Structural Modeling Approach Research Articles

Overcoming barriers in automotive SMEs to attain international competitiveness: an ISM approach modelling

PurposeThis paper aims to identify and model barriers to internationalising automotive small and medium-sized enterprises (SMEs) from emerging market perspectives using the interpretive structural modelling (ISM) approach.Design/methodology/approachIn this paper, 13 critical barriers are identified through an exhaustive literature review and the Delphi method. The ISM tool is then used to establish interrelationships among the identified barriers to expose and discuss the key barriers having high-driving power.FindingsIt was found that barriers such as trade agreements and export documentation, exchange rates and material inadequacies were relatively less challenging than the other barriers. At the next level, there are barriers such as supply chain, high international quality standards, legal barriers, skilled labour marketing capacity and information and logistics and infrastructure. Finally, barriers such as government policies, entrepreneurial orientation and technology and finance availability posed the most significant challenge for the internationalisation of Indian SMEs. These barriers warrants immediate and considerable attention.Research limitations/implicationsThis study developed a model based on experts’ opinions, which may be biased and influence the final model as proposed in this study. This research will help the owners/managers of the SMEs and policymakers identify and understand the significance and relevance of automotive sector barriers while strategizing.Originality/valueTo the best of the authors’ knowledge, this is the first time an attempt has been made to apply ISM methodology to explore the interdependencies among the critical barriers of internationalisation for SMEs of Indian automotive industries. This study will guide the owner–managers management practices to overcome ineffective practices and move towards successful internationalisation.

Unfolding the impacts of metaverse aspects on telepresence, product knowledge, and purchase intentions in the metaverse stores

The metaverse is a confluence of several technical breakthroughs that promotes social and economic engagement, resulting in a novel and immersive online purchasing experience. The main objective of this study is to investigate how metaverse attributes affect users' sense of telepresence, product knowledge and purchase intentions in the metaverse stores. This research provides a theoretical basis for understanding customer behavior in virtual stores by adapting the Stimulus Organism Response (S–O-R) model. The Partial Least Square Structural Equations Modelling (PLS-SEM) approach was applied to analyze the data collected from 258 respondents via an online survey. Respondents were consumers who have actively engaged in the metaverse applications. Metaverse stimuli attributes (i.e., effectiveness, vividness, and interactivity) have a positive significant influence on metaverse organismic experience (i.e., telepresence). Additionally, metaverse telepresence has a significant positive influence on customers responses (i.e., perceived product knowledge, and purchase intentions). This study offers cutting-edge recommendations for tech giants and metaverse developers to facilitate consumers’ buying behavior in metaverse stores.

Development of a Japanese version of the Awe Experience Scale (AWE-S): A structural topic modeling approach

Background: Awe, a complex emotion, arises in response to perceptually and conceptually vast stimuli that transcend one’s current frames of reference, which is associated with subjective psychological phenomena, such as a sense of self and consciousness. This study aimed to develop a Japanese version of the Awe Experience Scale (AWE-S), a widely used questionnaire that robustly measured the state of awe, and simultaneously investigated how the multiple facets of awe related to the narrative representations of awe experiences. Methods: The Japanese AWE-S was created via back-translation and its factor structure and validity was investigated through an online survey in Japan. Results: The results revealed that the Japanese AWE-S consisted of the same six factors as the original (i.e., time, self-loss, connectedness, vastness, physiological, and accommodation) and had sufficient internal consistency, test-retest reliability, construct validity, and also Japan-specific characteristics. The structured topic modeling generated seven potential topics of the descriptions of awe experiences, which were differently associated with each factor of the Japanese AWE-S. Conclusions: Our findings contribute to a deeper understanding of awe and reveal the constructs of awe in Japan through cross-cultural comparisons. Furthermore, this study provides conceptual and methodological implications regarding studies on awe.

