scholarly journals Technological progress spillover effect in Lithuanian manufacturing industry

Equilibrium ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. 783-806
Author(s):  
Mantas Markauskas ◽  
Asta Baliute
Keyword(s):  
Labor Productivity ◽  
Technological Progress ◽  
Manufacturing Industry ◽  
Vector Autoregression ◽  
Spillover Effect ◽  
Manufacturing Industries ◽  
Advantages And Disadvantages ◽  
Productivity Measure ◽  
Factors Influencing ◽  
Profit Value

Research background: Various methods for technological progress assessment and evaluation exist in the context of economic development. Each of the methods possesses distinct advantages and disadvantages in analysis of technological progress fluctuations. For most neoclassical growth theories, technological progress measures are included as exogenous variables, thus excluding evaluation of factors influencing technological progress variation throughout time. Purpose of the article: The aim of this article is to offer improvements on classical technological progress evaluation methodologies for manufacturing industries, separating effect of intersectoral technological progress spillover effect from internal factors influencing technological progress growth and perform analysis in the case of Lithuanian manufacturing industry. Methods: Earlier research papers used linear time series regression and vector autoregression methods to assess technological progress values and define equations explaining effect of different manufacturing level indicators on technological progress measure growth. This research paper uses results of previously mentioned methods and performs simulation analysis applying agent-based modelling framework. Findings & value added: The conducted vector autoregression analysis has showed that two variables which influence technological progress most significantly are labor productivity measure and gross profit value. Sensitivity analysis emphasizes that effect of these two variables on technological progress growth is substantially different. Increase in gross profit value affects technological progress growth for wider range of sectors from Lithuanian manufacturing industry (15 out of 18 analyzed sectors? technological progress measure values are affected by changes in gross profit, while changes in labor productivity influence technological progress values in the case of 9 sectors). Rising gross profit values also produce intersectoral technological progress spillover effect more significantly, while growth in labor productivity measure has stronger effect on technological progress fluctuations for sectors which are able to exploit this effect. Presented research suggests improved methodology for intersectoral technological progress spillover effect assessment in the context of manufacturing industries.

scholarly journals Energy Use and Labor Productivity in Ethiopia: The Case of the Manufacturing Industry

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2714 ◽  
Author(s):  
Selamawit G. Kebede ◽  
Almas Heshmati
Keyword(s):  
Panel Data ◽  
Labor Productivity ◽  
Alternative Model ◽  
Manufacturing Industry ◽  
Energy Use ◽  
Manufacturing Industries ◽  
Dynamic Panel ◽  
Control Variables ◽  
Theoretical Predictions ◽  
Dynamic Panel Estimator

This study investigates the effect of energy use on labor productivity in the Ethiopian manufacturing industry. It uses panel data for the manufacturing industry groups to estimate the coefficients using the dynamic panel estimator. The study’s results confirm that energy use increases manufacturing labor productivity. The coefficients for the control variables are in keeping with theoretical predictions. Capital positively augments productivity in the industries. Based on our results, technology induces manufacturing’s labor productivity. Likewise, more labor employment induces labor productivity due to the dominance of labor-intensive manufacturing industries in Ethiopia. Alternative model specifications provide evidence of a robust link between energy and labor productivity in the Ethiopian manufacturing industry. Our results imply that there needs to be more focus on the efficient use of energy, labor, capital, and technology to increase the manufacturing industry’s labor productivity and to overcome the premature deindustrialization patterns being seen in Ethiopia.

Factors influencing Industry 4.0 adoption

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sabai Khin ◽  
Daisy Mui Hung Kee
Keyword(s):  
Decision Making ◽  
Conceptual Model ◽  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Content Type ◽  
Advantages And Disadvantages ◽  
Factors Influencing ◽  
Use Of Technology ◽  
Market Opportunities ◽  
The Right

