Morteza Ghobakhloo, Mohammad Iranmanesh, Mantas Vilkas, Andrius Grybauskas and Azlan Amran
The present study offers a holistic but detailed understanding of the factors that might affect small and medium-sized enterprises (SMEs) adoption of Industry 4.0 technologies to…
Abstract
Purpose
The present study offers a holistic but detailed understanding of the factors that might affect small and medium-sized enterprises (SMEs) adoption of Industry 4.0 technologies to empower smaller businesses to embrace Industry 4.0.
Design/methodology/approach
The study conducted a systematic review of the literature and drew on the technology-organization-environment framework to identify various technological, organizational and environmental determinants of Industry 4.0 technology adoption and their underlying components. The study applied the textual narrative synthesis to extract findings from the eligible articles and interpret them into the Industry 4.0 technology adoption roadmap.
Findings
Industry 4.0 is a vital strategic option to SMEs, enabling them to keep up with the digitalization race. SMEs significantly lag behind large organizations in benefiting from disruptive Industry 4.0 technologies. SMEs are still struggling with the initial adoption decisions regarding the digital transformation under Industry 4.0. Results identified various determinants that might explain this condition. The study developed a digitalization roadmap that describes the necessary conditions for facilitating SMEs’ digitalization under Industry 4.0.
Practical implications
Various technological, organizational and environmental factors might determine the current positioning of SMEs against Industry 4.0. These determinants can act as barriers or drivers depending on their properties. The roadmap describes determinants indispensable to promoting Industry 4.0 technology adoption among SMEs, such as knowledge competencies or value chain digitalization readiness.
Originality/value
Exclusively focusing on empirical research that reported applied insights into Industry 4.0 technology adoption, the study offers unique implications for promoting Industry 4.0 digital transformation among SMEs.
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Andrius Grybauskas and Vaida Pilinkiene
The purpose of this paper is to investigate whether real estate investment trusts (REITs) have any significant cost-efficiency advantages over real estate operating companies…
Abstract
Purpose
The purpose of this paper is to investigate whether real estate investment trusts (REITs) have any significant cost-efficiency advantages over real estate operating companies (REOCs).
Design/methodology/approach
The data for listed companies were extracted from the Bloomberg terminal. The authors analyzed financial ratios and conducted a non-parametric data envelope analysis (DEA) for 534 firms in the USA, Canada and some EU member states.
Findings
The results suggest that REITs were much more cost-efficient than REOCs by all the parameters in the DEA model during the entire three-year period under consideration. Although the debt-to-equity levels were similar, REOCs were more relying on short-term than long-term maturities, which made them more vulnerable against market corrections or shocks. Being larger in asset size did not necessarily guarantee greater economies of scale. Both – the cases of increasing economies of scale and diseconomies – were detected. The time period 2015–2017 showed the general trend of decreasing efficiency.
Originality/value
Very few papers on the topic of REITs have attempted to find out whether a different firm structure displays any differences in efficiency. Because the question of REITs and sustainable growth of the real estate market has become a prominent issue, this research can help EU countries to consider the option of adopting a REIT system. If this system were successfully implemented, the EU member states could benefit from a more sustainable and more rapid growth of their real estate markets.
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Alina Stundziene, Vaida Pilinkienė and Andrius Grybauskas
This paper aims to identify the external factors that have the greatest impact on housing prices in Lithuania.
Abstract
Purpose
This paper aims to identify the external factors that have the greatest impact on housing prices in Lithuania.
Design/methodology/approach
The econometric analysis includes stationarity test, Granger causality test, correlation analysis, linear and non-linear regression modes, threshold regression and autoregressive distributed lag models. The analysis is performed based on 137 external factors that can be grouped into macroeconomic, business, financial, real estate market, labour market indicators and expectations.
Findings
The research reveals that housing price largely depends on macroeconomic indicators such as gross domestic product growth and consumer spending. Cash and deposits of households are the most important indicators from the group of financial indicators. The impact of financial, business and labour market indicators on housing price varies depending on the stage of the economic cycle.
Practical implications
Real estate market experts and policymakers can monitor the changes in external factors that have been identified as key indicators of housing prices. Based on that, they can prepare for the changes in the real estate market better and take the necessary decisions in a timely manner, if necessary.
Originality/value
This study considerably adds to the existing literature by providing a better understanding of external factors that affect the housing price in Lithuania and let predict the changes in the real estate market. It is beneficial for policymakers as it lets them choose reasonable decisions aiming to stabilize the real estate market.