Modelling the operational maturity challenges faced by online food ordering and delivery enterprises during Covid-19 lockdown in Oman: a fuzzy interpretive structural modelling approach

Purpose This research aims to ascertain the various operational maturity challenges faced by the online food ordering and delivery enterprises (OFODE), their nature and their interactive relationships. In particular, this paper aims to (a) identify the most relevant operational maturity challenges faced by the OFODE during the COVID-19 lockdown in Oman, (b) explore and establish any likely structural relationship among these challenges and (c) put them into logical clusters. Design/methodology/approach Experts helped to reduce the 18 initially identified maturity challenges to 13 most pressing ones. Mutual relationships, dominance of interactions and their classifications were explored using fuzzy interpretive structural modeling (FISM) and fuzzy MICMAC analysis. Findings The study of situation-specific operational maturity challenges convinced the authors to propose a distinct FISM model that depicts the relationship among these challenges. Keeping commissions and fees reasonable emerges as the challenge which all other challenges seemingly culminate into. One of the most important situation-specific challenges (i.e. customer confidence about infection free delivery) emerges as a linkage challenge which aggravates as well as is aggravated by certain challenges. Research limitations/implications Besides enriching literature, the proposed model has implications for practitioners particularly when the similar lethal waves are experienced anywhere. The number of respondents, subjective approach, specific context as well as the geographical area coverage are the key limitations. Originality/value To the best of the authors’ knowledge, this study is the first known scientific effort which attempts to model the operational maturity challenges faced by the OFODE during COVID-19 lockdown period. The authors used the FISM modeling approach to forge these interrelated challenges into a structural model.

An integrated Best–Worst Method and Interpretive Structural Modeling approach for assessing the barriers to circular economy implementation

The Circular Economy (CE) concept has emerged as a promising alternative for addressing the serious challenges faced by industries with high involvement in environmental pollution and waste generation. CE can contribute to minimizing waste generation and ecological degradation and ensuring environmental protection while maximizing the utilization of the available resources. Although research has analyzed the CE from different perspectives, insufficient emphasis in the extant empirical literature is given to Electronics Manufacturing (EM) industries in emerging economies. The overall generation of electronic and e-waste has grown exponentially, mainly due to the massive increase in the sale and use of modern electronic gadgets. This study aims to identify and prioritize the barriers to implementing CE in the EM industry and analyze their interrelationships. The process begins by identifying 15 significant barriers from the literature review. An integrated Best–Worst Method (BWM) and Interpretive Structural Modeling (ISM) with Matrice d’Impacts Croisés Multiplication Aappliquée à un Cclassement (MICMAC) framework were then utilized to prioritize the barriers based on their significance level. Lastly, we identify systemic interrelationships among these barriers. We show that the three topmost significant barriers behind CE adoption are the need for high initial investment and financial backing, lack of proper legislative support, and lack of support from top management. This study contributes to the current body of CE knowledge, guiding managers and policymakers in emerging economies to take proper actions for improving sustainability and achieving Sustainable Development Goals (SDGs) through sustainable and responsible resource utilization.

Readiness of financial resilience in start-ups

Financial resilience refers to a start-up's capacity to anticipate, plan for, respond to, and adapt to gradual change and abrupt unforeseen shocks to survive and thrive by enacting appropriate economic policies to decrease budget deficits. Economic history tells us that more companies fail to emerge from a downturn than go into or during it. Many studies have been done on financial resilience in many dimensions, but no one has studied start-ups’ organizational readiness for financial resilience. This gap inspires the current research, which uses the Total Interpretive Structural Modelling (TISM) approach to identify financial resilience factors and analyze hierarchical interrelationships start-ups’ organizational readiness factors for financial resilience. This article aims to identify, assess, and categorize start-up organizational preparation elements for financial resilience. The result shows that the first importance should be given to digital financial innovation, liquidity planning, going concern consideration, financial strategy of CFOs, and cyberthreats. Managers of start-ups can utilize the findings of this study to prepare for financial resilience professionally. In a fast-paced environment, start-ups may use financial resilience to gain a competitive edge.

