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How does the interaction between time and knowledge affect the evolution of organizations? Past research in organizational evolution has mostly investigated time and knowledge as two separate variables. In contrast, theoretical perspectives integrating these variables are still seemingly scant. The authors believe that filling this literature gap needs attention. Thus, this study aims to contribute by developing a conceptual framework.

This is a conceptual study. The framework is centred on the concept of “co-evolutionary time”, which the authors explain through a business example from the tourism industry. Supported by a narrative-based style, from a methodological point of view the framework is featured by the attempt to synthesize specific, extant literature into new theoretical development.

As its main theoretical contribution, the co-evolutionary time suggests how firms can adapt in a way that, from an evolutionary perspective, proves fitting both in terms of contents and methods, thus opening possibilities for new long-term social construction and reconstruction. As its main practical contribution, co-evolutionary time can constitute not only a temporary source of organizational success and competitive advantage but also an agent of enduring change and long-term business survival.

As its main novelty, the framework is developed through merging two literature streams. In particular, the authors first consider the literature about time, with a focus on its objective and subjective dimensions. The authors then consider the literature about organizational evolution, with a focus on the co-evolutionary nature of the firm/environment relationship.

This paper aims to develop a method based on artificial intelligence capable of predicting the due date (DD) of job shops in real-time, aiming to assist in the decision-making process of industries.

This paper chooses to use the methodological approach Design Science Research (DSR). The DSR aims to build solutions based on technology to solve relevant issues, where its research results from precise methods in the evaluation and construction of the model. The steps of the DSR are identification of the problem and motivation, definition of the solution’s objectives, design and development, demonstration, evaluation of the solution and the communication of results.

Along with this work, it is possible to verify that the proposed method allows greater accuracy in the DD definition forecasts when compared to conventional calculations.

Some limitations of this study can be pointed. It is possible to mention questions related to the tasks to be informed by users, as they could lead to problems in the performance of the artifact as the input data may not be correctly posted due to the misunderstanding of the question by part of the users.

The proposed artifact is a method capable of contributing to the development of the manufacturing industry to improve the forecast of manufacturing dates, assisting in making decisions related to production planning. The use of real production data contributed to creating, demonstrating and evaluating the artifact. This approach was important for developing the method allowing more reliability.

The criticality of late deliveries in transportation lies in the threat of considerable multi-level supply chain costs. This study aims to reveal the dynamic capabilities playing a facilitating role in preventing delay, thus providing timely delivery, as well as developing an understanding of how and when those capabilities are activated within the supply chain network.

An exploratory study was conducted involving 16 semi-structured expert interviews with the representatives of logistics service providers and shippers. Following an interpretive phenomenology framework, the prevention phenomenon was explained.

Findings revealed two preventive capability categories in delay prevention: (1) proactive capabilities, referring to the enabling actions planned before departure, and (2) reactive capabilities, referring to actions planned after departure. Findings pinpoint that, in addition to the proactive capabilities, reactive capabilities enabled by innovative problem-solving actions are crucial for adapting to a dynamically changing environment in prevention. Moreover, this study shows that prevention capabilities are characterized by tangible and intangible resources and integration of resources with external links which constitute a delay prevention network within a wider service ecosystem.

This study stands out with its specific focus on delay prevention capabilities and enabling actions from the perspectives of logistics service providers and shippers. The premises of the resource-based view are combined with dynamic capabilities theory, leading to a proposed time-based taxonomy of proactive and reactive capabilities in supply chains, aimed at creating value and strengthening resilience.

Today the role of industry 4.0 plays a very important role in enhancing any supply chain network, as the industry 4.0 supply chain uses Big Data and advanced analytics to inform the complete visibility. Latest data are available to bring clarity and support real-time decision-making in the entire supply chain that’s why adopting optimization techniques such as lean manufacturing and lean supply chain concept for enhancing the supply chain network of the organizations is a good idea and would benefit them in increasing their cost efficiency and productivity. The purpose of this work is to develop a technique, which may be useful for future researchers and managers to identify and classification of the significant lean supply chain enablers.

