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The purpose of this paper is to develop a model to analyze the interactions among the competitiveness factors of the real estate industry on the basis of Porter's Diamond Model. The model provides insights into the relationship between these factors in the context of the Beijing and Tianjin real estate industries.

Based on Porter's Diamond Model, this paper establishes the competitiveness factors model and divides the factors into four key categories. (i.e. productivity element, demand constraint, the strategy or structure of relevant and supportive industry and corporation, and horizontal competition). After relevant indices are picked up in each category, the paper utilizes structural equation modeling to analyze the contribution of each factor on competitiveness of real estate industry. Data are collected from Beijing and Tianjin in China and the model is practiced in the context of the real estate industry of the two cities.

Supported by empirical evidence, this study finds out that related industries have the most significant influence on competitiveness of real estate industry and the second important is demand factors. Based on these, four pieces of suggestion are given to improve the competitiveness of real estate industry combining with the condition of Beijing and Tianjin in this paper.

This research builds a conceptual model based on Porter's Diamond model to provide a much more comprehensive understanding of the interactions between competitiveness factors of real estate industry, and introduces structural equation modeling to quantitatively analyze the contribution of each factor to competitiveness.

Meetings, incentives, conferences and exhibitions (MICE) tourism has established as a tourism segment that is growing in popularity. It is less seasonality dependent, promotes the offer of services and contributes to the development of the sector. Therefore, this study aims to analyze the competitiveness factors for the improvement of MICE tourism in the city of Mazatlan.

It was developed with a mixed approach, using quantitative and qualitative data collection techniques, such as interviews with experts, surveys of stakeholders in the tourism sector and documentary analysis. Based on the theoretical review, the following four competitiveness factors were defined for MICE tourism: 1) resource factors, 2) destination management factors, 3) conditioning factors of the environment and 4) conditioning factors of the demand, applying and importance-performance analysis.

The results indicate that the factors of competitiveness in the case of the study that had greater importance and better performance are the conditioning factors of the demand and resource factors. However, the development and implementation of comprehensive destination management strategies are required to improve this segment, as well as giving due importance to taking into account the important conditioning factors of the environment.

This study makes a theoretical contribution to the literature on the competitiveness of tourist destinations in the MICE segment by identifying the factors for its development, as well as the practical implications for the specific case study. In addition to this, it was identified that there are few empirical studies that analyze the factors that contribute to improving the competitiveness of this segment.

The construction industry is facing an enormous number of challenges due to continuous advancements in construction technologies and techniques. Hence, construction management theories have to confront critical newly issues concerning market globalization and construction innovations. The key factor to address these challenges is to ameliorate the competitive abilities of the competing construction firms. In this context, measuring competitiveness of construction firms is an efficacious approach to amplify their competitive growth and profitability. To this end, the purpose of this research paper is to design a three-tier multi-criteria decision making model for competitiveness assessment and benchmarking of construction companies, meanwhile tackling a wide range of essential factors and attributes that covers broad aspects of the present competitive market.

In the first tier, four new pillars (4P) of competitiveness assessment are introduced for construction firms, namely, organization performance, project performance, environment and client and innovation and development. These pillars are able to aid in construction firms’ management on both long and short term basis. Hence, 21 key competitive factors and eighty key competitive criteria are identified, incorporated and analyzed in this research study. The second tier encapsulates carrying out a questionnaire survey in the Canadian and Vietnamese market to garner two main sets of information. The first set of information incorporates responses of the pairwise comparisons between competitiveness factors and criteria. The second set involves gathering utility scores pertinent to each competitiveness criteria. The developed model then leverages the use of analytical hierarchy process to scrutinize the relative importance priorities of competitiveness factors and criteria. The third tier of the developed model encompasses the use of multi-attribute utility theory to compute competitiveness scores for construction companies through blending criteria’ relative importance weights alongside their respective utility functions. In addition, the third tier comprises conducting a sensitivity analysis to derive the most important criteria influencing the overall competitiveness of construction companies. The developed model is tested and validated using three case studies; one construction company from Canada and two construction companies from Vietnam.

