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Integrating additive manufacturing (AM) tools in traditional mold-making provides complex yet affordable sand molds and cores. AM processes such as selective laser sintering (SLS) and Binder jetting three-dimensional printing (BJ3DP) are widely used for patternless sand mold and core production. This study aims to perform an in-depth literature review to understand the current status, determine research gaps and propose future research directions. In addition, obtain valuable insights into authors, organizations, countries, keywords, documents, sources and cited references, sources and authors.

This study followed the systematic literature review (SLR) to gather relevant rapid sand casting (RSC) documents via Scopus, Web of Science and EBSCO databases. Furthermore, bibliometrics was performed via the Visualization of Similarities (VOSviewer) software.

An evaluation of 116 documents focused primarily on commercial AM setups and process optimization of the SLS. Process optimization studies the effects of AM processes, their input parameters, scanning approaches, sand types and the integration of computer-aided design in AM on the properties of sample. The authors performed detailed bibliometrics of 80 out of 120 documents via VOSviewer software.

This review focuses primarily on the SLS AM process.

A SLR and bibliometrics using VOSviewer software for patternless sand mold and core production via the AM process.

This study aims to propose and evaluate the progress in the basic-pixel (a strategy to generate continuous trajectories that fill out the entire surface) algorithm towards performance gain. The objective is also to investigate the operational efficiency and effectiveness of an enhanced version compared with conventional strategies.

For the first objective, the proposed methodology is to apply the improvements proposed in the basic-pixel strategy, test it on three demonstrative parts and statistically evaluate the performance using the distance trajectory criterion. For the second objective, the enhanced-pixel strategy is compared with conventional strategies in terms of trajectory distance, build time and the number of arcs starts and stops (operational efficiency) and targeting the nominal geometry of a part (operational effectiveness).

The results showed that the improvements proposed to the basic-pixel strategy could generate continuous trajectories with shorter distances and comparable building times (operational efficiency). Regarding operational effectiveness, the parts built by the enhanced-pixel strategy presented lower dimensional deviation than the other strategies studied. Therefore, the enhanced-pixel strategy appears to be a good candidate for building more complex printable parts and delivering operational efficiency and effectiveness.

This paper presents an evolution of the basic-pixel strategy (a space-filling strategy) with the introduction of new elements in the algorithm and proves the improvement of the strategy’s performance with this. An interesting comparison is also presented in terms of operational efficiency and effectiveness between the enhanced-pixel strategy and conventional strategies.

The study aims to identify the practical borders of AI legal personality and accountability in human-centric services.

Using a framework tailored for AI studies, this research analyses structured interview data collected from auditors based in Poland.

The study identified new constructs to complement the taxonomy of arguments for AI legal personality: cognitive strain, consciousness, cyborg paradox, reasoning replicability, relativism, AI misuse, excessive human effort and substitution.

The insights presented herein are primarily derived from the perspectives of Polish auditors. There is a need for further exploration into the viewpoints of other key stakeholders, such as lawyers, judges and policymakers, across various global contexts.

The findings of this study hold significant potential to guide the formulation of regulatory frameworks tailored to AI applications in human-centric services. The proposed sui generis AI personality institution offers a dynamic and adaptable alternative to conventional legal personality models.

The outcomes of this research contribute to the ongoing public discourse on AI’s societal impact. It encourages a balanced assessment of the potential advantages and challenges associated with granting legal personality to AI systems.

This paper advocates for establishing a sui generis AI personality institution alongside a joint accountability model. This dual framework addresses the current uncertainties surrounding human, general AI and super AI characteristics and facilitates the joint accountability of responsible AI entities and their ultimate beneficiaries.

This study aims to investigate the impact of organizational mistreatments, such as workplace bullying and ostracism, on task performance. Additionally, it explores the mediating role of cyber loafing and the moderating role of negative reciprocity beliefs.

Adopting a positivism research philosophy and a deductive approach, this study focuses on the petroleum sector-related organizations where enterprise resource planning requires active Internet use, potentially leading to cyber loafing. Data were collected from employees using purposive sampling techniques, resulting in a sample size of 248. The data analysis was conducted using AMOS and SPSS software.

The results indicate that cyber loafing fully mediates the relationship between workplace bullying and ostracism on task performance. Additionally, negative reciprocity beliefs significantly moderate the relationship between workplace bullying, ostracism and cyber loafing.

