Multi-Objective and Multi-Attribute Optimisation for Sustainable Development Decision Aiding

Optimization is considered as a decision-making process for getting the most out of available resources for the best attainable results. Many real-world problems are multi-objective or multi-attribute problems that naturally involve several competing objectives that need to be optimized simultaneously, while respecting some constraints or involving selection among feasible discrete alternatives. In this Reprint of the Special Issue, 19 research papers co-authored by 88 researchers from 14 different countries explore aspects of multi-objective or multi-attribute modeling and optimization in crisp or uncertain environments by suggesting multiple-attribute decision-making (MADM) and multi-objective decision-making (MODM) approaches. The papers elaborate upon the approaches of state-of-the-art case studies in selected areas of applications related to sustainable development decision aiding in engineering and management, including construction, transportation, infrastructure development, production, and organization management

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

The Adoption and Effectiveness of Automation in Health Evidence Synthesis

Background: Health systems worldwide are often informed by evidence-based guidelines which in turn rely heavily on systematic reviews. Systematic reviews are currently hindered by the increasing volume of new research and by its variable quality. Automation has potential to alleviate this problem but is not widely used in health evidence synthesis. This thesis sought to address the following: why is automation adopted (or not), and what effects does it have when it is put into use? / Methods: Roger’s Diffusion of Innovations theory, as a well-established and widely used framework, informed the study design and analysis. Adoption barriers and facilitators were explored through a thematic analysis of guideline developers’ opinions towards automation, and by mapping the adoption journey of a machine learning (ML) tool among Cochrane Information Specialists (CISs). A randomised trial of ML assistance in Risk of Bias (RoB) assessments and a cost-effectiveness analysis of a semi-automated workflow in the maintenance of a living evidence map each evaluated the effects of automation in practice. / Results: Adoption decisions are most strongly informed by the professional cultural expectations of health evidence synthesis. The stringent expectations of systematic reviewers and their users must be met before any other characteristic of an automation technology is considered by potential adopters. Ease-of-use increases in importance as a tool becomes more diffused across a population. Results of the randomised trial showed that ML-assisted RoB assessments were non-inferior to assessments completed entirely by human researcher effort. The cost-effectiveness analysis showed that a semi-automated workflow identified more relevant studies than the manual workflow and was less costly. / Conclusions: Automation can have substantial benefits when integrated into health evidence workflows. Wider adoption of automation tools will be facilitated by ensuring they are aligned with professional values of the field and limited in technical complexity

Simulating the implementation of technological innovations in construction

Introducing new technologies or innovative processes can enhance construction efficiency and enable organisations to achieve objectives of lowering costs, continuous improvement and competitive advantage. New ideas have to show significant benefits before they are accepted. Despite of the differences between the construction and manufacturing industries, opportunities are still available to leam from manufacturing approaches to innovation. A fundamental challenge facing construction innovation is the way that construction organisations plan and control the implementation of innovation where many projects do not fulfil their time and cost objectives. Management should not only improve techniques for planning and scheduling but also allow managers to assess and simulate the anticipated performance resulting from innovation .. According to this assessment, managers would be more able and perhaps more ready to accept new processes/products or iterate the implementation process until a satisfactory level of performance has been achieved. Intangible benefits offered by advanced construction technologies are hard to quantify using traditional economic analysis techniques. This could result in the rejection of a potentially profitable idea. Benefits to be gained from improvements in operational efficiency are measured by cost and time-savings and increasing productivity. These benefits, in addition to intangible benefits, need to be measured and quantified. Simulating the implementation process of innovation has not been addressed, although many models have been developed to describe the innovation process in construction which considered implementation as a sequential process incorporating iterations. [Continues.

The R&D Boundaries of the Firm and the Governance of R&D Alliances: Essays on Institutions, Strategic Considerations and Contract Structure

The three essays in this dissertation examine questions related to the R&D boundaries of the firm and the governance of R&D alliances. The first essay draws on institutional theory to examine the history of corporate R&D in the U.S. since the mid-19th century. Formal and informal institutional rules and constraints are shown to play a role in the initial rise of markets for technology in the 19th century, their decline during the early-20th century, and their eventual return at the end of the 20th century. The influence of formal and informal institutions on the adoption of in-house R&D labs in the US during the mid-20th century is also examined. In the second essay, the focus shifts to an investigation of the discrete project-level R&D outsourcing decision. A framework for understanding the direct and indirect influence of strategic considerations and environmental factors is developed. The impact of project- and transaction-level characteristics on the R&D outsourcing decisions are also considered, highlighting the importance of integrating information possessed by managers from different levels of the firms. Finally, the second essay proposes that cross-level interactions may exist within the framework, which may help to explain why the decisions observed in some cases run counter to the predictions traditionally derived from theory. The third essay includes two empirical studies that examine different aspects of the contracts designed to govern R&D alliances. Using a unique set of contracts from the medical device industry, the studies in the final essay investigate the factors that influence the structure of R&D alliance contracts and the assignment of key decision and control rights in such contracts. In addition, the final essay investigates the impact of previous alliance experience on the relationship between the key factors identified and the structure of R&D alliance contracts

