Search results

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.

This article aims to trace the historical development of the behavioral strategy (BS) field, which implements psychology in strategic management. Mainly, it provides a contextual understanding of how this stream of research has historically evolved and what relevant future trajectories are. This work is part of the “over half a century of Management Decision” celebrative and informal Journal section.

We consider BS literature produced in management decision (MD), the oldest and longest-running scholarly publication in management, as a proxy for the evolution of management thought. Through a Systematic Literature Review (SLR) process, we collected – via the MD website and Scopus – a sample of 97 BS articles published in MD from its foundation (1967) until today (2024). Regarding the analysis, we adopted a Reflexive Thematic Analysis approach to synthesize the main BS topics, then read from a historical perspective regarding three “eras” over which the literature developed. Selected international literature outside the Journal’s boundaries was considered to complement this historical analysis.

Historically, within the BS field, the interest passed from the rules to rationally govern strategic decision-making processes, to studying what causes cognitive errors, to understanding how to avoid biases and to being prepared for dramatic changes. The article also identifies six future research trajectories, namely “positive heuristics,” “context-embedded mental processes,” “non-conventional thinking,” “cognitive evolutionary triggers,” “debiasing strategies” and “behavioral theories for new strategic challenges” that future research could investigate.

The limitation of the study lies in its exclusive focus on MD for investigating the historical evolution of BS, thereby overlooking critical contributions from other journals. Therefore, MD’s editorial preferences have influenced results. A comprehensive SLR on the BS field is still needed, requiring broader journal coverage to mitigate selection biases and enhance field appraisal.

This contribution is the first to offer a historical evolutionary view of the BS field, complementing the few other reviews on this stream of research. This fills a gap in the study of the evolution of management thought.

The study aims to fulfil two objectives, namely, to explore what dimensions comes into play in the current consumer EV purchase behaviour based on relevant literature identified in the past and to find out whether there are other reasons that are peculiar to the Indian automobile market which influenced consumers to adopt this eco-friendly technology.

Mixed method approach for collecting the primary data was used. The study was conducted in five cities across India, through a questionnaire administered to 266 existing EV owners using judgemental sampling, that included dimensions as to what made them purchase EVs (any variant whether hybrid or battery) and encouraged their green behaviour. Moreover, the survey also took care of gathering other motivational constructs that may have played a significant role in the Indian EV market. To take care of this, use of single open-ended question was done. Statistical techniques like exploratory factor analysis (EFA) and Partial Least Square-Structural Equation Modelling (PLS-SEM) version 4.0 were performed to gain meaningful insights from this investigation.

Based on our analysis, EV purchase behaviour in the Indian market could be explained by six configurational paths identified in conceptual framework of this study. The data analysed revealed long-term operational benefits or relative advantages, trialability or prior driving experience and positive attitude towards EVs playing an important role towards EV purchase decision followed by environmental concern, social norms and personal values of Indian consumers. Besides, several other factors were identified in open comments like favourable government policies, brand consciousness, compatibility and functional benefits in terms of comfortable and noiseless driving which tend to drive the decision of Indian consumers towards buying EVs. The identification of these motivations can help policymakers in targeting the customer market accordingly.

Although the study is restricted by its scope of sampling criteria, yet is distinct in its methodological approach and thereby adds value to existing literature on marketing and diffusion of EVs. The study explores the untapped gap in the current literature by helping identify reasons on the basis of which consumers made their choices of different EVs. Instead of analysing the potential antecedents of EV uptake, the study brings to light the actual reasons of EV purchase behaviour in the context of an emerging economy like India. That is to say, the horizon of EV motivations spans across technological, individual and regulatory attributes in the Indian automobile market.

This study is relevant and fulfils the underlying gap in the existing literature related to actual motivations that consumers look for before buying a high involvement product such as EVs. The results can be beneficial for marketers to design effective marketing strategies from the perspective of consumers. Promotional strategies addressing ease of using EVs and how battery technology is better than before needs to be clearly communicated by the marketers. Consumer education is necessary, in this regard, to make people aware of the rising air pollution levels. They have to be made understood the real advantages of owning EVs and how these non-conventional vehicles are far better when compared to traditional cars. In a nutshell, the study underscored potential determinants or reasons that made Indian consumers to go for EVs, based on statistically significant evidences which can be crucial for stakeholders operating in the Indian EV industry.

The study has important implications for members of the community. By sacrificing their egoistic tendencies or individual preferences for energy intensive products such as ICE based vehicles, people can imbibe altruistic orientation and can contribute to the collective benefit of all by shifting towards energy rich vehicles powered by electricity. This is necessary to achieve the broader goals of sustainable development and to curtail the rising climate change phenomenon.

The study applied both quantitative (deductive) and qualitative (inductive) approach to study into the reasons for consumers’ purchase motivations towards electric cars. Taking a distinct methodological approach, the study enhances the knowledge on EVs by asking consumers to comment in their own words as to why they purchased EVs. This was necessary so as to better explore the needs of the Indian automobile market and which factors govern the purchase of EVs in domestic market. Past literature majorly focused on purchase intention towards EVs but this study in unexampled and analysed the purchase behaviour towards EVs. It should be noted that our investigation reveals the complexity of EV purchase reasons than those identified in past literature and it adds to the current pool of literature on EVs by explaining factors with unrivalled illustrative power.

