Jianguo Yao, Antonio Crupi, Alberto Di Minin and Xumei Zhang
This paper aims to investigate how knowledge sharing influences technological innovation capability (TIC) of the software small- and medium-sized enterprises (SSMEs).
Abstract
Purpose
This paper aims to investigate how knowledge sharing influences technological innovation capability (TIC) of the software small- and medium-sized enterprises (SSMEs).
Design/methodology/approach
Based on the theories regarding knowledge management, TIC, software engineering and open innovation, this paper constructed a research model comprising factors affecting knowledge sharing, knowledge sharing and TIC, and then tested the model quantitatively. The study focuses on SSMEs in China collecting 457 online questionnaires and uses a structural equation model to test the hypotheses.
Findings
The knowledge sharing culture, organizational structure, middle-level leadership and management system have significantly positive effects on tacit knowledge sharing; management system and IT support have significantly positive effects on explicit knowledge sharing; both explicit and tacit knowledge sharing have significantly positive effects on TIC.
Research limitations/implications
The study enriches the research on knowledge sharing and TIC. However, it does not consider factors affecting knowledge sharing at the non-organizational level or the interaction between explicit and tacit knowledge sharing.
Practical implications
The study offers several recommendations/suggestions for helping SSMEs to promote and implement explicit or tacit knowledge sharing and TIC.
Originality/value
This paper examines the impact of knowledge sharing on TIC from the perspective of knowledge management deconstructing knowledge sharing from the epistemological dimension and the TIC of software companies on the basis of software engineering theory. It provided a new theoretical perspective for the research of knowledge management and technological innovation management in SSMEs.
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Patricia A. Maguire and Muzaffer Uysal
With the end of the Cultural Revolution and the rise of Deng Xiaoping, China began a new era of economic and political reform. In 1978 the open door policy was initiated. In…
Abstract
With the end of the Cultural Revolution and the rise of Deng Xiaoping, China began a new era of economic and political reform. In 1978 the open door policy was initiated. In October of 1984, Deng Xiaoping set in motion an ambitious program of financial and industrial reform aimed at eventually restructuring China's economy into a vaguely defined market system. This “second revolution” has run into difficulties because the Chinese officials lacked experience controlling a supply and demand economy and because of the opposition from conservative factions within the Chinese bureaucracy.
Nooshin Karimi Alavijeh and Samane Zangoei
Expansion of the consumption of renewable energy is a significant issue for reducing global warming, to cope with climate change and achieve sustainable development. This study…
Abstract
Purpose
Expansion of the consumption of renewable energy is a significant issue for reducing global warming, to cope with climate change and achieve sustainable development. This study aims to examine how research and development expenditure (R&D) affects renewable energy development in developed G-7 countries over the period from 2000 to 2019. Variables of trade liberalization and CO2 emissions are considered control variables.
Design/methodology/approach
This study has adopted a panel quantile regression. The impact of the variables on renewable development has been examined in quantiles of 0.1, 0.25, 0.5, 0.75 and 0.9. Also, a robust examination is accomplished by applying generalized quantile regression (GQR).
Findings
The empirical findings reveal a positive and significant relationship between R&D and the consumption of renewable energy in 0.1, 0.25, 0.5 and 0.75 quantiles. Also, the findings describe that the expansion of trade liberalization and CO2 emissions can significantly increase the development of renewable energy in G-7 countries. Furthermore, GQR verifies the main outcomes.
Practical implications
These results have very momentous policy consequences for the governments of G-7 countries. Therefore, investment and support for the R&D section to promote the development of renewable energy are recommended.
Originality/value
This paper, in comparison to other research, used panel quantile regression to investigate the impact of factors affecting renewable energy consumption. Also, to the best of the authors’ knowledge, no study has perused the effect of R&D along with trade liberalization and carbon emissions on renewable energy consumption in G-7 countries. Also, in this paper, as a robustness check for panel quantile regression, the GQR has been used.
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Shouhui Wang, Jianguo Dai, Qingzhan Zhao and Meina Cui
Many factors affect the emergence and development of crop diseases and insect pests. Traditional methods for investigating this subject are often difficult to employ and produce…
Abstract
Purpose
Many factors affect the emergence and development of crop diseases and insect pests. Traditional methods for investigating this subject are often difficult to employ and produce limited data with considerable uncertainty. The purpose of this paper is to predict the annual degree of cotton spider mite infestations by employing grey theory.
Design/methodology/approach
The authors established a GM(1,1) model to forecast mite infestation degree based on the analysis of historical data. To improve the prediction accuracy, the authors modified the grey model using Markov chain and BP neural network analyses. The prediction accuracy of the GM(1,1), Grey-Markov chain, and Grey-BP neural network models was 84.31, 94.76, and 96.84 per cent, respectively.
