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Numerous design methods are available to facilitate digital innovation processes in user interface design. Nonetheless, little guidance exists on their appropriate selection within the design process based on specific situations. Consequently, design novices with limited design knowledge face challenges when determining suitable methods. Thus, this paper aims to support design novices by guiding the situational selection of design methods.

Our research approach includes two phases: i) we adopted a taxonomy development method to identify dimensions of design methods by reviewing 292 potential design methods and interviewing 15 experts; ii) we conducted focus groups with 25 design novices and applied fuzzy-set qualitative comparative analysis to describe the relations between the taxonomy's dimensions.

We developed a novel taxonomy that presents a comprehensive overview of design conditions and their associated design methods in innovation processes. Thus, the taxonomy enables design novices to navigate the complexities of design methods needed to design digital innovation. We also identify configurations of these conditions that support the situational selections of design methods in digital innovation processes of user interface design.

The study’s contribution to the literature lies in the identification of both similarities and differences among design methods, as well as the investigation of sufficient condition configurations within the digital innovation processes of user interface design. The taxonomy helps design novices to navigate the design space by providing an overview of design conditions and the associations between methods and these conditions. By using the developed taxonomy, design novices can narrow down their options when selecting design methods for their specific situations.

The purpose of this study is to demonstrate that Strategic Enterprise Management (SEM) and Business Intelligence (BI) have the potential to integrate management decisions vertically through an organization’s hierarchy. This study also aims to present a design theory framework and build a model dimension using eight principles serving as mid-range theories.

This study uses a design science perspective to posit how organizations can successfully implement SEMBI (a union of SEM and BI). This study then completes the design theory by building the method dimension using two principles. Finally, the study presents testable hypotheses for the theory and an evaluation using stakeholder attitudes and judgments as proxies for objective measures.

In the search for a prescription for SEMBI success, this study finds that the notion of the Capability Maturity Model (CMM) is a good artifact with which to organize the principles the authors are seeking. CMM has since been adapted to suit different contexts by incorporating relevant principles from those domains. Hereafter, this study refers to SEMBI–CMM as the adapted solution for SEMBI's success.

This study coins and uses the term SEMBI to represent the union of SEM and BI. This term retains its distinct identities and principles and forms a holistic and integrated view of SEM and BI implementation strategies. In an effort to advance this line of research, this study employs a design science perspective to address the question of how an organization can successfully implement SEMBI.

This study aims to develop a modular and prescriptive digital transformation maturity model whose constituent elements have conceptual integrity as well as reveal the priority weights of maturity model components.

A literature review with a concept-centric analysis enlightens the characteristics of constituent parts and reveals the gaps for each component. Therefore, the interdependency network among model dimensions and priority weights are identified using decision-making trial and evaluation laboratory (DEMATEL)-based analytic network process (ANP) method, including 19 industrial experts, and the results are robustly validated with three different analyses. Finally, the applicability of the developed maturity model and the constituent elements are validated in the context of the manufacturing industry with two case applications through a strict protocol.

Results obtained from DEMATEL-based ANP suggest that smart processes with a priority weight of 17.91% are the most important subdimension for reaching higher digital maturity. Customer integration and value, with a priority weight of 17.30%, is the second most important subdimension and talented employee, with 16.24%, is the third most important subdimension.

The developed maturity model enables companies to make factual assessments with specially designed measurement instrument including incrementally evolved questions, prioritize action fields and investment strategies according to maturity index calculations and adapt to the dynamic change in the environment with spiral maturity level identification.

A novel spiral maturity level identification is proposed with conceptual consistency for evolutionary progress to adapt to dynamic change. A measurement instrument that is incrementally structured with 234 statements and a measurement method that is based on the priority weights and leads to calculating the maturity index are designed to assess digital maturity, create an improvement roadmap to reach higher maturity levels and prioritize actions and investments without any external support and assistance.

This study aims to develop a novel design method to make personalized masks for the effective prevention of pandemic respiratory infectious disease.

The changes in facial shape during speaking were analyzed using a three-dimensional (3D) scanning technique. In total, 13 anthropometric items were measured, and mask patterns were generated using a parametric pattern design method. Three sizing methods were proposed to reflect not only static but also dynamic body dimensions on the mask patterns.

A significant increase or decrease was observed in 10 out of 13 measurement items. Based on this, four items were selected to be used in the mask pattern design. The nose and cheek areas of a mask were fixed to protect the respiratory tract against viruses. The lower jaw area was deformed to improve the fit.

This study is expected to provide fundamental data to understand the changes in facial shape during movement. In addition, it is expected that the development of individualized personal protective equipment with movement adaptability will facilitate an effective response to various pandemic respiratory diseases.