In this paper, the authors considered hybrid analytical hierarchy process to find the ranking of the identified lean supply chain enablers by calculating their weightage. Interpretive structural modeling (ISM) is applied to develop the structural interrelationship among various lean supply chain management enablers. Considering the results obtained from ISM the Matrices d'Impacts Croises Multiplication Appliqué a un Classement (MICMAC) analysis is done to identify the driving and dependence power of Lean Supply Chain Management Enablers (LSCMEs).

Further, the best results applying these methodologies could be used to analyze their inter-relationships for successful Lean supply chain management implementation in an organization. The authors developed an integrated model after the identification of 20 key LSCMEs, which is very helpful to identify and classify the important enablers by ISM methodology and explore the direct and indirect effects of each enabler by MICMAC analysis on the LSCM implementation. This will help organizations optimize their supply chain by selective control of lean enablers.

For lean manufacturing practitioners, the result of the study can be beneficial where the manufacturer is required to increase efficiency and reduce cost and wastage of resources in the lean manufacturing process, as well as in enhancing the supply chain.

This paper is the first research paper that considered firstly deep literature review of identified lean supply chain enablers and second developed structured modeling of various lean enablers of supply chain with the help of various methodologies.

Endeavoring to expand their global market presence, firms often launch products into emerging markets where managers face the daunting task of deploying products by managing available, and often limited, supply chain resources. Yet, literature has not empirically examined managerial resource orchestration in this context. Accordingly, by embedding resource orchestration theory (ROT) into the emerging market context, the authors offer middle-range theorizing on supply chain resource orchestration (SCRO) and empirically test how acquiring, bundling and leveraging activities impact new product launch performance.

The authors test the model by analyzing empirical data from 175 individual product launches into emerging markets using a survey methodology.

The authors’ results suggest that SCRO holds the promise of being a viable middle-range theory in the supply chain field, especially where managers face limited resources and must “work with what they have to do what they can.”

The authors’ study also has some limitations. First, because a panel data service company was used to collect the data, the authors were not provided with any information regarding the respondents' company names or other identifying data. Second, because the authors did not directly interact with the respondents nor were the authors able to contact multiple individuals from their respective organizations, the study was limited to a single-respondent design. However, to counter issues associated with single-response bias, the central constructs in the study referenced phenomena related to a specific product launch project as opposed to constructs at the firm or inter-firm relational level.

The authors’ results reveal that SCRO activities can enhance the performance of new product launches, even in resource-starved emerging market contexts.

The results validate measures for several of the SCRO processes (i.e. supply chain resource acquisition, supply chain resource bundling and supply chain leveraging) and provide evidence that supply chain resource bundling and supply chain leveraging mediate the relationship between supply chain resource acquisition and product launch performance. Further, soft logistics infrastructure is found to be an important boundary condition for these relationships.

With the deep development of the new technological revolution and industrial transformation, the development, application, expansion and integration of digital technology provide opportunities for transforming the manufacturing industry from traditional manufacturing to intelligent manufacturing. However, little research currently focuses on analyzing the influencing factors of intelligent development in this field. There is a lack of research from the perspective of the digital innovation ecosystem to explore the intrinsic mechanism that drives intelligent development. Therefore, this article starts with high-end equipment manufacturing enterprises as the research subject to explore how their digital innovation ecosystem promotes the effectiveness of enterprise intelligent development, providing theoretical support and policy guidance for enterprises to achieve intelligent development at the current stage.

This article constructs a logical framework for the digital innovation ecosystem using a “three-layer core-periphery” structure, collects data using crawling for subsequent indicator measurement and assessment and uses the fuzzy set Qualitative Comparative Analysis method (fsQCA) to explore how the various components of the digital innovation ecosystem in high-end equipment manufacturing enterprises work together to promote the development of enterprise intelligently.

This article finds that the various components of the digital innovation ecosystem of high-end equipment manufacturing enterprises, through mutual coordination, can help improve the level of enterprise intelligence. Empirical analysis shows four specific configuration implementation paths for the digital innovation ecosystem of high-end equipment manufacturing enterprises to promote intelligent development. The core conditions and their combinations that affect the intelligent development of enterprises differ in each configuration path.

