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In 2023, the New England Journal of Entrepreneurship (NEJE) reached its 25th anniversary. To commemorate this major milestone as well as entrepreneurship’s growth as an academic field, the study employs bibliometric methods to provide key trends and research suggestions for entrepreneurship scholars using all original research published in the journal.

The authors perform two predominant bibliometric techniques, performance analysis and science mapping, using all 251 articles published by NEJE from 1998 to 2022.

The authors find that the impact of entrepreneurship research published at NEJE is growing consistently and that the challenge of the future will be to maintain this growth in tandem with greater publication productivity. The authors also find that although most contributions come from authors affiliated with institutions in the USA, there is a global representation from authors who have published in NEJE. Further, the authors found that the major entrepreneurship research themes of articles published in NEJE revolve around general entrepreneurship, entrepreneurial behavior, gender, technology, entrepreneurship education, innovation and value creation and sustainability.

The analysis is restricted to articles published in NEJE and therefore may not be representative of the entrepreneurship field. However, it can serve as a useful resource, particularly for prospective NEJE authors, to gain empirical insights about entrepreneurship research trends and rising topics of interest.

The authors’ work represents the first effort to synthesize research published in NEJE through bibliometric techniques and offers insights about important trends and themes in this rising outlet of the entrepreneurship field.

The diversity of perspectives means that one can find many factors and models of proenvironmental behavior. However, they typically suffer from limitations and varying degrees of validity in specific contexts, suggesting that today the prime goal should be to learn and improve the models which have been already developed. In this study, the authors build on the model for predicting proenvironmental behavior developed by Oreg and Katz-Gerro (2006), namely one of the most comprehensive cross-national proenvironmental behavior models and one of the few not to be limited to either a local or single-country context or specific proenvironmental behavior.

By using the statistical matching technique, the authors merged data from two existing databases without common identifiers – the International Social Survey Program (environmental module) and the European Social Survey (Round 5). The resulting multinational data concerning 9,710 observations enabled a replication with extensions of Oreg and Katz-Gerro's (2006) proenvironmental behavior model that incorporates newly added Schwartz's theory of human values. To achieve the study's main objective, that is, to present improvements to the original model of proenvironmental behavior, the authors used structural equation modeling (SEM) procedures to estimate four competing models in the R program.

This study implies that Schwartz's individually measured motivational types of values (benevolence [BE], universalism [UN], self-direction [SD]) are predictors of people's proenvironmental behavior, while his conceptualization of post-materialism yields a better model fit than Inglehart's country-level post-materialism scores. The results also corroborate previous findings that post-materialist values can stimulate proenvironmental behaviors through attitudes, perceived behavioral control and intentions. The present study reveals that proenvironmental attitudes did not change substantially in the 10-year period, even though the world's environmental and sustainability challenges have largely increased. Surprisingly, the mean value of several of the perceived threat variables even decreased.

The authors externally validate one of the most comprehensive proenvironmental behavior models by reproducing it using new multinational large-sample data with nearly 10,000 observations collected 10 years later. The most significant addition to the original model introduced in the current study is the inclusion of Schwartz's motivational types of values, which are measured at the individual level, namely BE, UN and SD. The authors also extend the model by adding proenvironmental behavior measures and group the construct into three latent variables: saving natural resources, green purchasing and environmental activism.

Today’s society interest in mobile photography drives consumers’ and brands’ growing usage of Instagram. This paper aims to address the consequences of excessive use of Instagram on the negative feeling of losing information when not connected and the emotional fatigue resulting from an overcharge with new information. The mediating role of addiction between Instagram overuse and the two outcomes is also analyzed.

Data from 342 active Instagram users were used to test the proposed model, applying the partial least square equation modeling method (SmartPLS 3).

Addiction partially mediates the impact of overuse on emotional fatigue and instastress. Addiction to Instagram was mainly due to respondents’ lack of control over the time spent on it resulting in incapability to reduce its usage.

Social networking site managers, educators, families and public institutions should promote an adequate use of Instagram, making users (especially the young) aware of the potential threats of its excessive usage. The control on the amount of time devoted to Instagram is a key factor for detaining overuse and addiction, as well as avoiding the negative outcomes analyzed in this research.

The findings contribute to the extant knowledge on the negative side of the digitization of the individual, as little is known about it to the best of the authors’ knowledge.