Authorities have set up numerous security checkpoints during times of armed conflict to control the flow of commercial and humanitarian trucks into and out of areas of conflict. These security checkpoints have become highly utilized because of the complex security procedures and increased truck traffic, which significantly slow the delivery of relief aid. This paper aims to improve the process at security checkpoints by redesigning the current process to reduce processing time and relieve congestion at checkpoint entrance gates.

A decision-support tool (clearing function distribution model [CFDM]) is used to minimize the effects of security checkpoint congestion on the entire humanitarian supply network using a hybrid simulation-optimization approach. By using a business process simulation, the current and reengineered processes are both simulated, and the simulation output was used to estimate the clearing function (capacity as a function of the workload). For both the AS-IS and TO-BE models, key performance indicators such as distribution costs, backordering and process cycle time were used to compare the results of the CFDM tool. For this, the Kerem Abu Salem security checkpoint south of Gaza was used as a case study.

The comparison results demonstrate that the CFDM tool performs better when the output of the TO-BE clearing function is used.

The efforts will contribute to improving the planning of any humanitarian network experiencing congestion at security checkpoints by minimizing the impact of congestion on the delivery lead time of relief aid to the final destination.

As laser scanning technology becomes readily available and affordable, there is an increasing demand of using point cloud data collected from a laser scanner to create as-built building information modeling (BIM) models for quality assessment, schedule control and energy performance within construction projects. To enhance the as-built modeling efficiency, this study explores an integrated system, called Auto-Scan-To-BIM (ASTB), with an aim to automatically generate a complete Industry Foundation Classes (IFC) model consisted of the 3D building elements for the given building based on its point cloud without requiring additional modeling tools.

ASTB has been developed with three function modules. Taking the scanned point data as input, Module 1 is built on the basis of the widely used region segmentation methodology and expanded with enhanced plane boundary line detection methods and corner recalibration algorithms. Then, Module 2 is developed with a domain knowledge-based heuristic method to analyze the features of the recognized planes, to associate them with corresponding building elements and to create BIM models. Based on the spatial relationships between these building elements, Module 3 generates a complete IFC model for the entire project compatible with any BIM software.

A case study validated the ASTB with an application with five common types of building elements (e.g. wall, floor, ceiling, window and door).

First, an integrated system, ASTB, is developed to generate a BIM model from scanned point cloud data without using additional modeling tools. Second, an enhanced plane boundary line detection method and a corner recalibration algorithm are developed in ASTB with high accuracy in obtaining the true surface planes. At last, the research contributes to develop a module, which can automatically convert the identified building elements into an IFC format based on the geometry and spatial relationships of each plan.

Responding to calls for deeper analysis of the conceptual foundations of service infusion in manufacturing, this paper examines the underlying assumptions that: (i) manufacturing firms incorporating services follow a pathway, moving from pure-product to pure-service offerings, and (ii) profits increase linearly with this process. We propose that these assumptions are inconsistent with the premises of behavioural and learning theories.

Machine learning algorithms are applied to test whether a successive process, from a basic to a more advanced offering, creates optimal performance. The data were gathered through two surveys administered to USA manufacturing firms in 2021 and 2023. The first included a training sample comprising 225 firms, whilst the second encompassed a testing sample of 105 firms.

Analysis shows that following the base-intermediate-advanced services pathway is not the best predictor of optimal performance. Developing advanced services and then later adding less complex offerings supports better performance.

Manufacturing firms follow heterogeneous pathways in their service development journey. Non-servitised firms need to carefully consider their contextual conditions when selecting their initial service offering. Starting with a single service offering appears to be a superior strategy over providing multiple services.

The machine learning approach is novel to the field and captures the key conditions for manufacturers to successfully servitise. Insight is derived from the adoption and implementation year datasets for 17 types of services described in previous qualitative studies. The methods proposed can be extended to assess other process-based models in related management fields (e.g., sand cone).

The purpose of this paper is to explore two financial inclusion measures adopted within the local welfare context of the city of Milan, Italy, examining their functioning and underpinning representations. The aim is also to understand how such representations take concrete shape in the practices of local actors, and their implications for the opportunities and constraints regarding individuals' effective inclusion. To this end, this paper takes a wide-ranging look at the interplay between the rise of financial inclusion and the individualisation and responsibilisation models informing welfare policies, within the broader context of financialisation processes overall.

This paper draws on the sociology of public action approach and provides a qualitative analysis of two case studies, a social microcredit service and a financial education programme, based on direct observation and semi-structured interviews conducted with key policy actors.

This paper sheds light on the rationale behind two financial inclusion services and illustrates how the instruments involved incorporate and tend to reproduce, individualising logics that reduce the problem of financial exclusion, and the social and economic vulnerability which underlies it, to a matter of personal responsibility, thus fuelling depoliticising tendencies in public action. It also discusses the contradictions underlying financial inclusion instruments, showing how local actors negotiate views and strategies on the problems to be addressed.

