects their opportunity intention. While this effect is also seen in men, the effect size is smaller. Also, prospective female entrepreneurs were generally more dissuaded from opportunity intention by the severe perceived environmental uncertainty of the crisis than were men.

Prior research on the interaction between social network support and opportunity intentions has been examined in the context of socio-economic normalcy. The authors test whether, how and why these interactions hold in times of crisis, with especial attention to the mechanisms of experienced stress, perceived environmental uncertainty and idea innovativeness.

Through the lens of self-perception theory, this paper investigates how avatar design (i.e. avatar user similarity) affects users' self-awareness and shapes their task engagement and performance in the Metaverse.

The authors conducted a 2 (avatar user similarity: high vs low) × 2 (task type: procedural vs creative) lab experiment and collected data from questionnaires, the recording of users' behavior during tasks and their actual task performance.

The results show that higher avatar user similarity leads to higher task engagement in general. Furthermore, while a similar avatar promotes users to regulate their behaviors and achieve better performance in a procedural task, high similarity also inhibits users' creativity by invoking habitual thinking, resulting in worse performance in generating original ideas in a creative task.

This study is expected to contribute to the information systems literature by revealing the value of avatar design and providing new perspectives on improving users' experiences in the Metaverse.

This study aimed to examine the similarities and differences between the ability to analyze the environment and exploit new knowledge (absorptive capacity) and the skills to generate value from innovation (appropriation). These fields have similar origins and are sometimes confused by practitioners and academics.

A review was conducted based on a full-text analysis of 681 and 431 papers on appropriation and absorptive capacity, respectively, from Scopus, Science Direct and Lens, using methodologies such as text mining, backward citation analysis, modularity clustering and latent Dirichlet allocation analysis.

In business disciplines, the fields are considered different; however, in other disciplines, it was found that some authors defined them quite similarly. The citation analysis results showed that appropriation was more relevant to absorptive capacity, or vice versa. From the dimension perspective, it was found that although appropriation was considered a relevant element for absorptive capacity, the last models did not include it. Finally, it was found that studies on both topics identified the importance of appropriation and absorptive capacity for innovation performance, knowledge management and technology transfer.

This is one of the first studies to examine in-depth the relationship between appropriation and absorptive capacity, bridging a gap in both fields.

The family is one of the foundations of society; its significance for societal redistribution in modern societies, though, remains particularly unclear. A major reason for this is that theoretical approaches to societal redistribution have not adequately included family either in social philosophy or in welfare state theory. As a consequence, also empirical analyses of differences and developments in societal redistribution have not included family or only in as far as family is affected by other redistributive principles. This paper contributes to filling this theoretical gap.

This paper theorises family as a redistributive principle. With reference to the major theoretical concepts of redistribution, it identifies the relevant dimensions of family in societal redistribution and develops a typology of its inclusion in societal redistribution.

Approaches to redistribution are shaped by distinct concepts of equal or unequal exchange, the relevant actors they identify and by different understandings of the economy. These distinctions are central to understanding the position of family in societal redistribution. With reference to the major theoretical concepts of redistribution, this paper identifies the relevant dimensions of family in societal redistribution and develops a typology of its inclusion in societal redistribution. Further investigations might draw on this typology and detect the theoretical foundations of its conceptualisations and its similarities to and deviations from the developed types.

This paper provides a theoretical groundwork for theoretical and empirical investigations of societal redistribution and for better comprehending its international variation. It aims to initiate a fundamental rethinking of the usual understanding of societal redistribution that widely ignores family as a redistributive principle of its own.

This article proposes an architecture of an intelligent and autonomous recommendation system to be applied to any virtual learning environment, with the objective of efficiently recommending digital resources. The paper presents the architectural details of the intelligent and autonomous dimensions of the recommendation system. The paper describes a hybrid recommendation model that orchestrates and manages the available information and the specific recommendation needs, in order to determine the recommendation algorithms to be used. The hybrid model allows the integration of the approaches based on collaborative filter, content or knowledge. In the architecture, information is extracted from four sources: the context, the students, the course and the digital resources, identifying variables, such as individual learning styles, socioeconomic information, connection characteristics, location, etc. Tests were carried out for the creation of an academic course, in order to analyse the intelligent and autonomous capabilities of the architecture.

The authors conducted 22 in-depth longitudinal interviews with 11 Hungarian migrant workers in the Dutch logistics sector, before and during the COVID-19 crisis, using thematic analysis and visual life diagrams to interpret them.

This study aims to contribute to conservation of resources theory, by exploring how global crises influence the perceived employability of migrant workers in low-wage, precarious work.

The authors find that resources are key in how migrants experience the valence of global crises in their careers and perceive their employability. When unforeseen consequences of the COVID-19 crisis coincided with migrants' resource gain spirals, this instigated a positively valenced career shock, leading to positive perceptions of employability. Coincidence with loss spirals led to negative perceptions.

The authors contribute to careers literature by showing that resources do not only help migrants cope with the impact of career shocks but also directly influence the valence of global crises in their perceived employability and careers.

Interestingly, when the COVID-19 crisis did not co-occur with migrants' resource gain and loss spirals, migrants experienced resource stress (psychological strain induced by the threat or actual loss of resources) and no significant change in their perceptions of employability.

Algorithm bias refers to repetitive computer program errors that give some users more weight than others. The aim of this article is to provide a deeper insight of algorithm bias in AI-enabled ERP software customization. Although algorithmic bias in machine learning models has uneven, unfair and unjust impacts, research on it is mostly anecdotal and scattered.

As guided by the previous research (Akter et al., 2022), this study presents the possible design bias (model, data and method) one may experience with enterprise resource planning (ERP) software customization algorithm. This study then presents the artificial intelligence (AI) version of ERP customization algorithm using k-nearest neighbours algorithm.

This study illustrates the possible bias when the prioritized requirements customization estimation (PRCE) algorithm available in the ERP literature is executed without any AI. Then, the authors present their newly developed AI version of the PRCE algorithm that uses ML techniques. The authors then discuss its adjoining algorithmic bias with an illustration. Further, the authors also draw a roadmap for managing algorithmic bias during ERP customization in practice.

To the best of the authors’ knowledge, no prior research has attempted to understand the algorithmic bias that occurs during the execution of the ERP customization algorithm (with or without AI).

The authors constructed an automatic essay scoring (AES) model in a discussion forum where the result was compared with scores given by human evaluators. This research proposes essay scoring, which is conducted through two parameters, semantic and keyword similarities, using a SentenceTransformers pre-trained model that can construct the highest vector embedding. Combining these models is used to optimize the model with increasing accuracy.

The development of the model in the study is divided into seven stages: (1) data collection, (2) pre-processing data, (3) selected pre-trained SentenceTransformers model, (4) semantic similarity (sentence pair), (5) keyword similarity, (6) calculate final score and (7) evaluating model.

The multilingual paraphrase-multilingual-MiniLM-L12-v2 and distilbert-base-multilingual-cased-v1 models got the highest scores from comparisons of 11 pre-trained multilingual models of SentenceTransformers with Indonesian data (Dhini and Girsang, 2023). Both multilingual models were adopted in this study. A combination of two parameters is obtained by comparing the response of the keyword extraction responses with the rubric keywords. Based on the experimental results, proposing a combination can increase the evaluation results by 0.2.

This study uses discussion forum data from the general biology course in online learning at the open university for the 2020.2 and 2021.2 semesters. Forum discussion ratings are still manual. In this survey, the authors created a model that automatically calculates the value of discussion forums, which are essays based on the lecturer's answers moreover rubrics.