PurposeThe digital transformation towards Industry 4.0 (I4.0) has become imperative for manufacturers, as it makes them more flexible, agile and responsive to customers. This study aims to identify the factors influencing the manufacturing firms’ decision to adopt I4.0 and develop a triadic conceptual model that explains this phenomenon.Design/methodology/approachThis study used a qualitative exploratory study design based on multiple case studies (n = 15) from the manufacturing industry in Malaysia by conducting face-to-face interviews. The data were analysed using NVivo. The conceptual model was developed based on grounded theory and deductive thematic analysis.FindingsResults demonstrate that driving, facilitating and impeding factors play influential roles in a firms’ decision-making to adopt I4.0. The major driving factors identified are expected benefits, market opportunities, labour problem, customer requirements, competition and quality image. Furthermore, resources, skills and support are identified as facilitating factors and getting the right people, lack of funding, lack of knowledge, technical challenges, training the operators and changing the mindset of operators to accept new digital technologies are identified as impeding factors.Research limitations/implicationsDue to its qualitative design and limited sample size, the findings of this study need to be supplemented by quantitative studies for enhanced generalizability of the proposed model.Practical implicationsKnowledge of the I4.0 decision factors identified would help manufacturers in their decision to invest in I4.0, as they can be applied to balancing advantages and disadvantages, understanding benefits, identifying required skills and support and which challenges to expect. For policymakers, our findings identify important aspects of the ecosystem in need of improvement and how manufacturers can be motivated to adopt I4.0.Originality/valueThis study lays the theoretical groundwork for an alternative approach for conceptualizing I4.0 adoption beyond UTAUT (Unified Theory of Acceptance and Use of Technology). Integrating positive and negative factors enriches the understanding of decision-making factors for I4.0 adoption.

scholarly journals The Contribution of Agricultural Manufacturing in the Egyptian Economic Growth: Kaldor's Hypotheses

2021 ◽  
pp. 156-166
Author(s):  
Ahmed Abou El-Yazid El-Rasoul ◽  
Mai Mustafa Hassan Morsi ◽  
Mohamed Ibrahim Younis
Keyword(s):  
Economic Growth ◽  
Growth Rate ◽  
Labor Productivity ◽  
Manufacturing Industry ◽  
Agricultural Sector ◽  
Manufacturing Sector ◽  
Least Square ◽  
Manufacturing Industries ◽  
The Real ◽  
Real Growth

This research uses a Kaldor’s hypotheses to estimate the contribution of the agricultural manufacturing sector to increase the economic growth of the Egyptian agricultural sector during the period 1997-2018. It based on the three "hypotheses" of growth. Kaldor model depends on three hypotheses related to the relationship between the growth of manufacturing sector and the economic growth. The study used the growth rate, dummy variable, Ordinary Least Square (OLS) test, and used CUSUM squares test and Chow breakpoint test. In addition to, testing the stability of time series depended on E-view 11.0. The food, beverage, tobacco industries and textiles industry are the largest two sectors in the Egyptian agricultural manufacturing industries, as they represent about 83.58% of the total value of the agricultural manufacturing industries output during the period 1997-2018. The results shows that the increase of real growth rates of food, beverage, tobacco industries and textile production lead to increasing in the real growth rate of agricultural output. According to CUSUM Sq test and Chow test, the year 2003 is considered as the switch point for the study variables. Also, if the real agricultural manufacturing production growth rate increases, the real agricultural manufacturing labor productivity growth rate will increase. And if the real growth rate of agricultural manufacturing production value increases, the real growth rate of agricultural non-manufacturing labor productivity will increase. The results of the research assist decision-makers in the field of manufacturing industry and agriculture in Egypt, especially in the stages of economic development.

scholarly journals Modelling Technological Progress Evaluation: Case of Lithuanian Manufacturing Industry

2020 ◽  
Vol 11 (6) ◽  
pp. 1
Author(s):  
Mantas Markauskas ◽  
Asta Baliute
Keyword(s):  
Granger Causality ◽  
Technological Progress ◽  
Manufacturing Industry ◽  
Spillover Effect ◽  
Granger Causality Test ◽  
Var Model ◽  
Var Models ◽  
Causality Test ◽  
Technological Spillover ◽  
Error Terms

The goal for this research is to build a framework for analysis of technological spillover effect between sectors in Lithuanian manufacturing industry and assess whether predictors of the created model closely follow dynamic fluctuations of technological progress assessed values. Analysis of academic literature suggested using Granger causality test and vector autoregression (VAR) model to analyze intersectoral technological progress spillover effect in any manufacturing industry. Granger causality test can suggest a potential relationship between technological progress values of particular sectors in manufacturing industry while VAR model can define the exact form and extent of spillover effect. VAR models identify presence of intersectoral technological spillover effect in case of 15 out of 18 sectors in Lithuanian manufacturing industry. In case of a few sectors error terms of VAR models are not stationary suggesting that additional exogenous variables need to be included to increase accuracy of estimated coefficients before these models can be used in further analysis. After minor changes presented VAR models can be used for sensitivity analysis analyzing how changes in different sectoral level parameters affect economic development of manufacturing industry as a whole.