This study presents a moderated-mediation model of organizational mistreatments and task performance, elucidating the mechanisms through the mediating role of cyber loafing and the moderating role of negative reciprocity beliefs.

The purpose of this study is to explore the effect of different post-curing times on the mechanical properties, specifically the visco-elastic characteristics, Poisson’s ratio and modulus, of three dimensional (3D) printed photopolymer composites (PPCs) reinforced with short glass fibres (SGF).

This research uses digital light processing-based Vat-photopolymerization process to 3D print PPCs reinforced with SGF at volume fractions of 2%, 4%, 6% and 8%. An inter-stage stirring process was used to reinforce the SGFs in a layer wise fashion. After printing, the parts undergo post-curing for 20, 60 and 100 min. The mechanical properties are then analyzed using dynamic mechanical analysis and in situ optical measurements. In addition, two-dimensional strain mapping from digital image correlation techniques is used to assess the structural behavior.

This study found that composites with 4% SGF reinforcement achieved the highest storage modulus, approximately 1,550 MPa, after 60 and 100 min of post-curing. In addition, the Poisson’s ratio of these composites increased from 0.2 to 0.41 with rising temperature. By applying Poisson’s ratio correction, the modulus was observed to be 1,800 MPa. These results indicate that optimal SGF content and post-curing times significantly enhance the mechanical properties of 3D-printed PPCs.

This research uniquely combines the reinforcement of photopolymers with SGFs at varying volume fractions and the detailed analysis of post-curing times to enhance the mechanical properties of 3D printed PPCs.

Enterprise social media (ESM) are expressive spaces where users exchange emotional workplace communication. While some studies have explored how positive emotions may be contagious, little research explored the notion that negative communication may accumulate on enterprise social media. This study explores perceived negativity bias and its correlates in the context of ESM.

This study relies on survey data collected from 599 employees of a global organization. The response rate was 18.7%. Structural equation modeling was used to test the hypotheses.

The results contribute to research on ESM by demonstrating that perceived negativity bias is positively related to feelings of accountability and negatively associated with social support. Furthermore, the results indicate that unmet communication expectations on ESM can have implications for perceived social support beyond online contexts and accountability through perceived negativity bias.

The findings demonstrate how employees' unmet expectations about ESM use increase feelings that a digital environment is disproportionately negative, which may create an “unsafe” space for employees and a fear of being held accountable for their contributions. This study highlights how the Expectation-Disconfirmation Theory provides a fruitful framework for studying enterprise social technologies.

This study suggests that work is not merely a rational endeavor, and that emotions and personal feelings (including negative ones) may shape workplace communication on ESM. We contribute to research on ESM use by using the Expectation-Disconfirmation Theory as a lens to study antecedents and implications of perceived negativity bias.

India liberalized its economy in 1991, which resulted in intense global competition, quality-conscious and demanding customers. Additionally, significant technological advancements lead to enhancements in products and processes. These forced Indian organizations to adopt innovative business strategies in the past 30 years. Meanwhile, the Lean Six Sigma methodology has significantly grown with vast applicability during the past 30 years. Thus, the purpose of this study is to develop the learning on Lean Six Sigma methodology in the Indian context through investigation of literature.

A three-stage systematic literature review approach was adopted to investigate the literature during the present study. In total, 187 articles published in 62 journals/conference proceedings from 2005 to 2022 (18 years) were shortlisted. The first part of the article summarizes the significant milestones towards the quality journey in the Indian context, along with the evolution of the Lean Six Sigma methodology. The second part examines the shortlisted papers on Lean Six Sigma frameworks, their applicability in industrial sectors, performance metrics, outcomes realized, publication trends, authorship patterns and leading researchers from the Indian perspective.

Lean Six Sigma has emerged as a highly acclaimed and structured business improvement strategy worldwide. The Indian economy has seen remarkable growth in the past decade and is one of the fastest-growing economies in the 21st century. Lean Six Sigma implementation in India has significantly increased from 2014 onward. The study revealed that researchers have proposed several different frameworks for Lean Six Sigma implementation, the majority of which are conceptual. Furthermore, the balanced applicability of Lean Six Sigma in manufacturing and service sectors was observed with the highest implementation in the health-care sector. Additionally, the widely adopted tools, techniques along with performance metrics exploring case studies were reported along with a summary of eminent and leading researchers in the Indian context.