La presente investigación aborda las consecuencias del uso excesivo de Instagram relacionadas con el sentimiento negativo de pérdida de información, cuando no se está conectado, y la fatiga emocional derivada de la sobrecarga de información. El estudio también analiza el papel mediador de la adicción entre el uso excesivo de Instagram y ambas consecuencias.

Para testar el modelo propuesto se utilizó información de 342 usuarios activos de Instagram, aplicándose el método de ecuaciones estructurales por mínimos cuadrados parciales (SmartPLS 3).

La adicción media parcialmente el impacto que el uso excesivo de Instagram tiene sobre (i) la fatiga emocional y (ii) el Instastress. La adicción a Instagram se deriva principalmente de la falta de control del tiempo invertido en su uso, lo que da como resultado su incapacidad para reducir dicho uso.

Los responsables de los medios sociales, los educadores, las familias y las instituciones públicas deberían promover un uso adecuado de Instagram, haciendo que los usuarios (especialmente los más jóvenes) sean conscientes de las amenazas potenciales de su uso excesivo. El control sobre la cantidad de tiempo dedicado a Instagram es un factor clave para detener el uso excesivo y la adicción, así como para evitar los resultados negativos analizados en esta investigación.

Los resultados contribuyen a ampliar el conocimiento sobre el lado negativo de la digitalización del individuo, dada la escasa literatura al respecto.

Adicción, Uso excesivo, Instastress, Fatiga emocional, Redes sociales, Instagram

Artículo de investigación

The key challenge that urban cities in most developing and transitional economies is confronting is municipal solid waste (MSW) management. Waste separation is a critical component to successful recycling management in terms of enhancing the quality of recyclables, reducing MSW and optimizing incineration. The urge to actualizing sustained waste separation behavior has been hindered by potential barriers. This study aims to examine the influences of external and internal stimuli of targeted households' waste separation intention in parts of China.

A multifactor framework predicting the process that leads to waste separation attitude and behavioral intention is proposed. SEM analysis is conducted in SmartPLS based on 371 survey questionnaires collected in Nanning city in China.

Policy regulation is the biggest determinant of attitude among external stimuli, while awareness of consequence has the strongest relationship with an attitude among internal stimuli. Facilitating conditions, subjective norms and moral norms are all significant predictors of attitude. As a result, increasing positive attitude leads to enhance waste separation lifestyle.

This study adopts a cross-sectional design to investigate the waste separation intention of local households. Data collection is restricted to one point in time for every individual. A mixed method is recommended. Quantitative research can examine variables provided in existing literature with numerical analysis. Qualitative research might be helpful to identify other unknown factors. Also, the survey questionnaires employ a self-reported manner, and respondents might be overrating to avoid embarrassment.

Future research is recommended to engage observation at houses or at the waste-collecting points for actual waste separation behavior. Moreover, this study measures intention toward household waste separation, but whether this intention will eventually lead to waste separation behavior is not a guarantee. Future study is recommended to examine whether intention translates into actual waste separation behavior.

Emphasizing the importance of policy element as a direct influence toward attitude, this paper focuses on the waste separation attitude accumulated from external and internal stimuli, and, concurrently, waste separation behavioral intention is influenced by accumulated attitudes. The study provides relevant policy development information of three Asian countries to enhance their present and future policy directions for a sustainable household waste separation management process

Digital twin (DT) is an emerging technology that enables sophisticated interaction between physical objects and their virtual replicas. Although DT has recently gained significant attraction in both industry and academia, there is no systematic understanding of DT from its development history to its different concepts and applications in disparate disciplines. The majority of DT literature focuses on the conceptual development of DT frameworks for a specific implementation area. Hence, this paper provides a state-of-the-art review of DT history, different definitions and models, and six types of key enabling technologies. The review also provides a comprehensive survey of DT applications from two perspectives: (1) applications in four product-lifecycle phases, i.e. product design, manufacturing, operation and maintenance, and recycling and (2) applications in four categorized engineering fields, including aerospace engineering, tunneling and underground engineering, wind engineering and Internet of things (IoT) applications. DT frameworks, characteristic components, key technologies and specific applications are extracted for each DT category in this paper. A comprehensive survey of the DT references reveals the following findings: (1) The majority of existing DT models only involve one-way data transfer from physical entities to virtual models and (2) There is a lack of consideration of the environmental coupling, which results in the inaccurate representation of the virtual components in existing DT models. Thus, this paper highlights the role of environmental factor in DT enabling technologies and in categorized engineering applications. In addition, the review discusses the key challenges and provides future work for constructing DTs of complex engineering systems.