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This study aims to develop a holistic method that integrates finite element modeling, machine learning, and experimental validation to propose processing windows for optimizing the laser powder bed fusion (LPBF) process specific to the Al-357 alloy.

Validation of a 3D heat transfer simulation model was conducted to forecast melt pool dimensions, involving variations in laser power, laser scanning speed, powder bed thickness (PBT) and powder bed pre-heating (PHB). Using the validated model, a data set was compiled to establish a back-propagation-based machine learning capable of predicting melt pool dimensional ratios indicative of printing defects.

The study revealed that, apart from process parameters, PBT and PHB significantly influenced defect formation. Elevated PHBs were identified as contributors to increased lack of fusion and keyhole defects. Optimal combinations were pinpointed, such as 30.0 µm PBT with 90.0 and 120.0 °C PHBs and 50.0 µm PBT with 120.0 °C PHB.

The integrated process mapping approach showcased the potential to expedite the qualification of LPBF parameters for Al-357 alloy by minimizing the need for iterative physical testing.

Existing literature has begun to identify the agonistic and contested aspects of the ongoing development of accountability systems. These “contests” are particularly important during periods of change when an accountability “deficit” has been identified, that is, when existing accountability systems are deemed inadequate and requiring revision. The purpose of this paper is to explore one such set of contests in the case of large technology and social media firms: the so-called “big tech”. The authors focus specifically on “big tech” because of increasing societal concerns about the harms associated with their products, services and business practices.

The authors analysed four US Congressional hearings, in which the CEO of Facebook was held to account for the company's alleged breaches and harms. The authors conducted a discourse analysis of the dialogue between the account giver (Mark Zuckerberg) and account holders (Members of Congress) in the oral testimony at the four hearings.

Two areas of contestation in the dialogue between the account giver and account holders are identified. “Epistemic contests” involved contestation about the “facts” concerning the harms the company had allegedly caused. “Responsibility contests” involved contestation about who (or what) should be held responsible for these harms and according to what standards or criteria.

The study advances critical dialogical accountability literature by identifying two areas of contestation during periods of change in accountability systems. In so doing, they advanced the theory by conceptualising the process of change as underpinned by discursive contests in which multiple actors construct and contest the “problem” with existing accountability systems. The outcomes of these contests are significant, the authors suggest, because they inform the development of reforms to the accountability system governing big tech firms and other industries undergoing similar periods of contestation and change.

The purpose of this research is to enhance the Laser Powder Bed Fusion (LPBF) additive manufacturing technique by addressing its susceptibility to defects, specifically lack of fusion. The primary goal is to optimize the LPBF process using a digital twin (DT) approach, integrating physics-based modeling and machine learning to predict the lack of fusion.

This research uses finite element modeling to simulate the physics of LPBF for an AISI 316L stainless steel alloy. Various process parameters are systematically varied to generate a comprehensive data set that captures the relationship between factors such as power and scan speed and the quality of fusion. A novel DT architecture is proposed, combining a classification model (recurrent neural network) with reinforcement learning. This DT model leverages real-time sensor data to predict the lack of fusion and adjusts process parameters through the reinforcement learning system, ensuring the system remains within a controllable zone.

This study's findings reveal that the proposed DT approach successfully predicts and mitigates the lack of fusion in the LPBF process. By using a combination of physics-based modeling and machine learning, the research establishes an efficient framework for optimizing fusion in metal LPBF processes. The DT's ability to adapt and control parameters in real time, guided by machine learning predictions, provides a promising solution to the challenges associated with lack of fusion, potentially overcoming the traditional and costly trial-and-error experimental approach.

Originality lies in the development of a novel DT architecture that integrates physics-based modeling with machine learning techniques, specifically a recurrent neural network and reinforcement learning.

The concept of interestingness has been investigated in several management disciplines but studies mobilizing such concept in supply chain management (SCM) to develop strategies for the field's advancement are relatively scarce. This research paper aims to investigate how SCM scholars rank attributes of interestingness and the strategies to harness interestingness in the field of SCM.

The authors adopt a mixed methods research design in which a survey on SCM researchers' ranking of interestingness' attributes and qualitative interviews with selected academics are conducted.

The findings highlight the importance given by SCM scholars to attributes such as rigor, relevance, novelty and communication and how they are interrelated. Also, other interestingness attributes are underlined by scholars during the qualitative interviews including inquisitiveness, engaging the reader, imaginativeness and entertainment. Furthermore, a research agenda to synthesize the propositions to develop interesting research is also proposed.