In order to develop a personal protective equipment (PPE) that has a good fit and can protect against pandemic respiratory infectious diseases, morphological analysis was attempted using 3D facial data. It would be possible to design various products and equipment to be worn on the face by using the method proposed in this study.

This study aims to develop an optimal tolerance allocation strategy involves integrating the unique transfer (UT) approach and the difficulty coefficient evaluation (DCE) routine in an interactive hybrid method. This method combines the strengths of both UT and DCE, ensuring simultaneous utilization for enhanced performance. The proposed tolerancing model manifests an integrated computer-aided design (CAD) tool.

By combining UT and DCE based on failure mode, effects and criticality analysis (FMECA) tool and the Ishikawa diagram, the proposed collaborative hybrid tool ensures an efficient and optimal tolerance allocation approach. The integration of these methodologies not only addresses specific transfer challenges through UT but also conducts a thorough evaluation of difficulty coefficients via DCE routine using reliability analysis tools as FMECA tool and the Ishikawa diagram. This comprehensive framework contributes to a robust and informed decision-making process in tolerance allocation, ultimately optimizing the design and manufacturing processes.

The presented methodology is implemented with the aim of generating allocated tolerances that align with specific difficulty requirements, facilitating the creation of a mechanical assembly characterized by high quality and low cost. To substantiate and validate the conceptual framework and methods, an integrated tool has been developed, featuring a graphical user interface (GUI) designed in MATLAB. This interface serves as a platform to showcase various interactive and integrated tolerance allocation approaches that adhere to both functional and manufacturing prerequisites. The proposed integrated tool, designed with a GUI in MATLAB, offers the capability to execute various examples that effectively demonstrate the benefits of the developed tolerance transfer and allocation methodology.

The originality of the proposed approach is the twining between the UT and DCE simultaneous in an integrated and concurrent tolerance transfer and allocation model. Therefore, the proposed approach is named an integrated CAD/tolerance model based on the manufacturing difficulty tool. The obtained results underscore the tangible advantages stemming from the integration of this innovative tolerance transfer and allocation approach. These benefits include a notable reduction in total cost and a concurrent enhancement in product quality.

The goal of this study is to predict the farmers’ concerns about agricultural index-insurance (AII) for weather risk mitigation of horticultural crops in hilly regions. The key impetus of analysis is to prioritize the AII requirements based on the farmers’ perspectives using the requirements prioritization approach.

The integrated approach has been applied in this paper. Initially, the MoSCoW prioritization technique has been employed to prioritize the AII attributes utilizing a four-dimensional agriculture insurance scale. Later, the rank sum weighting method was deployed to assign the ultimate rank to AII attributes based on the farmers’ responses.

Findings specified that out of 15 AII attributes, majority of 11 attributes were placed in “must have” and “should have” categories that related to claim, design, premium and grievance management dimensions. However, three AII attributes are placed in the “could have” category. Additionally, findings of rank-sum weighting method-based ranking can help insurers in redesigning farmers-oriented AII services for risk mitigation of horticulture crops by incorporating these ranks as per their priority level.

The prioritized AII attributes are helpful for insurers and managers in order to solve the problems associated with design, premium, claim and grievance management of AII.

Findings deliver significant insights to insurers to incorporate the prioritized AII attributes ranked by farmers.

This is the initial known analysis that integrated the MoSCoW and rank sum weighting method to prioritize the AII requirements prioritization among Indian farmers.

In response to multiple disruptions, the purchasing supply management (PSM) function in construction supply chain management (CSCM) has gained prominence due to stakeholder pressures, dynamic market conditions and the need to adhere to complex sustainability, safety and health regulations and standards. However, there is a noticeable absence of empirical research on measuring and mitigating PSM vulnerabilities, especially considering the distinct challenges faced by large engineer-to-order project-oriented manufacturers. To address these issues, the purpose of this study is to develop and test a novel method to assist companies in construction supply chains in assessing and managing risks associated with sustainable procuring and sourcing materials.

Grounded in the literature gaps on construction PSM and a real case supply chain, the research uses the design science research (DSR) approach to develop an integrated method for assessing PSM strategies in this sector. The method integrates three essential purchasing dimensions: supply risks, profit impact and sustainability risks of materials, supported by nine subdimensions. Empirical validation took place within a multinational European construction company based in Denmark.

Findings from the supplier–buyer relationships confirmed that the developed method allows for the identification of the key components that significantly impact supplier–buyer relationships, profitability and sustainability. The research further suggests that construction supply chain managers and purchasing practitioners can use the proposed method to evaluate PSM, thus enabling them to make more informed decisions.

Through the utilization of the proposed artifact, construction companies can take a more proactive approach to address PSM uncertainties, thereby enhancing their competitiveness in dynamic construction supply chains.

