Analyzing the modes of reasoning in design using the SAPPhIRE model of causality and the Extended Integrated Model of Designing

Literature suggests that people typically understand knowledge by induction and produce knowledge by synthesis. This paper revisits the various modes of reasoning – explanatory abduction, innovative abduction, deduction, and induction – that have been proposed by earlier researchers as crucial modes of reasoning underlying the design process. First, our paper expands earlier work on abductive reasoning – an essential mode of reasoning involved in the process of synthesis – by understanding its role with the help of the “SAPPhIRE” model of causality. The explanations of abductive reasoning in design using the SAPPhIRE model have been compared with those using existing models. Second, the paper captures and analyzes various modes of reasoning during design synthesis with the help of the “Extended Integrated Model of Designing”. The analysis of participants' verbal speech and outcomes shows the model's ability to explain the various modes of reasoning that occur in design. The results indicate the above models to provide a more extensive account of reasoning in design synthesis. Earlier empirical validation of both the models lends further support to the claim of their explanatory capacity.

Building the E-Commerce Supply Chain of the Future: What Influences Consumer Acceptance of Alternative Places of Delivery on the Last-Mile

Background: To cope with the expected further growth in e-commerce and to be able to continue delivering at low costs, new concepts for the last-mile are needed. This paper reviews the literature and qualitatively investigates which factors influence the acceptance of four alternative place-of-delivery innovations in a business-to-consumer (B2C) context: (1) parcel lockers, (2) reception boxes, (3) trunk delivery, and (4) home access systems; Methods: The available literature was reviewed. Furthermore, 37 interviews were conducted and analyzed using the deductive category development method. In the following, abductive reasoning can derive detailed research models that may form the basis for future confirmative studies; Results: The research gains more detailed insights into how consumers perceive innovative last-mile place-of-delivery concepts. The study provides a clearer picture of what factors influence the intention to use such alternative services; Conclusions: The results can be used by logistics service providers and e-tailers through targeted communication efforts and lay the groundwork for further confirmatory research.

Inference in Social Cognition

Inferences are ubiquitous in social cognition, governing everything from first impressions to the communication of meaning itself. Social cognitive inferences are typically varieties of diagnostic reasoning or, more properly, “abductive” reasoning, in which people infer simple but plausible—although not deductively certain—underlying causes for observable social behaviors. Abductive inference and its relationship to inductive and deductive inference are first introduced. A description of how abductive inference operates on a continuum between those that arise rapidly and automatically (and appear like deductions) and those that inspire more deliberative efforts (and thus often recruit more inductive information gathering and testing) is then given. Next, many classic findings in social cognition, and social psychology more broadly, that reveal how widespread this type of inference is explored. Indeed, both judgements under uncertainty and dual-process theories can be illuminated by incorporating the abductive frame. What then follows is a discussion on the work in ecological and evolutionary approaches that suggest that, although these inferences often go beyond the information given and are prone to predictable errors, people are good enough at social inference to qualify as being “ecologically rational.” The conclusion explores emerging themes in social cognition that only heighten the need for this broader understanding of inference processes.

Qualitative Research and Case Studies in Public Administration

Public administration has experienced academic growing pains and longstanding debates related to its identity as a social and administrative science. The field’s evolution toward a narrow definition of empiricism through quantitative measurement has limited knowledge cumulation. Because the goal of all scientific endeavors is to advance by building upon and aggregating knowledge across studies, a field-level point of view eschewing traditional dichotomies such as qualitative/quantitative debates in favor of methodological pluralism allows for examination of both the art and science of public administration. To accomplish this, traditional notions of quality, namely rigor, must be reconceptualized in a way that is appropriate for the philosophical commitments of a selected methodology. Rigor should focus on the accuracy, exhaustiveness, and systematicity of data collection and analysis. This allows for quality judgments about the degree to which the methods resulted in evidence that addresses the research questions and supports stated conclusions. This is a much broader approach to rigor that addresses multiple types of inquiry and knowledge creation. Once the question of rigor is not limiting the types of research done in the field, attention can be turned to the ways in which high-quality studies can contribute to knowledge cumulation. Case studies can be used as an example of a field-level point of view, as they have the ability to utilize abductive reasoning to consider both the whole (the entire case) and the particular (factors that contribute to outcomes, processes, or theories). Case studies explore the relationship between context-independent theories and context-dependent factors using different types of data collection and analysis: a triangulation of sorts. They can test theories in multiple ways and create or suggest new theories. Considering field-level questions as a case study and synthesizing findings from multiple related studies, regardless of methodology, can help move the field forward in terms of its connection between theory and practice, art and science.