Results demonstrated that the developed model has a potential to render a synthesized and methodical performance evaluation for the competitive ability of a given construction company. Furthermore, it was found that Vietnamese companies are more considerate towards pillars pertaining to environment and client while Canadian companies are more attentive towards innovation and development. The outcome of sensitivity analysis revealed that effectiveness of cost management highly affects the competitive ability of Vietnamese companies while effectiveness of cost management exhibits the most significant influence on the competitive of Canadian companies.

The developed model can benefit construction companies to understand their competitiveness in their market and diagnose their strengths and weaknesses. It is also can be useful in efficient utilization of their limited resources and development of sustainable and long-term strategic plans strategic plans, which consequently leads to maintaining better position in their dynamic business markets.

Literature review manifests that reported competitiveness assessment models and practices are not able to address present challenges, technologies and developments in construction market.

The purpose of this paper is to deepen the discussion regarding the competitiveness of clusters based on a theoretical and empirical study that compares the level of competitiveness of the Brazilian wine cluster located in Serra Gaúcha with the competitiveness of the Chilean cluster located in Valle del Maule.

A qualitative-descriptive approach was applied to the study, and data collection was conducted through secondary sources.

The analysis employed a competitiveness analysis model consisting of 11 competitiveness factors. The Chilean cluster presented a higher level of competitiveness in four competitiveness factors (“scope of viable and relevant business,” “introduction of new technologies,” “balance with no privileged positions” and “oriented strategy”), while the Brazilian cluster presented a higher level of competitiveness in three competitiveness factors (“concentration,” “cooperation” and “replacement”). For four of the competitiveness factors of the model, both clusters presented similar levels of competitiveness.

By comparing the two wine clusters, it was possible to identify aspects that can be improved to increase competitiveness, especially in the Brazilian cluster. These aspects include, first, the need for bottle manufacturers in Serra Gaúcha, which would have a positive impact on production costs; second, the expansion of the geographical indication registration for the entire Serra Gaúcha region, resulting in an enhanced image of Brazilian wine abroad; and third, greater incentives for exports, which would result in an increase in market share.

The paper proposes an explanation for the superior level of competitiveness of the Chilean cluster regarding the “scope of viable and relevant business,” “balance with no privileged positions,” “introduction of new technologies” and “strategy focussed on cluster development.” In terms of its contribution, the study developed additional metrics for the model adopted, which can be used for the competitive analysis of other agribusiness clusters.

This study aims to identify the main factors affecting export competitiveness and its barriers, focusing on the minerals industry so that a scale is achieved for measuring export competitiveness in this industry.

The research was conducted with a mixed method approach in the minerals industry. Among the active companies involved in this industry, 34 export companies and export management companies were selected and evaluated. In the qualitative phase, 18 experts and managers of the industry were interviewed to identify the factors affecting the export competitiveness of these companies and the barriers ahead of them. In the quantitative phase, a questionnaire was distributed among 412 managers and experts in this industry to categorize the identified factors and to measure the relationships among them. For data analysis in the qualitative phase, theme analysis was used. For the quantitative phase, factor analysis and structural equation modeling were adopted.

In addition to identifying the main components affecting the competitiveness of companies in exporting minerals as well as the main barriers ahead of them, the findings of the current research categorized these components using factor analysis. These components were categorized into factors, such as manufacturing factors, demand conditions, related and supporting industries, structural factors, competitive strategy and governmental supports. Afterward, their impacts on export competitiveness were measured and supported.

Although some studies have been conducted to examine the competitiveness in different industries, no research has been found that has examined and identified the main factors affecting export competitiveness and their impacts in the minerals industry with a mixed quantitative and qualitative approach. The findings of this research may help managers and policymakers, at the industrial and national levels, to reach a scale for assessing the export companies involved in this industry by identifying the most essential factors of export competitiveness of minerals. Furthermore, the findings of this research can act as a model for future researchers to develop a scale for export competitiveness in other industries.

The purpose of this paper is to identify and build a hierarchy of the factors influencing competitiveness of electronics manufacturing industry (EMI) at the industry level and apply the interpretive structural modeling, fuzzy Matriced’ Impacts Croisés Multiplication Appliquée á UN Classement (i.e. the cross-impact matrix multiplication applied to classification; MICMAC) and analytic hierarchy process (AHP) approaches. These factors have been explained with respect to managerial and government policymakers’ standpoint in Indian context.