The research contributes to bridging the theory and practice, providing valuable assistance for construction companies assessing and managing the PSM and supply risks within global construction value chains. This paper provides original insights for the CSCM, aiding in adopting competitive PSM strategies to improve overall supply chain performance.

The purpose of this paper is to analyse the current state of research on the integration of blockchain and building information modelling (BIM) in the Architecture, Engineering, Construction and Operations (AECO) industry as a means of identifying gaps between the existing paradigm and practical applications for determining future research directions and improving the industry. The study aims to provide clear guidance on areas that need attention for further research and funding and to draw academic attention to factors beyond the technical dimension.

A mixed-method systematic review is used, considering multiple literature types and using a sociotechnical perspective-based framework that covers three dimensions (technic, process and context) and three research elements (why, what and how). Data are retrieved and analysed from the Web of Science and Scopus databases for the 2017–2023 period.

While blockchain has the potential to address security, traceability and transparency and complement the system by integrating supporting applications, significant gaps still exist between these potentials and widespread industry adoption. Current limitations and further research needs are identified, including designing fully integrated prototypes, empirical research to identify operational processes, testing and analysing operational-level models or applications and developing and applying a technology acceptance model for the integration paradigm. Previous research lacks contextual settings, real-world tests or empirical investigations and is primarily conceptual.

This paper provides a comprehensive, critical systematic review of the integration of blockchain with BIM in the construction industry, using a sociotechnical perspective-based framework which can be applied in future reviews. The study provides insight into the current state and future opportunities for policymakers and practitioners in the AECO industry to prepare for the transition in this disruptive paradigm. It also provides a phased plan along with a clear direction for the transition to more advanced applications.

The conventional design method relies on a priori knowledge, which limits the rapid and efficient development of electronic packaging structures. The purpose of this study is to propose a hybrid method of data-driven inverse design, which couples adaptive surrogate model technology with optimization algorithm to to enable an efficient and accurate inverse design of electronic packaging structures.

The multisurrogate accumulative local error-based ensemble forward prediction model is proposed to predict the performance properties of the packaging structure. As the forward prediction model is adaptive, it can identify respond to sensitive regions of design space and sample more design points in those regions, getting the trade-off between accuracy and computation resources. In addition, the forward prediction model uses the average ensemble method to mitigate the accuracy degradation caused by poor individual surrogate performance. The Particle Swarm Optimization algorithm is then coupled with the forward prediction model for the inverse design of the electronic packaging structure.

Benchmark testing demonstrated the superior approximate performance of the proposed ensemble model. Two engineering cases have shown that using the proposed method for inverse design has significant computational savings while ensuring design accuracy. In addition, the proposed method is capable of outputting multiple structure parameters according to the expected performance and can design the packaging structure based on its extreme performance.

Because of its data-driven nature, the inverse design method proposed also has potential applications in other scientific fields related to optimization and inverse design.

Focusing on small- and medium-sized enterprises (SMEs), which are characterized by resource restrictions, this paper aims to explore consumer segment profiles by considering demographic, personality and creativity traits to determine whether consumers with different profiles exhibit distinct affective reactions to different logo design types (organic, cultural and abstract).

This exploratory study incorporates recent methodological developments, such as the novel response style correction method, to account for response style effects in evaluations of affect toward logo design. In separate analyses, respondents are segmented according to response style–corrected logo affect and personality and creativity items. The segmentation analysis relies on reduced k-means, a joint dimension and cluster analysis method, which accounts for dependencies between items while maximizing between-cluster variability. A total of 866 respondents from the Iberian Peninsula (Portugal: n = 543; Spain: n = 323) participated.

Based on a study using unknown logos (proxy for lower levels of budget communication, characteristics of SMEs), results reveal that there are three segments of consumers based on their affective response toward logo design: logo design insensitives, cultural logo dislikers and organic logo lovers. These segments are associated with different personality traits, creativity and biological sex (although biological sex is not a discriminant variable).

The decision not to control logos by color, to increase external validity, could limit the study’s internal validity if this aspect interacts with relevant study variables. Nevertheless, the empirical evidence can be used to further test associations between consumer profiles and responses to logo design.

Findings highlight the relevance of considering complex profile segments, combining demographics, psychographics and creativity to predict affective consumer responses to brand logo design. This research provides guidelines for SMEs when choosing or modifying their logo design to appeal to different consumer segments.

This study provides managers of SMEs (less present nowadays in empirical studies) with evidence suggesting that complex customer profiles help to understand differences in affective responses to natural logo designs. Furthermore, it relies on the use of a novel methodological development that improves the accuracy of the exploratory study developed.