Personal goals in consumers' adoption of mHealth services

PurposemHealth services are effective and cost efficient, yet wide-scale adoption of these services by consumers has yet to be achieved, constraining their public health benefit. Further investigation of non-technological determinants of mHealth adoption is needed; specifically, the role of consumers' goals has received scant attention and forms the research focus.Design/methodology/approachStudy 1 comprised 20 interviews with participants who possess a health goal, with the data analysed using an abductive reasoning approach. Study 2 was a 15-min online survey (n = 653), with the data analysed using multi-group structural equation modelling.FindingsStudy 1 identified several antecedents to the desirability and feasibility of consumers' health goals, which influence their desire to use mHealth services. Study 2 shows significant differences in the determinants of mHealth service acceptance depending on whether consumers set concrete as opposed to abstract goals, but social acceptance of mHealth services of these services is important for both groups.Practical implicationsThe findings suggest emphasising the importance of health goals to achieving other consumer goals (e.g. work or travel goals), the efficacy of mHealth services relative to other service alternatives for achieving those health goals, and the social acceptance of mHealth services to increase their uptake.Originality/valueThis study is the first to use construal-level theory to improve understanding of the role of consumers' goals in the adoption of mHealth services. By identifying the antecedents to goal desirability and feasibility, it also broadens the model of goal-directed behaviour.

Modeling and Reasoning in Event Calculus using Goal-Directed Constraint Answer Set Programming

Automated commonsense reasoning (CR) is essential for building human-like AI systems featuring, for example, explainable AI. Event calculus (EC) is a family of formalisms that model CR with a sound, logical basis. Previous attempts to mechanize reasoning using EC faced difficulties in the treatment of the continuous change in dense domains (e.g. time and other physical quantities), constraints among variables, default negation, and the uniform application of different inference methods, among others. We propose the use of s(CASP), a query-driven, top-down execution model for Predicate Answer Set Programming with Constraints, to model and reason using EC. We show how EC scenarios can be naturally and directly encoded in s(CASP) and how it enables deductive and abductive reasoning tasks in domains featuring constraints involving both dense time and dense fluents.

The Co-Constructed Logic Framework for Understanding Children’s Acts and Their Intentions

With the characteristics of the nonmonotonic logic and defeasible inference, abductive reasoning has been formalized in the field of artificial intelligence, dealing with the local pragmatics (e.g., the resolution of coreference, resolving syntactic and lexical ambiguity and interpreting metonymy and metaphor), recognizing discourse structure and even the speaker’s plan and other issues for natural language understanding. However, Hobbs’ analysis of abduction in recognizing the speaker’s plan was conducted only from the point of view of the verbal information processing that the listener does. To demonstrate the collaborative way that conversational partners working together to understand the logic of human acts and their intentions, this article analyzes the two conversations about the parents questioning their children’s intention for their acts with an abductive reasoning method. The results show that children and parents co-construct segments of discourse with coherence relations across several conversational turns, by that way they build together a simplified framework for understanding the logic of human acts and their intention. For example, when the father and his children co-constructed coherent segments of discourse with the result relation between them, they completed the particular intention understanding at the same time. This research helps in enriching the logic structure of artificial intelligence applications such as visual question answering models and enhancing their reasoning abilities.

Abduction and entrepreneurial learning

PurposeThe purpose of this article is to describe the similarities between abductive reasoning and entrepreneurial learning processes in order to contribute to the conceptual understanding of learning as an entrepreneurial process in itself.Design/methodology/approachThe research is theoretically rooted in a conceptual development of the understanding of entrepreneurial learning processes as abductive reasoning inspired by the philosopher Charles Sanders Peirce. The theoretical explication of the connection between entrepreneurial learning processes and abductive reasoning is additionally illustrated by a hypotheses-based didactic model, developed by the authors to scaffold abducting reasoning into learning processes.FindingsThe authors found in the theoretical investigation of abductive reasoning a conceptualisation of entrepreneurial learning processes that connects entrepreneurial learning processes to basic cognitive human competences, and the authors found that key concepts in entrepreneurship, such as hunches and experiments, can be understood in a broader philosophical framework as basic cognitive competences.Practical implicationsThe authors exemplify how abductive reasoning can be used in practice through a hypothesis-based didactic approach designed as a loop model.Originality/valueThe authors have discovered that abduction is closely related to entrepreneurship and can be a central conceptual link in understanding the relationship between entrepreneurship and learning. The athors also believe that Peirce's concept of abduction can contribute to the philosophical understanding of entrepreneurship as another name for a constant rethinking of the world.

Breaking the mould: achieving high-volume production output with additive manufacturing

PurposeThe study aims to examine a discrepant industrial case that demonstrates how to achieve economies of scale with additive manufacturing (AM), thereby expanding the scope of AM beyond high-variety, customised production contexts.Design/methodology/approachAbductive reasoning is applied to analyse a case of using AM to compete with conventional production, winning a contract to supply 7,700,000 products. Comparing this case to existing theories and contemporary practices reveals new research directions and practical insights.FindingsEconomies of scale were realised through a combination of technological innovation and the adoption of operations management practices atypical of AM shops (e.g. design for volume, low-cost resource deployment and material flow optimisation). The former improved AM process parameters in terms of time, cost and dependability; the latter improved the entire manufacturing system, including non-AM operations/resources. This system-wide improvement has been largely overlooked in the literature, where AM is typically viewed as a disruptive technology that simplifies manufacturing processes and shortens supply chains.Originality/valueIt is empirically shown that an AM shop can achieve economies of scale and compete with conventional manufacturing in high-volume, standardised production contexts.