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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. [ABSTRACT FROM AUTHOR]

Industrial energy conservation and CO 2 emission reduction (ECCER) management is a multi-objective optimization problem with multiple uncertainty factors. However, most studies have used deterministic optimization approaches, and neglected the uncertainty factors that affect the effectiveness of the management strategies. This study adopts a multi-objective optimization model under uncertainty to solve the ECCER management problem in China's iron and steel industry. Three objectives: minimum energy intensity, maximum CO 2 emission reduction, and minimum cost, are optimized simultaneously. This study simulates the perturbation of the uncertainty parameters within their fluctuation ranges via Latin Hypercube Sampling, adopts the mean objective function value mechanism to calculate the objective value, and obtains the optimized results using the second generation of the Non-dominated Sorting Genetic Algorithm (NSGA-II). Lastly, this study sets three types of preferences to generate final decision strategies via a Vague set-based approach. Results show: (1) The algorithm is reliable as per the verification of Hypervolume indicator and Spacing Metric; (2) The average values of energy intensity and CO 2 emission reduction amount in optimal solutions are 524.00 kgce and 125.03 kg per ton steel respectively, which are 6.3% and 7.6% lower than the deterministic optimal ones; (3) The decision strategies encourage the wider application of large-sized process equipment, identify 8-9 advanced technology and eight reutilization approaches as key measures, but find the use of renewable energy will still be in low level. This study aims to solve the industrial ECCER optimization problem under uncertainty, and put forward policy suggestions in sustainable manufacturing in this industry. • Energy conservation, CO 2 emission reduction, and cost control are set as objectives. • Random sampling is used to simulate the fluctuation of uncertain parameters. • This study adopts NSGA-II to search for optimal solutions. • Final decision strategies are generated via a Vague set based approach. [ABSTRACT FROM AUTHOR]