This study is confined to reviewed papers as per the research criteria with a significant focus on the Indian context and might have missed some papers due to the adopted papers selection strategy.

The present study is one of the initial attempts to investigate the literature published on Lean Six Sigma in the Indian context, including perspective on the Indian quality movement. Therefore, the present study will provide an understanding of Lean Six Sigma methodology in the Indian context to graduating students in engineering and management and entry-level executives. The analysis and findings on Lean Six Sigma frameworks, research approach, publications details, etc., will be helpful to potential research scholars and academia. Additionally, analysis of case studies on Lean Six Sigma implementation by Indian industries will assist the managers and professionals in decision making.

Over the past decade, artificial intelligence (AI) technologies have rapidly advanced organizational management, with many organizations adopting AI-based algorithms to enhance employee management efficiency. However, there remains a lack of sufficient empirical research on the specific impacts of these algorithmic management practices on employee behavior, particularly the potential negative effects. To address this gap, this study constructs a model based on the psychological ownership theory, aiming to investigate how algorithmic management affects employees’ knowledge hiding.

This study validates the model through a situational experiment and a multi-wave field study involving full-time employees in organizations implementing algorithmic management. Various analytical methods, including analysis of variance, regression analysis and path analysis, were used to systematically test the hypotheses.

The study reveals that algorithmic management exerts a positive indirect influence on knowledge hiding through the psychological ownership of personal knowledge. This effect is particularly pronounced when employees have lower organizational identification, highlighting the critical role of organizational culture in the effectiveness of technological applications.

This study is among the first empirical investigations to explore the relationship between algorithmic management and employee knowledge hiding from an individual perception perspective. By applying psychological ownership theory, it not only addresses the current theoretical gap regarding the negative effects of algorithmic management but also provides new theoretical and empirical support for the governance and prevention of knowledge hiding within organizations in the context of AI algorithm application. The study highlights the importance of considering employee psychology (i.e. psychological ownership of personal knowledge) and organizational culture (i.e. organizational identification) under algorithmic management. This understanding aids organizations in better managing knowledge risks while maximizing technological advantages and effectively designing organizational change strategies.

Since previous literature provides fragmented and conflicting results about the use of digital data for product innovation, the article aims to comprehensively explore and shed light on how agri-food firms utilise external and internal digital data sources when dealing with different product innovations, such as incremental, architecture and radical innovation.

This paper adopts an exploratory multiple-case study and a theory-building process, focussing on the agri-food industry. We collected primary and secondary data from eight manufacturing companies.

The findings of this research show an empirical framework of six agri-food firms’ digital data utilisation behaviours: the supervisor, the passive supervisor, the developer, the passive developer, the pathfinder and the conjunction behaviour. These digital data utilisation behaviours vary according to a combination of data sources, such as internal data related to inside phenomenon measures (e.g. data generated by sensors installed in the production plan) or external data (e.g., market trends, overall sector sales), and innovation purposes.

This article offers guiding principles that assist agri-food companies when utilising internal and external digital data sources for specific product innovation outcomes such as incremental, architectural and radical innovation.

The significance of external and internal data sources in stimulating product innovation has garnered substantial attention within academic discussions, highlighting the critical importance of analysing digital data for driving such innovation. Nonetheless, the predominant approach is to study a single innovation outcome through the lens of digital technology. In contrast, our study stands out by adopting a fundamental perspective on data sources, enabling a more nuanced explanation of the overall product innovation outcomes within the agri-food sector.

The purpose of this study is AlZn-based produced by high speed mechanical alloying method to investigate the effect of Zn amount on the composite material, Al-Zn-Mg-Cu-SiC composite material billets are obtained by sintering.

Mechanical alloying, X-ray diffraction analysis (XRD) graphics, sintering, polishing, scanning electron microscopy and energy dispersive X-ray analyzer (EDX) images and micro hardness tests were applied, respectively.

In the XRD analysis results, it was observed that the Al peak height decreased as the alloying time increased. When the samples sintered for 90 min are examined, it can be clearly seen that the hardness increases as the Zn ratio increases. EDX analysis results also support XRD results.

Increase in strength will require the use of thinner sheet metal and smaller rivets to achieve the same strength. This will reduce the weight of the aircraft. Weight reduction also means less fuel consumption and more economical flight. This increase in strength is a very important scientific achievement.