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To study the learning processes and mechanisms involved in sustainability-oriented supplier development (SSD), including how knowledge is transmitted by the buyer and how it is received, understood and internalised by the supplier.

An exploratory longitudinal multi-case study approach is adopted. The research context is a social SSD project focusing on occupational health and safety (OHS) management at four supplier factories. The paper draws on the constructs of absorptive capacity and boundary objects.

The development of a supplier's absorptive capacity for OHS management is triggered by the transfer of boundary objects that are created by the buyer. Findings suggest that each supplier starts explorative learning in a similar and passive way in order to accept the knowledge, but then each supplier proactively transforms and exploits the knowledge through continuous sensing, seizing, and reconfiguring loops that develop the boundary objects in a way that fits their own needs and contexts, incorporating the objects into organisational structures and routines.

The research furthers the understanding of the development of supplier absorptive capacity for sustainability via SSD projects, including how it is triggered and sustained. The impact of ostensive and performative aspects of boundary objects on knowledge transfer is presented. Finally, insight is provided into how absorptive capacity and dynamic capabilities are linked in the context of SSD.

Buying firms should seek to develop boundary objects that can trigger and maintain learning momentum for sustainability at supplier organisations in addition to effectively transferring SSD-related sustainability knowledge. There is also a need to allow for sufficient flexibility in the design of the boundary objects, and to pay sufficient attention to how suppliers contextualise and embed them into their own organisations, providing support for this process where necessary. For the supplier, establishing structures and routines for OHS management can help to prepare for future audits, thereby reducing audit fatigue.

The paper contributes to the supply chain learning literature by exploring the development of supplier absorptive capacity for sustainability triggered by the focal buying firm. It sheds new light on the role of boundary objects for facilitating knowledge transfer and learning between supply chain members in the context of SSD projects.

This paper aims to compare two groups of stocks to analyze the efficiency of an ethical portfolio in comparison with a conventional portfolio.

Efficiency test by second-order stochastic dominance (SSD) approach is applied on two groups, which consist of 12 stocks. Ethical portfolio is chosen from the stocks complying with the participation banking rules. Conventional portfolio is selected from Borsa Istanbul (BIST) with choosing the corresponding stocks for each ethical stock according to the sector and market capitalization. All the stocks of both groups are pairwise SSD compared.

Both groups of 12 stocks are inefficient portfolios; however, a group of 7 stocks constitute an efficient ethical portfolio with the total weight of 50.82 per cent among the set of 12 ethical stocks. On the other hand, a group of 6 stocks constitute an efficient conventional portfolio, with the total weight of 45.16 per cent among the set of 12 conventional stocks. By pairwise SSD comparison of corresponding stocks from both groups, despite none of the conventional stocks dominate ethical stocks, four ethical stocks dominated the conventional ones.

Back-testing and comparison with benchmark BIST 100 Index have been done for the selected portfolios. According to back-testing results, groups of SSD efficient stocks outperformed the groups, from which they were selected. Furthermore, both SSD efficient portfolios have higher returns than benchmark index, BIST 100.

The purpose of this study was to establish a Technology-Organization-Personality model of secure software development (SSD) innovation assimilation at the level of individual motivation. The model identifies individual psychological motivation, which influences innovation assimilation intention and behavior. It constitutes an organizational management view of SSD innovation assimilation from individual psychological motivation perspective.

An empirical study was employed to verify the assumption model. Semi-structured user interviews were conducted with some security experts to consult their advice and obtain the measurement scales. And questionnaires were circulated at a focus group meeting and among some software security professionals by email. Of 230 questionnaires that were answered, 215 could be used. IBM SPSS 19.0 and AMOS 17.0 were used alternately to analyze the data. Structural equation model was employed to verify the hypotheses of the model.

Results reveal that two types of individual motivation can influence SSD innovation assimilation, namely, potential organization support and individual needs. Furthermore, absorption capability was found to play a regulated function in the transition of SSD assimilation intention to behavior.

The findings reveal how individual motivation plays an important role in promoting complex innovation assimilation. It fills the gap of the research on organizational assimilation behavior and individual motivation in the context of SSD complex innovation, and provides management of software development organization with empirically based conceptualization to guide their personnel incentive policymaking.

This study aims to study how first-tier suppliers (FTs) operate as boundary-spanners between the focal firm and second-tier suppliers (STs) in extending sustainability-oriented supplier development (SSD) initiatives up the supply chain.

An exploratory multi-case study approach in the apparel industry is adopted, comprised of four cases focused on occupational health and safety issues. The paper uses primary semi-structured interviews and observation data and secondary documents, and it is informed by the boundary-spanning and social capital theory.

