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This paper aims to explore the integration challenges during the early stages of implementation of value streams as team aggregation structures as a novel organizational construct in a modern organization.

We use an immersive ethnographic approach to follow the transition to value streams as team aggregation structures in a large organization during the first three years of implementation. We integrate systematic observations with interviews to get insights into the dynamics of change and the most important challenges faced by the organization during this transition.

We integrate systematic observations collected during the organizational change with insights from interviews carried out with managers to provide tentative answers to some key questions related to the implementation of multiteam systems. We reflect on their performance, entitativity, autonomy as well as on the satisfaction of their members.

We discuss some of the most important managerial challenges during the transition to value streams as novel organizational constructs and we derive some actionable insights for team and value stream managers leading such change processes.

Our study provides a rich account of the first stages of implementing an organizational design that brings together different teams in organizational structures that are focused on the value provided to customers.

With the increasing popularity of live streaming, the exploration of online behaviors on live streaming platforms has become a research hot spot in academia. However, the literature lacks cross-cultural investigations into live streaming addictive (LSA) behaviors among viewers. To address this gap, based on stimulus-organism-response (SOR) theory and motivation theory, this study investigates how information seeking, sensation seeking and community commitment (stimulus) affect hedonic and utilitarian motivation (organism), which in turn influence viewers’ LSA behaviors (response). This study also compares American and Chinese viewers to understand how LSA behaviors differ across national cultures.

We surveyed live stream viewers in both nations, collecting 758 valid samples including 377 Chinese respondents and 381 American respondents for use with the research model. A partial least squares (PLS) approach is employed for data analysis.

The results show that information seeking increases utilitarian motivation, sensation seeking increases hedonic motivation, and community commitment strengthens both hedonic and utilitarian motivation. Both hedonic motivation and utilitarian motivation positively affect LSA behaviors. In addition, based on cross-cultural analysis, we found that there was a significant mediating path common to the respondents in China and the USA (i.e. sensation seeking ? Hedonic motivation ? LSA).

The findings of this study make theoretical contributions to live streaming research and offer practical guidance for live streaming firms to formulate proper marketing strategies. Last, this study outlines several limitations and possible directions for future research.

In the actual livestreaming background, to obtain more income, some broadcasters will transform their original single role orientation into mixed one. This research study aims to conduct an empirical study on the influence of the broadcasters' role orientation transformation on the viewers' tipping behavior.

The authors collect data from Kuai, a leading online live streaming service provider in China. The dataset includes 175,701 live streaming data from 971 broadcasters in 7 months. To avoid unobservable factors, the authors adopt two difference-in-differences (DID) models to estimate the effect of two kinds of broadcaster's role orientation transformation on the broadcaster’s direct income separately. And the authors use the Heckman-type correction to solve broadcasters’ self-selected problem.

The authors evaluated that there is a U-shape relationship between the broadcasters' role orientation transformation and their direct income. The broadcasters' direct income experienced a sharp decline for a short period of time after transformation and followed by a rise after a period of adaptation. And for broadcasters with different genders and amounts of fans, the influence degree of role orientation transformation is various.

This paper provides a fresh usage of the regulatory engagement theory in the brand new information communication technology. And it also explores the boundary effect of the participating object's self-factors in the regulatory engagement theory. Besides, this paper expands the research of livestreaming into natural background. Such results also provide operable suggestions for the livestream platform, the broadcaster himself and the enterprises who want to employ some broadcasters to recommend their products.

This study adopts the elaboration likelihood model and configuration perspectives to explore the internal mechanisms underlying the influence of live streaming on consumer trust building and purchase intention.

This study invited 757 experienced live streaming e-commerce users from Chinese platforms such as TikTok and RED, who participated in survey by filling questionnaires collected online. The research employed a mixed-method approach using SEM and fsQCA. SEM was utilized to analyze quantitative data to determine the direct and mediated relationships within product trust, while fsQCA served as a complement to identify the combinations of conditions that enhance product trust.

The findings reveal three important insights. Firstly, in the context of live streaming e-commerce, both product characteristics and streamer characteristics significantly influence consumers' trust in products. The para-social interaction plays a partial mediating role in the relationship between streamer characteristics and product trust. Secondly, four distinct paths are identified that contribute to enhancing product trust in live streaming e-commerce. Thirdly, PSI emerging as a core condition across all four paths, underscores the importance for merchants to foster positive social interactions with consumers beyond the live streaming environment.