Interestingness attributes such as rigor, relevance and novelty are discussed. Recommendations for interesting research are suggested which can be useful to scholars and journal editors. The findings of this research are also relevant for practitioners for a better understanding of academic/practice relationships to develop high impact collaboration.

This paper is among the few studies that focus on interestingness in SCM research from the perspective of scholars. In doing so, the authors seek to contribute to the classic debate in SCM field about “relevance-rigour” duality by providing a broader outlook based on interestingness and proposing a research agenda for prospective studies in the field.

Engaged employees assure organizational competitiveness and sustainability. The purpose of this study is to explore the relationship between job resources and employee turnover intentions, with employee engagement as a mediating variable.

Data were collected from 934 employees of eight wholly-owned pharmaceutical industries. The proposed model and hypotheses were evaluated using structural equation modeling. Construct reliability and validity was established through confirmatory factor analysis.

Data supported the hypothesized relationship. The results show that job autonomy and employee engagement were significantly associated. Supervisory support and employee engagement were significantly associated. However, performance feedback and employee engagement were nonsignificantly associated. Employee engagement had a significant influence on employee turnover intentions. The results further show that employee engagement mediates the association between job resources and employee turnover intentions.

The generalizability of the findings will be constrained due to the research’s pharmaceutical industry focus and cross-sectional data.

The study’s findings will serve as valuable pointers for stakeholders and decision-makers in the pharmacuetical industry to develop a proactive and well-articulated employee engagement intervention to ensure organizational effectiveness, innovativeness and competitiveness.

By empirically demonstrating that employee engagement mediates the nexus of job resources and employee turnover intentions, the study adds to the corpus of literature.

While COVID-19 mitigation measures (CMMs) aided in steady recovery during the pandemic, they also impeded movement across economies/borders, affecting quality assurance (QA) of Cross-border Construction Logistics and Supply Chain (Cb-CLSC). However, prior studies on the pandemic in the construction project industry have not revealed how CMMs have impacted QA. Thus, this study aims to evaluate the impact of the CMMs on the QA of Cb-CLSC.

This is achieved by adopting an embedded mixed-method approach involving a desk literature review and engaging 150 experts from different economies across the globe using expert surveys, and results verified via semi-structured expert interviews. Structural equation modelling-based multiple regression analysis (SEM-MRA) was integrated to examine the impact of the CMMs on the QA, along with descriptive and content analysis.

The study confirmed that CMMs have not only impacted the QA negatively but also influenced the positioning of the QA for the post-pandemic era and probably to survive the risks of future pandemics. Among all the identified CMMs, the top three critical measures include “lockdown (CMM2)”, “use of personal protective equipment, such as nose masks, disinfects, etc. (CMM5)”, and “electronic/virtual meetings (CMM7)”. However, CMM5 possesses the highest contributory power to form CMM in impacting the QA, and this can be regarded as largely positive by strengthening health and safety management systems. Its negative impact lies with the project cost increment and the inconveniences of using nose and face masks.

This study provides a better understanding to construction practitioners and policy makers on how the pandemic policies, i.e. CMMs, have impacted QA and can aid in formulating planning and operational decisions to adequately position the QA for the post-pandemic era and to endure the risks of future pandemics.

The study contributes to knowledge in that it provides a better understanding of how the pandemic policies, such as CMMs, have impacted QA and can aid in formulating planning and operational decisions to adequately position the QA for the post-pandemic era and to endure the risks of future pandemics. This area of study has been given limited attention among prior studies during the pandemic.

Digital servitization research has focused on how manufacturing firms use digital technologies to change business models and offer smart services; less attention has been devoted to the degree to which external actors in the existing ecosystem accept these smart services. Therefore, the authors pose the following research question: How does a manufacturing firm introduce and gain acceptance of new smart services within an established ecosystem?

Building on servitization, ecosystem and legitimacy theories, this paper addresses the research question through an in-depth case study of a world-leading original equipment manufacturer that is currently developing and introducing new smart services in its existing ecosystem.

The findings suggest that external actors emphasize different types of legitimacy in deciding whether to accept a new smart service. The findings also show that the type of legitimacy required to gain acceptance changes throughout the development of the smart service, from the definition of the value proposition to the design and delivery of the service.

This study can assist smart service providers in identifying which type of legitimacy is important for each ecosystem actor and strengthening these types of legitimacy to gain acceptance from the ecosystem.