Findings
Compared with the single grey forecast model, both the Grey-Markov chain model and the Grey-BP neural network model had higher forecast accuracy, and the accuracy of the latter was highest. The improved grey model can be used to predict the degree of cotton spider mite infestations with high accuracy and overcomes the shortcomings of traditional forecasting methods.
Practical implications
The two new models were used to estimate mite infestation degree in 2015 and 2016. The Grey-Markov chain model yielded respective values of 1.27 and 1.15, whereas the Grey-BP neural network model yielded values 1.4 and 1.68; the actual values were 1.5 and 1.8.
Originality/value
The improved grey model can be used for medium- and long-term predictions of the occurrence of cotton spider mites and overcomes problems caused by data singularity and fluctuation. This research method can provide a reference for the prediction of similar diseases.
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Jennifer Nabaweesi, Twaha Kaawaase Kigongo, Faisal Buyinza, Muyiwa S. Adaramola, Sheila Namagembe and Isaac Nabeta Nkote
The study aims to explore the validity of the modern renewable energy-environmental Kuznets curve (REKC) while considering the relevance of financial development in the…
Abstract
Purpose
The study aims to explore the validity of the modern renewable energy-environmental Kuznets curve (REKC) while considering the relevance of financial development in the consumption of modern renewable energy in East Africa Community (EAC). Modern renewable energy in this study includes all other forms of renewable energy except traditional use of biomass. The authors controlled for the effects of urbanization, governance, foreign direct investment (FDI) and trade openness.
Design/methodology/approach
Panel data of the five EAC countries of Burundi, Kenya, Rwanda, Tanzania and Uganda for the period 1996–2019 were used. The analysis relied on the use of the autoregressive distributed lag–pooled mean group (ARDL-PMG) model, and the data were sourced from the World Development Indicators (WDI), World Governance Indicators (WGI) and International Energy Agency (IEA).
Findings
The REKC hypothesis is supported for modern renewable energy consumption in the EAC region. Financial development positively and significantly affects modern renewable energy consumption, whereas urbanization, FDI and trade openness reduce modern renewable energy consumption. Governance is insignificant.
Originality/value
The concept of the REKC, although explored in other contexts such as aggregate renewable energy and in other regions, has not been used to explain the consumption of modern renewable energy in the EAC.
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Muhammad Aamir Shafique Khan, Du Jianguo, Shuai Jin, Munazza Saeed and Adeel Khalid
Using the conservation of resources (COR) theory, the present study aims to examine the role of participative leadership in frontline service employees (FLEs)’ service recovery…
Abstract
Purpose
Using the conservation of resources (COR) theory, the present study aims to examine the role of participative leadership in frontline service employees (FLEs)’ service recovery performance. The present study also tests FLEs’ role breadth self-efficacy (RBSE) as a theoretically relevant mediator and FLE trait mindfulness as an important moderator.
Design/methodology/approach
Data were collected using time-lagged (three rounds, two weeks apart) from two sources (193 FLEs and 772 customers, who experienced a service failure). Structural equation modeling (Mplus, 8.6) was employed to analyze the data.
Findings
The results revealed that participative leadership was positively associated with FLEs service recovery performance, both directly and indirectly, via RBSE. The results also showed that FLE trait mindfulness moderated the link of participative leadership with RBSE and the indirect association of participative leadership with service recovery performance, via RBSE.
Practical implications
This study suggests that organizational leaders who exhibit participative leadership behavior are valuable for organizations. By demonstrating such behaviors, they boost FLEs' RBSE, which in turn improves their service recovery performance.
Originality/value
The present work makes important contributions to the literature on service recovery performance by foregrounding two important yet overlooked antecedents (participative leadership and RBSE) of FLE service recovery performance. The present work also contributes to the nascent literature on the antecedents and outcomes of RBSE in service contexts.
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Mohamed Ismail Mohamed Riyath, Narayanage Jayantha Dewasiri, Mohamed Abdul Majeed Mohamed Siraju, Athambawa Jahfer and Kiran Sood
Purpose: This study investigates internal/own shock in the domestic market and three external volatility spillovers from India, the UK, and the USA to the Sri Lanka stock market…
Abstract
Purpose: This study investigates internal/own shock in the domestic market and three external volatility spillovers from India, the UK, and the USA to the Sri Lanka stock market.
Need for the Study: The external market’s internal/own shocks and volatility spillovers influence portfolio choices in domestic stock market returns. Hence, it is required to investigate the internal shock in the domestic market and the external volatility spillovers from other countries.