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Morteza Ghobakhloo, Masood Fathi, Mohammad Iranmanesh, Mantas Vilkas, Andrius Grybauskas and Azlan Amran
This study offers practical insights into how generative artificial intelligence (AI) can enhance responsible manufacturing within the context of Industry 5.0. It explores how…
Abstract
Purpose
This study offers practical insights into how generative artificial intelligence (AI) can enhance responsible manufacturing within the context of Industry 5.0. It explores how manufacturers can strategically maximize the potential benefits of generative AI through a synergistic approach.
Design/methodology/approach
The study developed a strategic roadmap by employing a mixed qualitative-quantitative research method involving case studies, interviews and interpretive structural modeling (ISM). This roadmap visualizes and elucidates the mechanisms through which generative AI can contribute to advancing the sustainability goals of Industry 5.0.
Findings
Generative AI has demonstrated the capability to promote various sustainability objectives within Industry 5.0 through ten distinct functions. These multifaceted functions address multiple facets of manufacturing, ranging from providing data-driven production insights to enhancing the resilience of manufacturing operations.
Practical implications
While each identified generative AI function independently contributes to responsible manufacturing under Industry 5.0, leveraging them individually is a viable strategy. However, they synergistically enhance each other when systematically employed in a specific order. Manufacturers are advised to strategically leverage these functions, drawing on their complementarities to maximize their benefits.
Originality/value
This study pioneers by providing early practical insights into how generative AI enhances the sustainability performance of manufacturers within the Industry 5.0 framework. The proposed strategic roadmap suggests prioritization orders, guiding manufacturers in decision-making processes regarding where and for what purpose to integrate generative AI.
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Morteza Ghobakhloo, Mantas Vilkas, Alessandro Stefanini, Andrius Grybauskas, Gediminas Marcinkevicius, Monika Petraite and Peiman Alipour Sarvari
Using a dynamic capabilities approach, the present study aims to identify and assess the effects of organizational determinants on capabilities underlying Industry 4.0 design…
Abstract
Purpose
Using a dynamic capabilities approach, the present study aims to identify and assess the effects of organizational determinants on capabilities underlying Industry 4.0 design principles, such as integration, virtualization, real-time, automation and servitization.
Design/methodology/approach
PLS-SEM enables a two-stage hierarchical latent variable reflective-formative model which was used for assessing the effect of organizational determinants on Industry 4.0 design principles. Five hundred six manufacturing companies constitute the effective sample, representing a population of manufacturing companies in an industrialized country.
Findings
The findings reveal that Industry 4.0 design principles extensively depend on digitalization resource availability. At the same time, companies that possess digitalization and change management capabilities tend to devote more resources to digitalization. Finally, the paper reveals that networking and partnership capability is the critical enabler for change management and digitalization capabilities.
Practical implications
The paper provides empirical evidence that the successful development of Industry 4.0 design principles and their underlying integration, virtualization, real-time, automation and servitization capabilities are resource dependent, requiring significant upfront investment and continuous resource allocation. Further, the study implies that companies with networking and partnership, change management and digitalization capabilities tend to allocate more resources for Industry 4.0 transformation.
Originality/value
Exclusively focusing on empirical research that reported applied insights into determinants of Industry 4.0 design principles, the study offers unique implications for promoting Industry 4.0 digital transformation among manufacturing companies.
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Alina Stundziene, Vaida Pilinkiene and Andrius Grybauskas
This paper aims to identify the economic stimulus measures that ensure stability of the Lithuanian housing market in the event of an economic shock.
Abstract
Purpose
This paper aims to identify the economic stimulus measures that ensure stability of the Lithuanian housing market in the event of an economic shock.
Design/methodology/approach
The econometric analysis includes stationarity test, Granger causality test, correlation analysis, autoregressive distributed lag models and cointegration analysis using ARDL bounds testing.
Findings
The econometric modelling reveals that the housing price in Lithuania correlates with quarterly changes in the gross domestic product and approves that the cycles of the real estate market are related to the economic cycles. Economic stimulus measures should mainly focus on stabilizing the economics, preserving the cash and deposits of households, as well as consumer spending in the case of economic shock.
Originality Value
This study is beneficial for policy makers to make decisions to maintain stability in the housing market in the event of any economic shock.