scholarly journals ТЕХНОЛОШКА СТРУКТУРА И ПРОДУКТИВНОСТ РАДА ПРЕРАЂИВАЧКЕ ИНДУСТРИЈЕ РЕПУБЛИКЕ СРБИЈЕ – НИВО ОБЛАСТИ

TEME ◽  
2020 ◽  
pp. 1005
Author(s):  
Мићић Владимир ◽  
Савић Љубодраг ◽  
Бошковић Горица
Keyword(s):  
Labor Productivity ◽  
Productivity Growth ◽  
Manufacturing Industry ◽  
Structural Changes ◽  
Value Added ◽  
Productivity Measure ◽  
Technological Structure ◽  
Medium Level ◽  
The Republic ◽  
Technological Intensity

Labor productivity of the manufacturing industry is an important factor of economic growth and compatibility. The aim of the research is to point out the significance of conducting efficient structural and technological changes in the manufacturing industry of the Republic of Serbia and to examine their impact on the growth of labor productivity. Technological structure was examined according to the technological intensity and methodology of OECD. Labor productivity was analyzed by partial productivity measure, value added per employee from the aspect of impact of various factors on its growth, shift-share analysis. The results of the research show that labor productivity growth rates in the manufacturing industry are high and positive, that they are higher than gross value added, which is the result of change in the number of employees. Productivity growth is higher in areas that belong to high and medium-level technology and is based on the inter-sector effect. The results of this research are useful to the creators of industrial politics when initiating structural changes and relocating the factors that impact labor productivity towards more productive areas of the manufacturing industry.

scholarly journals Mechanisms of Environmental Regulation’s Impact on Green Technological Progress—Evidence from China’s Manufacturing Sector

2021 ◽  
Vol 13 (4) ◽  
pp. 1600
Author(s):  
Weijiang Liu ◽  
Mingze Du ◽  
Yuxin Bai
Keyword(s):  
Sustainable Development ◽  
Energy Consumption ◽  
Technological Change ◽  
Environmental Protection ◽  
Technological Progress ◽  
Manufacturing Industry ◽  
Manufacturing Sector ◽  
The Sustainable Development ◽  
Biased Technological Change ◽  
Chinese Manufacturing Industry

As the world’s largest developing country, and as the home to many of the world’s factories, China plays a crucial role in the sustainable development of the world economy regarding environmental protection, energy conservation, and emission reduction issues. Based on the data from 2003–2015, this paper examined the green total factor productivity and the technological progress in the Chinese manufacturing industry. A slack-based measure (SBM) Malmquist productivity index was used to measure the bias of technological change (BTC), input-biased technological change (IBTC), and output-biased technological change (OBTC) by decomposing the technological progress. It also investigated the mechanism of environmental regulation, property right structure, enterprise-scale, energy consumption structure, and other factors on China’s technological progress bias. The empirical results showed the following: (1) there was a bias of technological progress in the Chinese manufacturing industry during the research period; (2) although China’s manufacturing industry’s output tended to become greener, it was still characterized by a preference for overall CO2 output; and (3) the impact of environmental regulations on the Chinese manufacturing industry’s technological progress had a significant threshold effect. The flexible control of environmental regulatory strength will benefit the Chinese manufacturing industry’s technological development. (4) R&D investment, export delivery value, and structure of energy consumption significantly contributed to promoting technological progress. This study provides further insight into the sustainable development of China’s manufacturing sector to promote green-biased technological progress and to achieve the dual goal of environmental protection and healthy economic growth.