The paper makes an original contribution to the field of sociology and social policy by focusing on two under-researched instruments of financial inclusion and improving understanding of the finance-welfare state nexus and of the contradictions underpinning attempts at financial inclusion of the most vulnerable.

Large supermarket chains are adopting customer-service robots to improve service delivery in physical stores. Successful deployment of these robots depends on shoppers' willingness to interact with them, requiring an understanding of influencing factors. This study, grounded in the Cognitive-Affective-Normative (CAN) theory, seeks to systematically explore the factors influencing Gen Z shoppers' willingness to interact with customer-service robots.

A hybrid approach combining Structural Equation Modeling (SEM) and Necessary Condition Analysis (NCA) was employed to analyze survey data collected from 945 Gen Zs in the Czech Republic.

The results from SEM highlight significant cognitive, normative, and affective factors that influence the intention of Gen Z shoppers to interact with a customer-service robot. Specifically, cognitive factors such as effort and performance expectancy, along with normative factors like subjective norms, emerged as critical determinants. Furthermore, affective factors such as technology anxiety and positive emotions significantly influence users' readiness to use customer-service robots for service requests. The study also underscores that positive emotions, effort expectancy, performance expectancy, and subjective norms are vital prerequisites for interacting with customer-service robots.

The originality of this work lies in its two significant contributions to the burgeoning field of SRs in retail literature. First, it extends the CAN theory to the context of SRs among Gen Z shoppers in Czechia, thereby enriching the existing literature on SRs in retail. Second, by employing a hybrid analytical approach, our research offers both empirical and methodological advancements, providing rigorous insights crucial for enhancing the understanding of the pivotal factors influencing shoppers' interactions with SRs in physical store environments.

This study investigates the application of collaborative inquiry within innovation management, employing platform thinking to address challenges of generalizability and relevance. The aim is to integrate Collaborative Inquiry methods, characterized by participatory, diffuse, and reflective practices, to transform research into a tool for impactful change in organizations in the field of innovation management.

A longitudinal participatory case study approach focuses on the IDeaLs case—a research platform that collaborated with multiple companies over several years. The data gathered and analyzed comes from the research project within the research platforms over the first two editions and from the research platform management and coordination activities.

The study introduces the Collaborative Research Platform Approach (CRPA), demonstrating its effectiveness in addressing typical constraints of traditional research methodologies through a real-world application within the IDeaLs case. The findings highlight the CRPA's potential in fostering a dynamic, co-creative research environment that bridges theoretical knowledge with practical applications, thus enhancing both scholarly and organizational outcomes while pursuing a future change within the organizations.

There are two main research implications. First, it proposes platform thinking as a theoretical lens to read a multi-stakeholder phenomenon in the research domain, confirming its nature of value-creation mechanisms, using it outside the business model and strategic space. Second, it offers a methodological contribution by presenting the CRPA framework.

The CRPA framework offers organizations a structured approach to managing collaborative research projects that align with both academic rigor and practical relevance. Companies engaged in the study reported enhanced ability to implement actionable insights from research, influencing real-time decision-making processes.

By fostering collaborative engagements across multiple stakeholders, the CRPA promotes a research culture that values inclusivity and practical impact, potentially leading to broader societal benefits through improved innovation management practices.

This paper contributes to the innovation management field by proposing the CRPA, which integrates principles of Platform Thinking with Collaborative Inquiry. This novel approach is designed to improve the applicability and scope of innovation research, offering a robust framework that enhances engagement and utility across academic and business domains. It uses platforms as a theoretical lens to read a multi-stakeholder environment in the research domain.

The study aims to identify and understand the underlying behavioral tendencies and motivations influencing investor sentiments and examines the relationship between these underlying factors and investment decisions during the COVID-19-induced financial risks.

The study uses the primary data and information collected from 300 Indian retail equity investors using a nonprobability sampling technique, specifically purposive and snowball sampling. This research uses the insights from Phuoc Luong and Thi Thu Ha (2011) and Shefrin (2002) to delineate behavioral factors influencing investment decisions. Structural equation modeling estimates the causal relationship between underlying behavioral factors and investment decisions during the COVID-19-induced financial risks.

The study establishes that the “Regret Aversion,” “Gambler’s Fallacy” and “Greed” significantly influence investment decisions, and provide a comprehensive understanding of how psychological motivations shape investor behavior. Notably, “Mental Accounting” and “Conservatism” exhibit insignificance, possibly influenced by the unique socioeconomic context of the pandemic. The research contributes to 35% of variance understanding and prompts the researchers and policymakers to tailor investment strategies aligned to these behavioral tendencies.

The findings hold policy implications for investors and policymakers and provide tailored recommendations including investor education programs and regulatory measures to ensure a resilient and informed investment community in the context of India's evolving financial landscapes.

Theoretically, behavior tendencies and motivations have been strongly linked to investment decisions in the stock market. Yet, empirical evidence on this relationship is limited in developing countries where investors focus on risk management. To the best of the authors’ knowledge, this study is among the first to document the influence of underlying behavioral tendencies and motivation factors on investment decisions regarding retail equity in a developing country.