This study presents a hierarchy and weight-based model that demonstrates mutual relationships among the significant factors of competitiveness of the Indian EMI.

This study covers a wide variety of factors that form the bedrock of the competitiveness of the EMI. Interpretive structural modeling and fuzzy MICMAC are used to cluster the influential factors of competitiveness considering the driving and dependence power. AHP is used to rank the factors on the basis of weights. Results show that the “government role” and “foreign exchange market” have a significantly high driving power. On the other hand, the “capital resource availability” and “productivity measures” come at the top of the interpretive structural modeling hierarchy, implying high dependence power.

The study has strong practical implications for both the manufacturers and the policymakers. The manufacturers need to focus on the factors of competitiveness to improve performance, and at the same time, the government should come forward to build a suitable environment for business in light of the huge demand and frame suitable policies.

The lackluster performance of the industry is because of the existing electronics policies and environmental conditions. The proposed interpretive structural modeling and fuzzy MICMAC and AHP frameworks suggest a better understanding of the key factors and their mutual relationship to analyze competitiveness of the electronics manufacturing industry in view of the Indian Government’s “Make in India” initiatives.

This paper contributes to the industry level competitiveness and dynamics of multi-factors approach and utilize the ISM–fuzzy MICMAC and AHP management decision tool in the identification and ranking of factors that influence the competitiveness of the EMI in the country.

This paper aims to identify factors that affect the sports tourism destination's competitiveness on a small island. Hence, this study looks at and evaluates these factors. The study then comes up with a model that clarifies the interrelationships between these factors.

The authors broke down the data analysis process into three steps. The first step was to conduct a literature review and use industry and academia experts' help to determine the essential aspects (fuzzy Delphi method). Then, a hierarchical model was developed, and the factors were categorised using the interpretive structural modelling (ISM) approach. Factors' driving and dependency power were also determined using MICMAC analysis.

This work has identified 13 key factors related to the sports tourism destination's competitiveness on a small island. For a small island like Kish Island, the two independent variables (government support and destination political stability) that define the institutional framework for the destination are most important. Building corresponding competitive and support strategies to address these two independent variables is thus beneficial.

The research's results provide decision-makers, practitioners, and researchers with new insights into the hierarchical model of determinants. The study will fill the existing gap between theory and practice.

Sports tourism destination managers on small islands may benefit from the proposed model since the model will enable them to organise the managers' priorities better to enhance the managers' destinations' competitiveness and provide tourists with a more accurate depiction of the destination.

According to the authors' knowledge, the research design presented in this article has provided the first attempt to hierarchical analyse these factors and develop a model for sports tourism destination competitiveness on small islands and destinations with less-developed economies. This study fills the gap in the destination competitiveness and sports tourism literature by not only identifying the key influencing factors but also examining the interactions between these factors and providing empirical evidence supporting their relationships.

This study explores the variables that drive the impact of artificial intelligence (AI) on the competitiveness of a tourism firm. The relationship between the variables is established using the modified total interpretive structural modelling (m-TISM) methodology. The factors are identified through literature review and expert opinion. This study investigates the hierarchical relationship between these variables.

The modified total interpretive structural modelling (m-TISM) method is used to develop a hierarchical interrelationship among variables that display direct and indirect impact. The competitiveness of a tourism firm is measured by investigating the effect of variables on the firm's financial performance.

The study identifies ten key factors essential for analysing the impact of AI on a firm's competitiveness. The m-TISM methodology gave us the hierarchical relationship between the factors and their interpretation. A theoretical TISM model has been constructed based on the hierarchy and relationship of the elements. The elements that fall in Level V are “AI Skilled Workforce”, “Infrastructure” and “Policies and Regulations”. Level IV includes the elements “AI Readiness”, “AI-Enabled Technologies” and “Digital Platforms”. Elements that fall under Level III are “Productivity” and “AI Innovation”. Level II and Level I comprise “Tourist Satisfaction” and “Financial Performance”, respectively. The levels indicate the elements' hierarchical level, with Level I the highest and Level V the lowest.

Tourism and AI scholars can analyse the given variables by including the transitive links and incorporate new variables depending upon future research. The m-TISM model constructed from literature review and expert opinion can act as a theoretical base for future studies to be conducted by researchers.