Evaluation of ISO 9001 Barriers in Manufacturing Organizations Using ISM-Fuzzy MICMAC Approach

The prime motive of this research study is to identify the barriers of ISO 9001 in manufacturing organizations in order to institute the appropriate associations and classify them into different categories based on their driving power ([Formula: see text]) and dependence power ([Formula: see text]). For this purpose, 20 barriers influencing the ISO 9001 implementation has been identified through existing literature works. Afterwards, interpretive structural modeling (ISM) approach has been applied to develop the relationship hierarchy and fuzzy cross-impact matrix multiplication applies to classification (MICMAC) analysis has been carried out to form the category groups. The frameworks developed in this paper will serve as an information bank in order to identify the significant barriers in ISO 9001 implementation path and develop suitable strategies or steps in order to eliminate them.

An interpretive model to assess the barriers to ocean energy toward blue economic development in India

Energy from the ocean has been globally recognized as one of the most significant sources of renewable energy. In the Indian context, it has the potential to meet the country's long-term energy requirements as the Indian peninsula possesses a vast coastline. However, harnessing the energy from the ocean has several challenges and risks that serve as a major roadblock to India's attainment of this energy advantage. This study explores the challenges to ocean energy development by reviewing the literature and analyses the complex interactions between them using the Interpretive Structural Modelling approach. Data are collected from twenty-six experts and an interpretive model is developed. The analysis reveals that lack of policy push, technology, R&D, and trained workforce are some of the key barriers to the development of the ocean energy sector in India, requiring special focus to streamline the infrastructural development of ocean energy as a sustainable source. The findings provide valuable strategic insights to policymakers.

Critical Failure Factors of Flood Early Warning and Response Systems (FEWRS): A Structured Literature Review and Interpretive Structural Modelling (ISM) Analysis

Flood warning and response systems are essential components of risk reduction strategies with the potential to reduce loss of life and impact on personal assets. However, recent flood incidents have caused significant loss of human lives due to failures in current flood warning and response mechanisms. These failures are broadly related to policies concerning, and governance aspects within, warning generation, the behaviour of communities in responding to early warnings, and weaknesses in associated tools and technologies used in communicating early warnings and responding. Capturing critical failure factors affecting flood warning and response systems can provide opportunities for making corrective measures and for developing a more advanced and futuristic system for early flood warnings. This paper reports the findings of a structured review that was conducted to identify critical failure factors in flood early warning and response systems. This study found twenty-four critical failure factors (CFFs). The interpretive structural modelling (ISM) approach conducted in this study resulted in identifying four different types of failure factors (autonomous, dependent, linkage, and independent) with varying dependence and driving powers. Analysis shows that governance, leadership, finance, standard operating procedures (SoP), and community engagement are the most dominating factors with the highest driving factor, which can overcome other dependent factors. The outcome of this review could be helpful for policymakers and practitioners in overcoming failure factors and implementing effective early warning and response systems.

A deep learning model for predicting optimal distance range in crosslinking mass spectrometry data.

Macromolecular assemblies play an important role in all cellular processes. While there has recently been significant progress in protein structure prediction based on deep learning, large protein complexes cannot be predicted with these approaches. The integrative structure modeling approach characterizes multi-subunit complexes by computational integration of data from fast and accessible experimental techniques. Crosslinking mass spectrometry is one such technique that provides spatial information about the proximity of crosslinked residues. One of the challenges in interpreting crosslinking datasets is designing a scoring function that, given a structure, can quantify how well it fits the data. Most approaches set an upper bound on the distance between Cα atoms of crosslinked residues and calculate a fraction of satisfied crosslinks. However, the distance spanned by the crosslinker greatly depends on the neighborhood of the crosslinked residues. Here, we design a deep learning model for predicting the optimal distance range for a crosslinked residue pair based on the structures of their neighborhoods. We find that our model can predict the distance range with the area under the receiver-operator curve of 0.86 and 0.7 for intra- and inter-protein crosslinks, respectively. Our deep scoring function can be used in a range of structure modeling applications.

