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This study addresses the implications of smart city development paths (techno-centric and human-centric) by investigating the evolution of a city strategy, focusing on how different actors in a dialogue centred on strategic planning documents for Saint Petersburg, Russia, visualised the smart city and then made it calculable.

The authors conducted a case study based on a documentary analysis supported by ethnographic elements relying on the smart city conceptual proposals, the approved city strategy and the artifacts of expert discussions leading to the strategy implementation plan.

Through the lens of dialogue theory, the authors show how government and non-government actors in different organisational settings devised techno-centric smart city calculations, which arose despite an initial human-centric vision.

While the case study allowed the study to illustrate the depth and richness of the context of the authoritarian Russian state where the role of citizens in public decision-making is rather limited, different and even contrasting results could be produced in other contexts.

There is a gap between a smart city vision and its grounding in calculations. Thus, the human-centric elements require special attention, and the organisation of the dialogue on smart city strategy must enable plurality of voices besides those of government actors.

The case suggests viewing the human-centric and techno-centric perspectives not as dichotomous, but rather emerging consecutively throughout the journey from an initial strategic vision to its implementation in the city's calculations.

The purpose of this article is to posit an alternative learning design approach to the technology-led magnification and multiplication of learning and to the linearity of curricular design approaches such as a constructive alignment. Learning design ecosystem thinking creates complex and interactive networks of activity that engage the widest span of the community in addressing critical pedagogical challenges. They identify the pinch-points where negative engagements become structured into the student experience and design pathways for students to navigate their way through the uncertainty and transitions of higher education at-scale.

It is a conceptual paper drawing on a deep and critical engagement of literature, a reflexive approach to the dominant paradigms and informed by practice.

Learning design ecosystems create spaces within at-scale education for deep learning to occur. They are not easy to design or maintain. They are epistemically and pedagogically complex, especially when deployed within the structures of an institution. As Gough (2013) argues, complexity reduction should not be the sole purpose of designing an educational experience and the transitional journey into and through complexity that students studying in these ecosystems take can engender them with resonant, deeply human and transdisciplinary graduate capabilities that will shape their career journey.

The paper is theoretical in nature (although underpinned by rigorous evaluation of practice). There are limitations in scope in part defined by the amorphous definitions of scale. It is also limited to the contexts of higher education although it is not bound to them.

This paper challenges the dialectic that argues for a complexity reduction in higher education and posits the benefits of complexity, connection and transition in the design and delivery of education at-scale.

Manufacturers face various challenges and risks during their digital servitization (DS), due to the complexity caused by introducing breakthrough technologies, increasingly complex product-service solutions and new stakeholders in the business network. The process necessitates the implementation of various changes that usually happen over a long period of time. Using complexity management as a theoretical lens, this paper delves into manufacturers’ DS journeys and explores how manufacturers manage the associated complexities.

This paper investigates the DS journey of two manufacturers in a longitudinal case study from 2014 to 2021.

Three main complexity management actions during the DS journey were identified: shaping the digital service system, shaping the organization and shaping the network. Tied to different types of complexities, these actions demonstrate how manufacturers navigate their journey. The findings also reveal different complexity management approaches used at the different stages of this journey.

This paper offers a comprehensive framework for understanding complexity management in the DS journey, including the types of complexities, complexity management actions and complexity management approaches and their rationale. This paper shows that different requirements are created during emerge, consolidate and evolve stages of the DS journey. Manufacturers need a dynamic approach that considers changes in complexities and actions over time.

India has been withstanding increasing pressure of enrolment in the higher education system, resulting in the creation of new universities in consonance with the recommendations of the Knowledge Commission (2007). Barring a few institutions of paramount excellence, the mushrooming universities fail to conform to equitability of quality and standards, that is teaching-learning-dissemination and research, except for accommodating higher gross enrolment ratio. It has resulted in an asymmetric and sporadic development of human resources, leaving a large basket of learners out of the pursuit for aspiring higher academic, research and professional enrichment. The country needs to develop an innovative common minimum curriculum and evaluation framework, keeping in view the trinity of diversity, equity and inclusion (DEI) across the Indian higher education system to deliver human resources with equitable knowledge, skill and intellectual acumen.

The paper has been developed using secondary information.

The manuscript has developed an innovative teaching-learning framework that would ensure every Indian HEI to follow a common minimum curriculum and partial common national evaluation system so that the learners across the country would enjoy the essence of equivalence.

This research has designed a comprehensive model to integrate the spirit of the “DEI” value proposition in developing curriculum and gearing common evaluation. This would enable the country to reinforce the spirit of social equity and the capacity to utilise resources with equitability and perpetuity.

With the development of social productive forces and the advancement of agricultural practices since the founding of New China, the connotation of agricultural modernization with Chinese characteristics has undergone a process from formation to continuous expansion and deepening.

Its evolution can be roughly divided into four stages: the exploration stage, the formation stage, the establishment stage and the deepening stage. The historical evolution of the connotation of agricultural modernization with Chinese characteristics demonstrates four typical characteristics, namely increasingly scientific logical premise, continuously diversified orientations, increasingly improved core contents and progressively maturing strategies of development.

The achievements of agricultural modernization have laid a solid foundation for China's industrial modernization and the rapid development of the national economy. Meanwhile, the authors have identified through practical exploration a path of agricultural modernization with Chinese characteristics. In recent years, academic research on the connotation of agricultural modernization with Chinese characteristics has gradually heated up, and relevant achievements have emerged constantly.