This study enhances understanding of the dynamic live streaming e-commerce industry, offering insights into consumer behavior and practical guidance for merchants seeking to build engaged, trustworthy customer relationships.

Live Streaming Marketing has emerged as a transformative medium, facilitating real-time product promotion and brand messaging and reshaping consumer engagement. However, knowledge of the impact of Store Atmospheric cues within live streaming contexts remains scarce. This research delves into the dynamic interplay between streamers and viewers across diverse live streaming platforms, with a focus on the impact of distinct atmospheric cues. It also seeks to explore prosocial behavior and integrate elements of social comparison theory.

We conducted semi-structured interviews with 14 streamers and 26 viewers. Participants who were active on streaming platforms and had experience of multiple live streaming sessions were purposively identified. The thematic coding approach and NVivo 12 software were employed to gain a nuanced understanding of live streaming dynamics.

Our findings highlight the significant role of emerging atmospheric cues in shaping immersive streaming experiences and fostering prosocial behavior. Additionally, we observed three formats of upward social comparisons between streamers and viewers, wherein viewers compared themselves with streamers and peers, and streamers engaged in comparisons with more experienced counterparts. This finding contributes to a sense of digital community and positive interactions because of live streaming adoptions.

By extending the application of social comparison theory, this study provides valuable insights for practitioners and scholars, enriching the understanding of both streamers’ and viewers’ psychological behavior and the dynamics of virtual retail settings.

Organizations are continually improving their practices to improve operational performance. They already employ Lean Manufacturing techniques (LM) to reduce unnecessary waste. Industry 4.0 techniques enhance operational performance in association with LM. Despite the proven benefits of LM principles and the advancements offered by Industry 4.0 technologies, many organizations struggle to integrate these approaches effectively. This research paper explores how LM principles can be combined with Industry 4.0 technologies to provide valuable guidance for businesses looking to adopt lean automation strategies.

A systematic literature review on LM and Industry 4.0 was done to investigate the possible technical integration of both methods. Ninety-two articles are extracted systematically from the Scopus and Web of Science databases. This study states a systematic literature review, including quantitative analysis of bibliographic networks and cluster analysis, to identify emergent ideas and their further implementation.

The research findings highlight the positive impact of integrating lean production with Industry 4.0 techniques, benefiting organizations in achieving their goals. A lean automation integration framework is proposed based on the literature review and the findings.

This study provides industry administrators and practitioners valuable guidance for enhancing organizational productivity. These implications can provide businesses with competitive advantages, enhance customer satisfaction, and enable them to adapt to the dynamic demands of the contemporary business environment.

This literature review study has substantially contributed to the technological integration of lean and Industry 4.0. The work has also identified potential emerging areas that warrant further research.

This study aims to improve the market competitiveness of iron and steel manufacturers in developing countries by reducing their production costs.

The research methodology relies on a case study-based approach. The study relies on six steps. The first is the preparation, then the five steps of the six-sigma – define, measure, analyze, improve, control. The qualitative and quantitative data were considered. The qualitative analysis relies on the experts’ judgment of internal status. The quantitative analysis uses the job floor data from three iron and steel manufacturers. After collecting, screening and analyzing the data, the root causes of the different wastes were identified that increase production costs. Consequently, lean manufacturing principles and tools are identified and prioritized using the decision-making trial and evaluation laboratory method, and then implemented to reduce the different types of waste.

The main wastes are related to inventory, time, quality and workforce. The lean tools were proposed with the implementation plan for the discovered root causes. The performance was monitored during and after the implementation of the lean initiatives in one of the three companies. The obtained results showed an increase in some performance indicators such as throughput (70.6%), revenue from by-products (459%), inventory turnover (54%), operation availability (45%), and plant availability (41%). On the other hand, results showed a decrease of time delay (78%), man-hour/ton (52.4%) and downgraded products (63.3%).

The current case study findings can be utilized by Iron and Steel factories at the developing countries. In addition, the proposed lean implementation methodology can be adopted for any other industries.

The current work introduces an original and practical road map to implement the lean six-sigma body of knowledge in the iron and steel manufacturers.

This work introduces an effective and practical case study-based approach to implementing the lean six-sigma body of knowledge in the iron and steel manufacturers in one of the underdevelopment countries. The consideration of the opinion of the different engineers from different sectors shows significant identification of the major problems in the manufacturing and utility sectors that lead to significant performance improvement after solving them.