Firstly, this article discusses the practical problems of intelligent transformation and development in the manufacturing industry and focuses on the intelligent development effectiveness of various components of the digital innovation ecosystem of high-end equipment manufacturing enterprises in the context of digitalization. Secondly, this article uses crawling, text sentiment analysis and other methods to creatively collect relevant data to overcome the research dilemma of being limited to theoretical analysis due to the difficulty in obtaining data in this field. At the same time, based on the characteristics of high-end equipment manufacturing enterprises, the “three-layer core-periphery” digital innovation ecosystem framework constructed in this article helps to gain a deep understanding of the development characteristics of the industry's enterprises, provides specific indicator analysis for their intelligent development, opening the “black box” of intelligent development in the industry's enterprises and bridging the gap between theory and practice. Finally, this study uses the fsQCA research method of configuration analysis to explore the complexity of the antecedents and investigate the combined effects of multiple factors on intelligent development, providing new perspectives and rich research results for relevant literature on the intelligent development of high-end equipment manufacturing enterprises.

This case explores how driver training school create experience value for their trainees. It describes the development of driver training industry, the foundation and new training mode of Rongan Driving School, changes and challenges of environment for Rongan facing and so on, which will guide readers to discuss six influence factors of customer experience, six dimensions of customer-experience value, the relationship between them, and the influence of social environment. Rongan's innovative training mode of “pay after learning, time-based billing, one car for one person”, provides a good training experience for driving trainees. It has become the benchmark of the national driving training industry within six years.

Multiple topics often exist on social media platforms that compete for users' attention. To explore how users’ attention transfers in the context of multitopic competition can help us understand the development pattern of the public attention.

This study proposes the prediction model for the attention transfer behavior of social media users in the context of multitopic competition and reveals the important influencing factors of users' attention transfer. Microblogging features are selected from the dimensions of users, time, topics and competitiveness. The microblogging posts on eight topic categories from Sina Weibo, the most popular microblogging platform in China, are used for empirical analysis. A novel indicator named transfer tendency of a feature value is proposed to identify the important factors for attention transfer.

The accuracy of the prediction model based on Light GBM reaches 91%. It is found that user features are the most important for the attention transfer of microblogging users among all the features. The conditions of attention transfer in all aspects are also revealed.

The findings can help governments and enterprises understand the competition mechanism among multiple topics and improve their ability to cope with public opinions in the complex environment.

Competitive tendering in South Africa is often associated with procurement based on the lowest fee tendered. Previous research on this topic did not provide in-depth examinations of how pricing within consulting engineering companies was affected by competitive tendering nor did it illuminate the extent to which professional services were impacted by competitive tendering. This paper aims to examine the implications of competitive tendering on pricing and delivery of consulting engineering services in South Africa.

A survey research strategy with a questionnaire as the research instrument elicited qualitative data from 28 experienced consulting engineers in South Africa. Thematic analysis was used to analyse qualitative data from the questionnaires.

Three key themes were identified, namely: considerations when determining consulting engineering fees on competitively tendered projects; the impact of reduced fees due to competitive tendering on the delivery of consulting engineering services; and interventions to prevent unsustainably “low” professional fees. Many consulting engineers in South Africa still determine fees using fee scales, while other considerations include resources, project complexity, risk, etc. Most participants asserted that design optimisation/value engineering, training, meetings and construction monitoring were adversely impacted by “low” fees.

This paper provides in-depth qualitative feedback from experienced consulting engineers (most having more than 20 years’ experience) on a topical issue in the South African construction industry. Thematic analysis was a novel method of analysis that was not used previously in this area of study.

This case describes the growth of ReNew Power during its first decade of operation. Sumant Sinha, a first-generation entrepreneur and former banker, founded the company, which grew from a modest generator-cum-developer of wind energy-based electricity to one of India's largest companies in the renewable energy sector. With the entry of large, well-funded players such as Tata Power and Adani Green into the Indian renewable sector by the end of 2020, Sinha had to make a strategic decision: should ReNew continue to organically scale up its presence in an increasingly competitive yet expanding Indian renewable energy sector, should it diversify geographically, or should it pursue emerging opportunities for vertical or horizontal integration within the sector? The case provides an opportunity to discuss how alternative business models and competitive scenarios may facilitate or inhibit the growth of a player in the renewable energy sector.