The Communist Party of China (hereinafter “CPC”) has placed considerable emphasis on agricultural issues and has been committed to promoting agricultural modernization since the founding of New China. Through long-term persistence and unremitting efforts, China has made remarkable achievements in agricultural development: significantly improved agricultural production conditions and agricultural output capacity, constantly optimized agricultural structure and steadily increased the income of farmers.

The purpose of this paper is to expose the playout of power dynamics when a new business intelligence (BI) system is implemented in a central pharmacy department in a National Health Service (NHS) hospital. The authors aim to explore the assumptions, experiences and actions of organisational stakeholders and ascertain how different professional groups obtain influence, power and control during this process.

In this research the authors employ structuration theory (ST), to establish how and where domination is achieved. To achieve this, the authors investigate the production and reproduction of structure as part of a longitudinal assessment using interviews and questionnaires.

Constant renewal and evolution are crucial in the implementation of a BI system. During the process of implementation and change many stakeholders resent the change. Disempowering these users leads to new power structures led by BI analysts.

The findings from this paper can help strengthen implications of BI systems implementation and better understand the impact these systems have on wider stakeholders. With coherent communication and an engaged attitude new BI systems can be implemented without alienating the key user stakeholders.

This paper differs from other papers by advocating that new systems and processes alter individual power structures in organisations, disrupting internal dynamics and introducing new aspects of control and dominance.

In the age of the Fourth Industrial Revolution, Industry 4.0 can increase the productivity and competitiveness of companies in the international marketplace. The purpose of this article is to investigate the drivers for and outcomes of the adoption of Industry 4.0 technologies in the case of a food processing company located in Italy.

The present work adopted a case study approach by investigating an Italian food processing company to investigate the drivers, challenges and outcomes of Industry 4.0 adoption in the agri-food sector.

This research highlighted drivers and challenges related to the adoption of different Industry 4.0 technologies. Secondly, it underlined the impacts of Industry 4.0 in terms of firm performance, operations management, human resource management and strategy.

Industry 4.0 technologies remain underexplored from the strategic perspective in the agri-food sector. This article provides preliminary evidence on the digital transformation of food processing companies, with a focus on Industry 4.0. Practical implications for managers, CEOs and entrepreneurs are discussed.

The study investigates the influence of ChatGPT on the labor market dynamics, aiming to provide a structured understanding of the changes induced by generative AI technologies.

An analysis of existing literature serves as the foundation for understanding the impact, while the supply and demand model helps assess the effects of ChatGPT. A text-mining approach is utilized to analyze the International Standard Occupation Classification, identifying occupations most susceptible to disruption by ChatGPT.

The study reveals that 32.8% of occupations could be fully impacted by ChatGPT, while 36.5% might experience a partial impact and 30.7% are likely to remain unaffected.

While this study offers insights into the potential influence of ChatGPT and other generative AI services on the labor market, it is essential to note that these findings represent potential implications rather than realized labor market effects. Further research is needed to track actual changes in employment patterns and job market dynamics where these AI services are widely adopted.

This paper contributes to the field by systematically categorizing the level of impact on different occupations, providing a nuanced perspective on the short- and long-term implications of ChatGPT and similar generative AI services on the labor market.

This paper aims to identify the factors that are constraining technological innovation to support the development of coalbed methane in China.

The analysis applies ideas relating to national and sector systems of innovation to explain why China’s strategies to support research and technological innovation have failed to stimulate the desired progress in coalbed methane production. It also provides a counter-example of the USA that implemented a number of measures in the 1970s that proved very effective.

The deficiencies of China’s research and development strategies in support of coalbed methane development reflect the national and sectoral systems of innovation. They are exacerbated by the structure of the national oil and gas industry. Key constraints include the excessively top-down management of the national R&D agenda, insufficient support for basic research, limited collaboration networks between companies, research institutes and universities and weak mechanisms for diffusion of knowledge. The success of the USA was based on entirely different systems for innovation and in quite a different industrial setting.

The originality of this analysis lies in placing the challenges facing research and innovation for China’s coalbed methane development in the context of the national and sectoral systems for innovation and comparing with the approach and success of the USA.

Recent advancements in Artificial Intelligence (AI) and, at its core, Machine Learning (ML) offer opportunities for organizations to develop new or enhance existing capabilities. Despite the endless possibilities, organizations face operational challenges in harvesting the value of ML-based capabilities (MLbC), and current research has yet to explicate these challenges and theorize their remedies. To bridge the gap, this study explored the current practices to propose a systematic way of orchestrating MLbC development, which is an extension of ongoing digitalization of organizations.

Data were collected from Finland's Artificial Intelligence Accelerator (FAIA) and complemented by follow-up interviews with experts outside FAIA in Europe, China and the United States over four years. Data were analyzed through open coding, thematic analysis and cross-comparison to develop a comprehensive understanding of the MLbC development process.

The analysis identified the main components of MLbC development, its three phases (development, release and operation) and two major MLbC development challenges: Temporal Complexity and Context Sensitivity. The study then introduced Fostering Temporal Congruence and Cultivating Organizational Meta-learning as strategic practices addressing these challenges.

This study offers a better theoretical explanation for the MLbC development process beyond MLOps (Machine Learning Operations) and its hindrances. It also proposes a practical way to align ML-based applications with business needs while accounting for their structural limitations. Beyond the MLbC context, this study offers a strategic framework that can be adapted for different cases of digital transformation that include automation and augmentation of work.