Lean manufacturing (LM) concepts have been widely adopted in diverse industrial sectors. However, no literature review focusing on case studies describing LM implementation is available. Case studies represent the actual implementation and provide secondary data for further analysis. This study aims to review the same to understand the pathways of LM implementation. In addition, it aims to analyse other related review questions, such as how implementing LM impacts manufacturing capabilities and the maturity level of manufacturing organisations that implemented LM, to name a few.

A literature review of case studies that discuss the implementation of LM during the last decade (from 2010 to 2020) is carried out. These studies were synthesised, and content analyses were performed to reveal critical insights.

The implementation pattern of LM significantly varies across manufacturing organisations. The findings show simultaneous improvement in manufacturing capabilities. Towards the end of the last decade, organisations implemented LM with radio frequency identification, e-kanban, simulation, etc.

Reviewing the case studies documenting LM implementation to comprehend the various nuances is a novel attempt. Furthermore, potential future research directions are identified for advancing the research in the domain of LM.

3D printing has been warmly welcomed by clothing enterprises for its customization capacity in recent years. However, such clothing enterprises have to face the digital transformation challenges brought by 3D printing. Since the business model is a competitive weapon for modern enterprises, there is a research gap between business model innovation and digital transformation challenges for 3D-printing garment enterprises. The aim of the paper is to innovate a new business model for 3D-printing garment enterprises in digital transformation.

A business model innovation canvas (BMIC), a new method for business model innovation, is used to innovate a new 3D-printing clothing enterprises business model in the context of digital transformation. The business model canvas (BMC) method is adopted to illustrate the new business model. The business model ecosystem is used to design the operating architecture and mechanism of the new business model.

First, 3D-printing clothing enterprises are facing digital transformation, and they urgently need to innovate new business models. Second, mass customization and distributed manufacturing are important ways of solving the business model problems faced by 3D-printing clothing enterprises in the process of digital transformation. Third, BMIC has proven to be an effective tool for business model innovation.

The new mass deep customization-distributed manufacturing (MDC-DM) business model is universal. As such, it can provide an important theoretical reference for other scholars to study similar problems. The digital transformation background is taken into account in the process of business model innovation. Therefore, this is the first hybrid research that has been focused on 3D printing, garment enterprises, digital transformation and business model innovation. On the other hand, business model innovation is a type of exploratory research, which means that the MDC-DM business model’s application effect cannot be immediately observed and requires further verification in the future.

The new business model MDC-DM is not only applicable to 3D-printing garment enterprises but also to some other enterprises that are either using or will use 3D printing to enhance their core competitiveness.

A new business model, MDC-DM, is created through BMIC, which allows 3D-printing garment enterprises to meet the challenges of digital transformation. In addition, the original canvas of the MDC-DM business model is designed using BMC. Moreover, the ecosystem of the MDC-DM business model is constructed, and its operation mechanisms are comprehensively designed.

Lean production has been proved to be a cost-effective and efficient means of production that reduces non-valve added activities. Industry 4.0 (I4.0) is a technology-driven platform that allows machines to interact with other systems through artificial intelligence, machine learning, industrial Internet of Things (IoT), etc. that improve the production system with flexibility, quality and customization throughout the whole value chain. New approaches to digitization of lean production have recently been emerged and they are transforming the industry and increasing productivity throughout the value chain. Through this article, an effort has been made to review the research published in this field.

This paper reviews the literature published in various journals, the databases Web of science (WoS), ScienceDirect, Scopus, Emerald etc. were referred with a focus on lean concepts and tools and I4.0 technologies; it has been noticed that the integration of the lean tools with I4.0 technologies is a very effective tool for the industry.

It has been found in the literature published earlier in various journals that lean manufacturing (LM) is commonly acknowledged and considered a best practice to improve the productivity. It is concerned with the tight integration of people into the industrial process through continuous improvement which leads to value addition throughout the whole value chain by eliminating non vale added activities. The findings show that organizations can improve their productivity and flexibility with speed and accuracy by integrating I4.0 technologies with LM, which is foremost need of any industry across the world.

This article accentuates the connections between the principles and tools developed under the umbrella of I4.0 and those developed by the LM techniques, with a specific emphasis on how some of the principles and tools of I4.0 improve the implementation of lean principles dependent on the competence levels of the technology. Very few articles have been published in this area, and this paper is an original piece of research covering a review of extant research published in various journals.