A cognitive perspective on learning, decision-making, and technology evaluations in organisations

This dissertation examines how firms’ selection of technological and R&D opportunities shape the performance of their innovation efforts. Managers select R&D investments in complex and uncertain environments where it is difficult to learn from past decisions. I examine this challenge using empirical and agent-based modelling methods and by focusing on three interrelated aspects: managers’ individual learning processes, the adaptation of mental representations in complex environments, and the role of distributed expertise in group evaluations. In the first chapter, I propose an alternative explanation to how managers learn from experience that does not involve feedback and that is thus applicable to contexts where learning from feedback is difficult. I test this novel learning mechanism, termed ‘representation learning’, by analysing a large proprietary dataset of patent evaluations and termination decisions made by managers at a Fortune 500 firm. The second chapter explores further implications for performance of representation learning by means of an agent-based model of representation and policy search in rugged landscapes. This study examines how different representation search strategies affect decision-makers’ adaptation in complex environments. Finally, the third chapter explores the performance of group evaluation processes when evaluators differ in the depth and breadth of their knowledge of the technologies being evaluated. This research contributes to management literature by shedding light on the cognitive processes underlying learning and decision-making in uncertain and complex environments. These findings also have practical implications for strategy research and practice concerning the management of uncertain R&D and technology investments.Open Acces

Exploratory and Exploitative Knowledge Sharing in Interorganizational Relationships

A growing body of research investigates the role that organizational learning plays in generating superior firm performance. Researchers, however, have given limited attention to this learning effect in the context of long-term interorganizational relationships. This paper focuses on a specific aspect of learning, that is, explorative and exploitative knowledge sharing, and examines its impacts on sustained performance. We examine interorganizational design mechanisms and digitally-enabled knowledge representation as antecedents of knowledge sharing. The empirical context is dyadic relationship between a supply chain solutions vendor and its customers for two major classes of supply chain services. Our theoretical predictions are tested by using data collected from both sides of this customer-vendor dyad. The findings suggest that dual emphasis on exploration and exploitation is important for sustained relationship performance for customers. The customer evaluates balancing exploration and exploitation important whereas the vendor emphasizes only on exploitation

Cumulative Innovation & Open Disclosure of Intermediate Results: Evidence from a Policy Experiment in Bioinformatics

Recent calls for greater openness in our private and public innovation systems have particularly urged for more open disclosure and granting of access to intermediate works–early results, algorithms, materials, data and techniques–with the goals of enhancing overall research and development productivity and enhancing cumulative innovation. To make progress towards understanding implications of such policy changes we devised a large-scale field experiment in which 733 subjects were divided into matched independent subgroups to address a bioinformatics problem under either a regime of open disclosure of intermediate results or, alternatively, one of closed secrecy around intermediate solutions. We observe the cumulative innovation process in each regime with fine-grained measures and are able to derive inferences with a series of cross-sectional comparisons. Open disclosures led to lower participation and lower effort but nonetheless led to higher average problem-solving performance by concentrating these lesser efforts on the most performant technical approaches. Closed secrecy produced higher participation and higher effort, while producing less correlated choices of technical approaches that participants pursued, resulting in greater individual and collective experimentation and greater dispersion of performance. We discuss the implications of such changes to the ongoing theory, evidence and policy considerations with regards to cumulative innovation

Exploratory and Exploitative Knowledge Sharing in Interorganizational Relationships

A growing body of research investigates the role that organizational learning plays in generating superior firm performance. Researchers, however, have given limited attention to this learning effect in the context of long-term interorganizational relationships. This paper focuses on a specific aspect of learning, that is, explorative and exploitative knowledge sharing, and examines its impacts on sustained performance. We examine interorganizational design mechanisms and digitally-enabled knowledge representation as antecedents of knowledge sharing. The empirical context is dyadic relationship between a supply chain solutions vendor and its customers for two major classes of supply chain services. Our theoretical predictions are tested by using data collected from both sides of this customer-vendor dyad. The findings suggest that dual emphasis on exploration and exploitation is important for sustained relationship performance for customers. The customer evaluates balancing exploration and exploitation important whereas the vendor emphasizes only on exploitation