Structural classification of peptide/HLA complexes enables accurate high-throughput modeling

Abstract The class I major histocompatibility complex (MHC-I) proteins display 8 to 15 residue peptides derived from endogenous proteins on the cell surface for immune surveillance. Accurate structural modeling of peptides bound to HLA (the human MHC) proteins has been mired by the inability to capture the central residues of the peptide. Here, an analysis of high-resolution X-ray crystal structures revealed that diverse peptide conformations arise as a result of local changes in the backbone configuration. As a result, we established an internal-coordinate based system to assess structural similarity between peptide backbones and found that peptide/HLA complexes, encompassing a wide range of allotypes and peptide sequences, present a discrete set of peptide backbone conformations. Leveraging these representative backbones and a regression model trained on a physically realistic scoring function, we developed RepPred, a structural modeling approach which predicts near native nonamer/HLA-A*02:01 complexes with high accuracy and throughput. Our method significantly outperformed five state-of-the-art peptide/HLA modeling approaches on the basis of structural accuracy. Thus, the structural information obtained from RepPred can be used to address long-standing immunology challenges such as predicting peptide immunogenicity. Supported by grants from NIH (R01AI143997, R35GM125034, U01DK112217).

Are the global economic policy uncertainties blocking the export flows of emerging markets? A heterogeneous panel SVAR analysis

Purpose – This paper examines the effect of global economic policy uncertainty (EPU) on emerging markets (EMs) export flows. Methods – This paper uses a structural panel vector autoregression modeling approach to capture country interdependencies and the likelihood that EMs’ responses are heterogeneous and dynamic. An unbalanced monthly panel data from 2003:01 to 2019:12 is used to estimate impulse responses and variance decompositions not only for the entire panel data but also for each EM. Findings – The results show that global EPU has a persistent and negative effect on exports, while foreign income and the exchange rate increase export volumes in EMs. Given the different responses of EMs to uncertainty shocks, the second-stage regression estimates suggest that greater sectoral export diversification in an EM can potentially reduce the unfavorable impact of global EPU on their export flows. Meanwhile, the higher technology content of exports leads to a multiplication of global EPU transmissions. Implication – These findings advance the literature by highlighting the importance of accounting for the transmission effect of global EPU in EMs by considering country heterogeneity. Originality – This is the sole paper examining the factors that mitigate or amplify GEPU impacts on export flows by estimating second-step ordinary least square equations.

Challenges to Sustainable Development in China’s Banking Industry: A Structural Equipment Modeling Approach for Fighting Phishing in China

This research aims to explore how China’s banking industry is influenced by phishing. We used a statistical analysis method and the Structural Equation Model (SEM). The results show that the degree of public security force involvement, the function of telecom operators, the implementation of relative legislation initiatives, and the issues of cooperative governance are vital to limit the occurrence rate of phishing and related cybercrimes, which challenge the development of China’s banking industry. This research will hopefully guide Chinese policy makers to create and implement more progressive initiatives for preventing phishing and facilitate the development of China’s banking industry.

Saudization Barriers in Labor Market: An Analytical Study

This study identifies Saudization barriers. According to the literature review, thirteen key barriers were identified and classified as Individual, Organizational and Governmental barriers, including: Refusal to work in low-skilled positions (1), Inactive job seeking (2), Lack of interest in vocational training (3), Preference of governmental sector jobs (4), Deficit in educational requirements and qualifications (5), Insufficient skilled Saudi workers (6), Low-cost hiring process (7), Lack of Labor market-driven requirements and qualifications (8), Lack of soft-skills and work ethics (9), Low job commitment and Ghost jobs among Saudi female worker (10), Unclear policies and data collection strategies (11), Forced Saudization in specific occupations (12), and Deficit in interministerial collaboration (13). The Interpretive Structural Modeling (ISM) approach was used to understand the inter-relationships between the identified factors, after collecting the opinion of human resources experts in different industries in the Saudi labor market. This study also identified the “driving barriers” (i.e, the barriers influenced by the other barriers) according to MICMAC analysis. The findings of the ISM model indicate that the top barriers are three; Lack of interest in vocational training (3), Preference of governmental sector jobs (4) and Deficit in educational requirements and qualifications (5), hence at the top of this model. This study identified and showed the interrelationships between barriers, which is helpful for decision-makers to be able to direct future initiatives to overcome top barriers and develop inter-sectorial and inter-ministerial coordination plans to develop clear strategies and data collection methodologies to support the country Vision 2030 in reducing unemployment rates among Saudi workers.