The influence of downstream social capital on the upstream boundary-spanning actions of FTs is highlighted. More specifically, it is found that the cognitive and relational capital that exists in the downstream relationship between an FT and the focal firm affects whether the FT adopts compliance- or improvement-oriented boundary-spanning actions in their upstream relationships with STs. Particularly important aspects of cognitive and relational capital are highlighted while the phenomenon of FTs adding their own personal interpretation to sustainability requirements when fulfilling their boundary-spanning role is identified.

A distinction is made between compliance- and improvement-oriented boundary-spanning actions. A deeper insight into the boundary-spanning role of FTs in extending SSD initiatives up the supply chain to STs is provided along with a deeper understanding of how this role is impacted by social capital.

Focal firms should seek to build adequate cognitive and relational capital with their FTs before deploying SSD initiatives to extend their reach further upstream in the supply chain. In doing so, it is also important to be cognisant of the social capital that exists between FTs and STs.

The paper contributes to the SSD literature by going beyond the buyer–FT dyad to examine the FT's boundary-spanning role in the wider buyer–FT–ST chain relationship. The study theoretically and empirically draws out the importance of relation-specific assets through the social capital lens.

The purpose of this paper is to contribute to the literature in three ways: first, the authors investigate the impact of the sampling errors on optimal portfolio weights and on financial investment decision. Second, the authors advance a comparative analysis between various domestic and international diversification strategies to define a stochastic optimal choice. Third, the authors propose a new methodology combining the re-sampling method, stochastic optimization algorithm, and nonparametric stochastic dominance (SD) approach to analyze a stochastic optimal portfolio choice for risk-averse American investors who care about benefits of domestic diversification relative to international diversification. The authors propose a new portfolio optimization model involving SD constraints on the portfolio return rate. The authors define a portfolio with return dominating the benchmark portfolio return in the second-order stochastic dominance (SSD) and having maximum expected return. The authors combine re-sampling procedure and stochastic optimization to establish more flexibility in the investment decision rule.

The authors apply the re-sampling procedure to consider the sampling error in the optimization process. The authors try to resolve the problem of the stochastic optimal investment strategy choice using the nonparametric SD test by Linton et al. (2005) based on sub-sampling simulated p values. The authors apply the stochastic portfolio optimization algorithm with SSD constraints to define optimal diversified portfolios beating benchmark indices.

First, the authors find that reducing sampling error increases the dominance relationships between different portfolios, which, in turn, alters portfolio investment decisions. Though international diversification is preferred in some cases, the study’s results show that for risk-averse US investors, in general, there is no difference between the diversification strategies; this implies that there is no increase in the expected utility of international diversification for the period before and after the 2007-2008 financial crisis. Nevertheless, the authors find that stochastic diversification in domestic, global, and Europe, Australasia, and Far East markets delivers better risk returns for the US risk averters during the crisis period.

The originality of the idea in this paper is to introduce a new methodology combining the concept of portfolio re-sampling, stochastic portfolio optimization with SSD constraints, and the nonparametric SD test by Linton et al. (2005) based on subsampling simulated p values to analyze the impact of sampling errors on optimal portfolio returns and to investigate the problem of stochastic optimal choice between international and domestic diversification strategies. The authors try to prove more coherence in the portfolio choice with the stochastically and the uncertainty characters of the paper.

To improve production efficiency, industrial robots are expected to replace humans to complete the traditional manual operation on grasping, sorting and assembling workpieces. These implementations are closely related to the accuracy of workpiece location. However, workpiece location methods based on conventional machine vision are sensitive to the factors such as light intensity and surface roughness. To enhance the robustness of the workpiece location method and improve the location accuracy, a workpiece location algorithm based on improved Single Shot MultiBox Detector (SSD) is proposed.

The proposed algorithm integrates a weighted bi-directional feature pyramid network into SSD. A feature fusion architecture is structured by the combination of low-resolution, strong semantic features and high-resolution, weak semantic features. The architecture is built through a top-down pathway, bottom-up pathway, lateral connections and skip connections. To avoid treating all features equally, learnable weights are introduced into each feature layer to characterize its importance. More detailed information from the low-level layers is injected into the high-level layers, which could improve the accuracy of workpiece location.

It is found that the maximum location error at the center point calculated from the proposed algorithm is decreased by more than 22% compared with that of the SSD algorithm. Besides, the average location error evolves a decrease by at least 5%. In the trajectory prediction experiment of the workpiece center point, the results of the proposed algorithm demonstrate that the average location error is below 0.13 mm and the maximum error is no more than 0.23 mm.