Methodology: This study employs a quantitative method using ARMA(1,1)-GARCH(1,1) model. All Share Price Index (ASPI) is the proxy for the Colombo Stock Exchange (CSE) stock return. It uses daily time-series data from 1st April 2010 to 21st June 2023.
Findings: The findings revealed that internal/own and external shocks substantially impact the stock price volatility in CSE. Significant volatility clusters and persistence with extended memory in ASPI confirm internal/own shock in the market. Furthermore, CSE receives significant volatility shock from the USA, confirming external shock. This study’s findings highlight the importance of considering internal and external shocks in portfolio decision-making.
Practical Implications: Understanding the influence of internal shocks helps investors manage their portfolios and adapt to market volatility. Recognising significant volatility spillovers from external markets, especially the USA, informs diversification strategies. From a policy standpoint, the study emphasises the need for robust regulations and risk management measures to address shocks in domestic and global markets. This study adds value to the literature by assessing the sources of volatility shocks in the CSE, employing the ARMA-GARCH, a sophisticated econometrics model, to capture stock returns volatility, enhancing understanding of the CSE’s volatility dynamics.
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Bo Chen, Zheng Meng, Kai Yang, Yongzhen Yao, Caiwang Tan and Xiaoguo Song
The purpose of this paper is to predict and control the composition during laser additive manufacturing, since composition control is important for parts manufactured by laser…
Abstract
Purpose
The purpose of this paper is to predict and control the composition during laser additive manufacturing, since composition control is important for parts manufactured by laser additive manufacturing. Aluminum and steel functionally graded material (FGM) were manufactured by laser metal deposition, and the composition was analyzed by means of spectral analysis simultaneously.
Design/methodology/approach
The laser metal deposition process was carried out on a 5 mm thick 316L plate. Spectral line intensity ratio and plasma temperature were chosen as two main spectroscopic diagnosis parameters to predict the compositional variation. Single-trace single-layer experiments and single-trace multi-layer experiments were done, respectively, to test the feasibility of the spectral diagnosis method.
Findings
Experiment results showed that with the composition of metal powder changing from steel to aluminum, the spectral intensity ratio of the characteristic spectral line is proportional to the elemental content in the plasma. When the composition of deposition layers changed, the characteristic spectrum line intensity ratio changed obviously. And the linear chemical composition analysis results confirmed the gradient composition variation of the additive manufacturing parts. The results verified the feasibility of composition analysis based on spectral information in the laser additive manufacturing process.
Originality/value
The composition content of aluminum and steel FGM was diagnosed by spectral information during laser metal deposition, and the relationship between spectral intensity and composition was established.
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R.M. Kapila Tharanga Rathnayaka, D.M.K.N Seneviratna and Wei Jianguo
Because of the high volatility with unstable data patterns in the real world, the ability of forecasting price indices is notoriously embarrassing and represents a major challenge…
Abstract
Purpose
Because of the high volatility with unstable data patterns in the real world, the ability of forecasting price indices is notoriously embarrassing and represents a major challenge with traditional time series mechanisms; especially, most of the traditional approaches are weak to forecast future predictions in the high volatile and unbalanced frameworks under the global and local financial depressions. The purpose of this paper is to propose a new statistical approach for portfolio selection and stock market forecasting to assist investors as well as stock brokers to predict the future behaviors.
Design/methodology/approach
This study mainly takes an attempt to understand the trends, behavioral patterns and predict the future estimations under the new proposed frame for the Colombo Stock Exchange (CSE), Sri Lanka. The methodology of this study is carried out under the two main phases. In the first phase, constructed a new portfolio mechanism based on k-means clustering. In the second stage, proposed a nonlinear forecasting methodology based on grey mechanism for forecasting stock market indices under the high-volatile fluctuations. The autoregressive integrated moving average (ARIMA) predictions are used as comparison mode.
Findings
Initially, the k-mean clustering was applied to pick out the profitable sectors running under the CSE and results indicated that BFI is more significant than other 20 sectors. Second, the MAE, MAPE and MAD model comparison results clearly suggested that, the newly proposed nonlinear grey Bernoulli model (NGBM) is more appropriate than traditional ARIMA methods to forecast stock price indices under the non-stationary market conditions.
Practical implications
Because of the flexible nonlinear modeling capability, proposed novel concepts are more suitable for applying in various areas in the field of financial, economic, military, geological and agricultural systems for pattern recognition, classification, time series forecasting, etc.
Originality/value
For the large sample of data forecasting under the normality assumptions, the traditional time series methodologies are more suitable than grey methodologies. However, the NGBM is better both in model building and ex post testing stagers under the s-distributed data patterns with limited data forecastings.