Дистанційне зондування як ефективний інструмент проєктування доріг

The subject of this study is the process of engineering and geodetic surveys for developing road projects. The purpose of this article is to improve the efficiency of the process of engineering and geodetic research in road design by implementing remote data from various sources and their systematization and algorithmization of use. The task is to analyze the preparatory and field stages of the process to determine possible ways of increasing its efficiency. Focusing on information flows, to develop an information model of the process of engineering and geodetic surveys and the corresponding scientific and methodological support for road design to introduce advanced technologies for collecting data about the terrain, which reduce the time of surveys and increase the accuracy of the results obtained. For this, the following methods are used: system and structural analysis, set theory, and DFD modeling. Scientific results have been obtained. The requirements of the current legislation regarding searches and the best experience of their conduct have been systematized. A set-theoretic model of information flows of the process of engineering and geodetic surveys is proposed, with the involvement of structural modeling approaches that make it possible to combine remote data from different sources and consider the dynamics of their changes and the logic of interaction. The IDEF0 model, which considers remote sensing data as a source of accurate and up-to-date information, has been developed, and the data flow diagram algorithmizes the mechanism of their combination to obtain the necessary topographical and geodetic materials. The developed scientific and methodical support for information support of road design creates a structure of information technology for engineering and geodetic surveys. Its experimental use has confirmed the reduction of the time for performing geodetic works while simultaneously increasing the accuracy of the obtained geometric and geodetic parameters, which are necessary for the formation of objective conclusions when forming the estimate of the road project, determining the scope of works, etc. Conclusions. The results of the bibliographic search confirmed that the effectiveness of the topographic-geodetic survey procedure in road design is complicated by the large size of the geographical areas to be surveyed, complex engineering and geological conditions, or the presence of restrictions on the duration of work and financial resources. This leads to the need to practically implement into the project-research organizations new approaches based on highly productive methods of collecting information about the area. The scientific and methodological provision of information support for road design has been developed, in particular, the structure of the information technology of engineering and geodetic surveys. Its experimental use has confirmed the reduction of the time required to perform searches and the improvement of the accuracy of the obtained geometric and geodetic parameters of the area where the implementation of the road project is planned.

MPK12 in stomatal CO2 signaling: function beyond its kinase activity.

Protein phosphorylation is a major molecular switch involved in the regulation of stomatal opening and closure. Previous research defined interaction between MAP kinase 12 and Raf-like kinase HT1 as a required step for stomatal movements caused by changes in CO2 concentration. However, whether MPK12 kinase activity is required for regulation of CO2 -induced stomatal responses warrants in-depth investigation. We apply genetic, biochemical, and structural modeling approaches to examining the noncatalytic role of MPK12 in guard cell CO2 signaling that relies on allosteric inhibition of HT1. We show that CO2 /HCO3 - -enhanced MPK12 interaction with HT1 is independent of its kinase activity. By analyzing gas exchange of plant lines expressing various kinase-dead and constitutively active versions of MPK12 in a plant line where MPK12 is deleted, we confirmed that CO2 -dependent stomatal responses rely on MPK12's ability to bind to HT1, but not its kinase activity. We also demonstrate that purified MPK12 and HT1 proteins form a heterodimer in the presence of CO2 /HCO3 - and present structural modeling that explains the MPK12:HT1 interaction interface. These data add to the model that MPK12 kinase-activity-independent interaction with HT1 functions as a molecular switch by which guard cells sense changes in atmospheric CO2 concentration.