Management/Practitioners can focus on the available characteristics and capitalise on them while working on the factors lacking in their organisation to enhance their competitiveness. Entrepreneurs starting their own business can utilise the elements in understanding the ecosystem of strengthening a firm's competitiveness. They can work to improve on the aspects which are crucial and trigger the impact on competitiveness. The government and management can devise policies and strategies that encompass the essential factors that positively impact the competitiveness of the firms. The approach can then be looked at with a holistic approach to cater to the other related components of the tourism industry.

This study is the first of its kind to use the modified TISM methodology to understand the impact of AI on the competitiveness of tourism firms.

This paper aims to assess the contribution of competitiveness factors in how small and medium-size enterprises (SMEs) would access competitive advantage (CA) by focussing on industry structure and devise a conceptual model thereof.

The enterprises from three industries consisting of knowledge-based, single-use medical device producers and construction stone cutting each with different structures were assessed. The method is qualitative and quantitative where grounded theory and exploratory factor analysis (EFA) are applied. The initial survey involves 36 deep semi-structured interviews with some of the top managers of each of the three selected industries and a questionnaire distributed among 158 individuals with 46 structured questions.

The findings indicate that the micro-competitiveness factors are more contributive in achieving CA than macro factors as follows: in knowledge-based enterprises, customer relationship management (CRM), goods/services features and knowledge management are the most important variables. As to single-use medical device production industry, the sales force, sales promotion, and CRM are the most effective factors. Regarding construction stone-cutting industry, quality of the stone, sales promotion, and advertisement play the same role. The results of the EFA indicate that the three impressive factors, including capabilities of the enterprises, strategies of the enterprises and macro factors, are the extracted factors.

The findings here would assist SMEs’ managers in identifying the most essential factors in accessing CA.

The innovation of this study is that although there exist many studies on SMEs and their CAs, this study seeks the models of CA among SMEs in industries with different structures.

The main objective of this study was to establish a model of competitiveness factors to measure the value of construction companies adequately.

A theoretical model of four main factors was developed, and they include human capital, ethical values, process innovation and financing. Information was collected from 18 construction companies in the city of Metropolitan Lima, collecting information on each of these factors. Through an analysis of the principal components, the weighting of each factor about the competitiveness of a construction company was determined.

The cost of person-hours, the cost of equipment to execute work, and the cost of materials used are the strongest indicators to measure the competitiveness of a construction company. On the other hand, the number of employees holding university degrees and the number of master builders holding technical degrees in the human capital factor also have a moderate weight in determining the competitiveness value of a company. The indicator of work delivered within the deadline also moderately affects competitiveness. Finally, the monthly debt payment indicator has a very small effect on the company’s competitiveness.

In conclusion, this study provides evidence of a competitiveness model with highly related factors on human capital, process innovation and ethical values as the most important in measuring competitiveness.

El objetivo principal de la investigación fue establecer un modelo de factores de competitividad para medir adecuadamente el valor de las empresas constructoras.

Se desarrolló un modelo teórico de cuatro factores principales, que incluyen el capital humano, los valores éticos, la innovación de procesos y el financiamiento. Se recolectó información de 18 empresas constructoras de la ciudad de Lima Metropolitana, recolectando información de cada uno de estos factores. A través de un análisis de componentes principales se determinó la ponderación de cada uno de ellos sobre la competitividad de una empresa constructora.

El costo de horas hombre, el costo de los equipos para ejecutar obras y el costo de material utilizado son los indicadores más fuertes para medir la competitividad de una empresa constructora. Por otro lado, la cantidad de empleados con títulos universitarios y la cantidad de maestros de obra con título técnico del factor de capital humano también tienen un peso moderado en la determinación del valor de competitividad de una empresa. El indicador de obras entregadas dentro de plazo también tiene un efecto moderado sobre la competitividad. Finalmente, el indicador de pago mensual de la deuda tiene un efecto muy pequeño sobre la competitividad de la empresa.

En conclusión, el estudio proporciona evidencia de un modelo de competitividad con factores altamente relacionados sobre el capital humano, la innovación de procesos y los valores éticos como los más importantes a la hora de medir la competitividad.