Factors Influencing Innovation and Industrial Performance in Chinese Manufacturing Industry

2017 ◽  
Vol 14 (06) ◽  
pp. 1750040 ◽  
Author(s):  
Nnaemeka Vincent Emodi ◽  
Girish Panchakshara Murthy ◽  
Chinenye Comfort Emodi ◽  
Adaeze Saratu Augusta Emodi
Keyword(s):  
Product Development ◽  
New Product Development ◽  
Product Innovation ◽  
Manufacturing Industry ◽  
Positive Impact ◽  
Export Performance ◽  
Patent Application ◽  
New Product ◽  
Factors Influencing ◽  
Industrial Performance

This study investigates the factors influencing the Chinese manufacturing industry’s innovation and industrial performance utilizing a panel data approach on a sample of Chinese manufacturing enterprises over the period of 2008–2013. The industries were grouped according to related sectors into five groups, a general group was also created which included the whole data sample. The study found that research and development (R&D) expenditure positively influenced the growth of product innovation and industrial performance, but not necessarily knowledge innovation and export performance. Also, expenditure on new product development had a positive impact on both innovation and industrial performance. The growth of patent application was discovered to be influenced by an R&D project and foreign patent license. Finally, the number of enterprises and firm size (i.e. number of employees) contributed positively to the industrial output performance. The findings suggest that industrial R&D and new product development influences the success of product innovation and sales performance. The study recommends that the government should set up policies that will stimulate industrial R&D, while supporting technology transfers from foreign partners. Most importantly, government policies on the development of the industry should be addressed on a sectorial level and not a “one-size-fit-all” type of policy.

Cycles in symbol production? A new model to explain concentration, diversity and innovation in the music industry

Popular Music ◽  
1995 ◽  
Vol 14 (1) ◽  
pp. 55-93 ◽  
Author(s):  
Michael Christianen
Keyword(s):  
Negative Correlation ◽  
Music Industry ◽  
The Other ◽  
Recording Industry ◽  
Cultural Goods ◽  
New Model ◽  
Advantages And Disadvantages ◽  
Factors Influencing ◽  
Using Data ◽  
The One

With the publication of the article ‘Cycles in symbol production’ (Peterson and Berger 1975) a discussion started concerning the advantages and disadvantages of the production of cultural goods under market conditions. The analysis by Peterson and Berger showed a negative correlation between concentration in the recording industry, on the one hand, and the diversity and innovativeness of the music, on the other. Repetition of the analysis using data from the 1980s (Burnett 1990; Lopes 1992) has shown that for this period Peterson and Berger's hypotheses should be rejected. Is there a connection between concentration and diversity and innovation? Are there cycles in symbol production? There seems to be no conclusive answer. In this article, I will attempt to clear up this matter. First, I will repeat the analysis of the relation between concentration and diversity/innovation, using the same model as Peterson and Berger, but with different definitions for the variables concentration, diversity and innovation. Then I will suggest a new model, which can be helpful in uncovering other factors influencing diversity and innovation in the music industry. I will come to that later. Let me first give the reader a brief overview of previous research.

scholarly journals Using a Novel Grey DANP Model to Identify Interactions between Manufacturing and Logistics Industries in China

2018 ◽  
Vol 10 (10) ◽  
pp. 3456 ◽  
Author(s):  
Peng Jiang ◽  
Yi-Chung Hu ◽  
Ghi-Feng Yen ◽  
Hang Jiang ◽  
Yu-Jing Chiu
Keyword(s):  
Manufacturing Industry ◽  
Multiple Criteria Decision Making ◽  
Value Added ◽  
Manufacturing Industries ◽  
Direct Influence ◽  
Key Factors ◽  
Logistics Industry ◽  
Influence Matrix ◽  
Factors Affecting ◽  
Two Factors

As a crucial part of producer services, the logistics industry is highly dependent on the manufacturing industry. In general, the interactive development of the logistics and manufacturing industries is essential. Due to the existence of a certain degree of interdependence between any two factors, interaction between the two industries has produced a basis for measurement; identifying the key factors affecting the interaction between the manufacturing and logistics industries is a kind of decision problem in the field of multiple criteria decision making (MCDM). A hybrid MCDM method, DEMATEL-based ANP (DANP) is appropriate to solve this problem. However, DANP uses a direct influence matrix, which involves pairwise comparisons that may be more or less influenced by the respondents. Therefore, we propose a decision model, Grey DANP, which can automatically generate the direct influence matrix. Statistical data for the logistics and manufacturing industries in the China Statistical Yearbook (2006–2015) were used to identify the key factors for interaction between these two industries. The results showed that the key logistics criteria for interaction development are the total number of employees in the transport business, the volume of goods, and the total length of routes. The key manufacturing criteria for interaction development are the gross domestic product and the value added. Therefore, stakeholders should increase the number of employees in the transport industry and freight volumes. Also, the investment in infrastructure should be increased.

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