Conceptualizing disruptive innovation: an interpretive structural model approach

Christensen insightfully proposed disruptive innovation. The theory is growing stronger but is still haunted by vagueness and post hoc definition issues, which signifies the nature of disruptive innovation may remain unveiled. In order to eliminate the controversies and confusion, this study sorts out commonalities and key driving forces for market encroachment of disruptive innovation, and utilizes the interpretative structural model to reorganize the original theoretical framework based on the principle of “Occam’s razor”. The results show that (1) the value network is the theoretical outset of disruptive innovation and manifests as two different value dimensions at the product or technical level, which endows disruptive innovation with “duality”; (2) the duality makes disruptive innovation have asymmetric advantages and disadvantages compared to existing products or technologies in terms of price and performance; The asymmetry changes enterprises’ competition basis and consumers’ evaluation matrices, which essentially explains the key to market encroachment of disruptive innovation; (3) the duality is the essence that distinguishes disruptive innovation from other types of innovation.

Communicating Concerns, Emotional Expressions, and Disparities on Ethnic Communities on Social Media During the COVID-19 Pandemic: A Structural Topic Modeling Approach

Ethnic and racial disparities in the coronavirus (COVID-19) pandemic raise significant concerns. This study analyzes social media discourses toward four ethnic communities in the United States during the pandemic and reveals disparities in pandemic experiences among them. A total of 488,029 tweets mentioning one of the four ethnic communities, that is, Asians, Blacks, Hispanics, and Native Americans, were investigated by a structural topic modeling approach with emotional expressions and time as covariates in the topic model. The results demonstrate that discourses about Asian, Hispanics, and Native American communities were often induced by pandemic-related events, concerning topics beyond one’s community, and reflecting an experience of implicit racism and an adoption of technical supports from health systems. Meanwhile, discourses about Blacks were racially related, discussing topics within the community, and reflecting an experience of explicit racism and an adoption of psychological supports from ingroup. We discuss the implications of our findings on ethnic health disparities.

Zakatech: Readiness and Development of Zakat Fundraising in Indonesia

Badan Amil Zakat Nasional (BAZNAS) as a sharia social financial institution offers the latest technology in the process of socialization, service and zakat fundraising. The new model offered is ZakaTech, an online payment model that makes it easier for a muzaki (zakat payer) to fulfill his or her responsibility to pay zakat in the midst of the covid-19 pandemic. In addition, the distribution of zakat funds using ZakaTech method is considered very effective in helping mustahik, especially helping the poor who are economically affected by the covid-19 pandemic. This paper aims to analyze the strategy of developing zakat model with Interpretative Structural Modeling (ISM) approach and zakatech Readiness Index. The results show that the main strategy needed in terms of developing ZakaTech is the ability of human resources (amil zakat) in managing and analyzing data in the era of big data using digital services. The main elements involved in ZakaTech development in Indonesia include the government as a regulator, educational institutions, media digital, amil and muzaki (pentahelix).

A DNA damage–induced phosphorylation circuit enhances Mec1ATR Ddc2ATRIP recruitment to Replication Protein A

The cell cycle checkpoint kinase Mec1ATR and its integral partner Ddc2ATRIP are vital for the DNA damage and replication stress response. Mec1-Ddc2 "senses"single-stranded DNA (ssDNA) by being recruited to the ssDNA binding Replication Protein A (RPA) via Ddc2. In this study, we show that a DNA damage-induced phosphorylation circuit modulates checkpoint recruitment and function. We demonstrate that Ddc2-RPA interactions modulate the association between RPA and ssDNA and that Rfa1-phosphorylation aids in the further recruitment of Mec1-Ddc2. We also uncover an underappreciated role for Ddc2 phosphorylation that enhances its recruitment to RPA-ssDNA that is important for the DNA damage checkpoint in yeast. The crystal structure of a phosphorylated Ddc2 peptide in complex with its RPA interaction domain provides molecular details of how checkpoint recruitment is enhanced, which involves Zn2+. Using electron microscopy and structural modeling approaches, we propose that Mec1-Ddc2 complexes can form higher order assemblies with RPA when Ddc2 is phosphorylated. Together, our results provide insight into Mec1 recruitment and suggest that formation of supramolecular complexes of RPA and Mec1-Ddc2, modulated by phosphorylation, would allow for rapid clustering of damage foci to promote checkpoint signaling.