This study develops a framework to help describe the thresholds for acceptance of a smart service through the development phases, as well as to indicate the types of legitimacy that smart service providers must relate to when seeking to gain acceptance for their new offering.

Despite the opportunities of digital twins (DTs) for smart buildings, limited research has been conducted regarding the facility management stage, and this is explained by the high complexity of accurately representing and modelling the physics behind the DTs process. This study thus organises and consolidates the fragmented literature on DTs implementation for smart buildings at the facility management stage by exploring the enablers, applications and challenges and examining the interrelationships amongst them.

A systematic literature review approach is adopted to analyse and synthesise the existing literature relating to the subject topic.

The study revealed six main categories of enablers of DTs for smart building at the facility management stage, namely perception technologies, network technologies, storage technologies, application technologies, knowledge-building and design processes. Three substantial categories of DTs application for smart buildings were revealed at the facility management stage: efficient operation and service monitoring, efficient building energy management and effective smart building maintenance. Subsequently, the top four major challenges were identified as being “lack of a systematic and comprehensive reference model”, “real-time data integration”, “the complexity and uncertainty nature of real-time data” and “real-time data visualisation”. An integrative framework is finally proposed by examining the interactive relationship amongst the enablers, the applications and the challenges.

The findings could guide facility managers/engineers to fairly understand the enablers, applications and challenges when DTs are being implemented to improve smart building performance and achieve user satisfaction at the facility management stage.

This study contributes to the knowledge body on DTs by extending the scope of the existing studies to identify the enablers and applications of DTs for smart buildings at the facility management stage and the specific challenges.

This paper aims to explore the determinants of the level of minibonds issued by companies. In doing so, it discusses the importance of minibonds in providing a market-based funding source. In the empirical analysis, special attention is paid to the study of the recovery from the COVID-19 crisis.

The analysis is carried out through an econometric approach, on the basis of a high-quality empirical dataset related to the Italian small- and medium-sized enterprises (SMEs). The reference period covers the recent pandemic. From a theoretical point of view, a regression model is implemented, including a multicollinearity analysis and an outlier detection procedure.

The results of the study indicate that factors such as leverage, cash flow, firm collaterals and seniority can explain the amount of minibonds issued. These findings provide valuable insights into the drivers of minibond issuance and highlight the potential benefits of minibonds as a funding option for Italian SMEs.

Importantly, results highlight relevant managerial implications at two levels. On one side, we carry on a managerial discussion about the worthiness of accessing the minibonds market; on the other side, we give insights on the managerial implications related to the features of the companies issuing minibonds.

The paper investigates an innovative financial instrument that has been introduced recently and has not yet been studied in depth. To the best of our knowledge, this is the first contribution assessing the main drivers for minibonds issuance level, which is a timely and relevant managerial research topic. In addition, this study also takes into account the impact of the COVID-19 pandemic on minibond issuance, making the analysis appropriate for explaining the current economic context.

For a public servant (PS) to be innovative, he or she needs to gather and process enough vital information from budget setting processes. However, research addressing how budgetary participation (BP) can trigger innovative behaviour (IB) in PSs and eventually foster task performance (TP) is rare, which is why the authors conduct this research. The purpose of this study is to understand how BP shapes TP through the IB of PSs.

The authors develop and test a mediation model with 860 responses from public sector workers across 25 government agencies using the PLS-SEM technique of Smart PLS 4. Possible control factors were addressed for both the mediator and target-independent construct. In particular, the authors use sex, age and tenure as control factors for IB. Also, the authors use job satisfaction, job engagement and perceived fairness in the budgetary system as control factors for TP.

The authors find a favourable and significant relationship between BP and TP; BP and IB; and IB and TP. The authors also find that IB partially mediates the relationship between BP and TP, such that BP fosters TP through the innovativeness of PSs. This finding suggests that PSs who participate in budget preparation are able to innovate, which, in turn enable them to perform tasks effectively.

The authors call on forthcoming researchers to test the mediation model in other public sector settings worldwide. They may also consider other variables that can possibly mediate the positive impacts of BP on TP.

Lessons are discussed for governments, human resources directors and managers, management accountants, budget officers, procurement officers and other public sector workers and consultants.

The authors show how BP fosters TP through the innovativeness of PSs, since there is much more to know in this regard. The authors also help to resolve the paradox of inconsistency in the BP–TP literature by using IB as a mediator.