In this work, a workpiece location algorithm based on improved SSD is developed to extract the center point of the workpiece. The results demonstrate that the proposed algorithm is beneficial for workpiece location. The proposed algorithm can be readily used in a variety of workpieces or adapted to other similar tasks.

Solid solder deposit (SSD) technology was developed in the early to mid‐1990s to improve first pass yields in the manufacture of electronic devices. Examines the SSD process and how this technology differs from a transfer of the paste printing process from the assembly site to the board fabricator. Other questions considered include what design considerations should be addressed in implementing SSDs, what the benefits are for the end‐user, what types of product are best suited to SSD, and how a board fabricator should implement this technology in a manufacturing environment.

Workpiece sorting is a key link in industrial production lines. The vision-based workpiece sorting system is non-contact and widely applicable. The detection and recognition of workpieces are the key technologies of the workpiece sorting system. To introduce deep learning algorithms into workpiece detection and improve detection accuracy, this paper aims to propose a workpiece detection algorithm based on the single-shot multi-box detector (SSD).

Propose a multi-feature fused SSD network for fast workpiece detection. First, the multi-view CAD rendering images of the workpiece are used as deep learning data sets. Second, the visual geometry group network was trained for workpiece recognition to identify the category of the workpiece. Third, this study designs a multi-level feature fusion method to improve the detection accuracy of SSD (especially for small objects); specifically, a feature fusion module is added, which uses “element-wise sum” and “concatenation operation” to combine the information of shallow features and deep features.

Experimental results show that the actual workpiece detection accuracy of the method can reach 96% and the speed can reach 41 frames per second. Compared with the original SSD, the method improves the accuracy by 7% and improves the detection performance of small objects.

This paper innovatively introduces the SSD detection algorithm into workpiece detection in industrial scenarios and improves it. A feature fusion module has been added to combine the information of shallow features and deep features. The multi-feature fused SSD network proves the feasibility and practicality of introducing deep learning algorithms into workpiece sorting.

There has been much research focusing on contracting and its effect on individual voluntary sector organisation, and some mapping of the extent of voluntary sector participation in joint community care planning. Each of these is a new and formal relationship with the statutory sector, and in many cases the tasks are fulfilled by the same voluntary sector worker (usually the senior paid officer of the agency). But the impact that these two new relationships have on the voluntary organisation’s perception of its dependence and inter‐dependence has received less attention. The paper will draw on structured interviews in three local authorities, with voluntary sector participants in contracts for social care, and with participants in joint community care planning groups, as well as on documentary research. It will explore the impact of the evolving roles for those seeking to operate effectively in the pluralist provision of public services. It will analyse experiences within joint community care planning structures, and will analyse experiences of contractual relationships. The paper will seek to identify the elements present in each research site which influence the culture of joint working within the two statutory/ voluntary relationships.

The practice of artificial intelligence (AI) is increasingly being promoted by technology developers. However, its adoption rate is still reported as low in the construction industry due to a lack of expertise and the limited reliable applications for AI technology. Hence, this paper aims to present the detailed outcome of experimentations evaluating the applicability and the performance of AI object detection algorithms for construction modular object detection.

This paper provides a thorough evaluation of two deep learning algorithms for object detection, including the faster region-based convolutional neural network (faster RCNN) and single shot multi-box detector (SSD). Two types of metrics are also presented; first, the average recall and mean average precision by image pixels; second, the recall and precision by counting. To conduct the experiments using the selected algorithms, four infrastructure and building construction sites are chosen to collect the required data, including a total of 990 images of three different but common modular objects, including modular panels, safety barricades and site fences.

The results of the comprehensive evaluation of the algorithms show that the performance of faster RCNN and SSD depends on the context that detection occurs. Indeed, surrounding objects and the backgrounds of the objects affect the level of accuracy obtained from the AI analysis and may particularly effect precision and recall. The analysis of loss lines shows that the loss lines for selected objects depend on both their geometry and the image background. The results on selected objects show that faster RCNN offers higher accuracy than SSD for detection of selected objects.

The results show that modular object detection is crucial in construction for the achievement of the required information for project quality and safety objectives. The detection process can significantly improve monitoring object installation progress in an accurate and machine-based manner avoiding human errors. The results of this paper are limited to three construction sites, but future investigations can cover more tasks or objects from different construction sites in a fully automated manner.

This paper’s originality lies in offering new AI applications in modular construction, using a large first-hand data set collected from three construction sites. Furthermore, the paper presents the scientific evaluation results of implementing recent object detection algorithms across a set of extended metrics using the original training and validation data sets to improve the generalisability of the experimentation. This paper also provides the practitioners and scholars with a workflow on AI applications in the modular context and the first-hand referencing data.