Knowledge management technologies and organizational performance: a meta-analytic study

Abstract

PurposeThis meta-analytic study tries to synthesize the mixed relationships between knowledge management technologies (KMT) and organizational performance as well as aims to explore the impacts of contextual elements, such as national culture, economy and industries, on these relationships.Design/methodology/approachFindings on various subjects from 40 previous empirical studies were examined using meta-analysis.FindingsIt was found that KMT are positively related to overall organizational performance as well as financial and nonfinancial performance and that the relationship between KMT and financial performance is stronger in developing economies than in developed economies.Practical implicationsIt helps practitioners better understand the role of KMT in organizational performance in various contexts and provides practical suggestions for KMT implementation.Originality/valueAs the first meta-analytic study to address the generalizability of KMT–organizational performance relationships, this paper offers an improved understanding of the benefits of KMT. It also expands knowledge about how contextual issues related to national culture, economies and industries affect KMT payoffs.

Soft computing in business: exploring current research and outlining future research directions

“…Topic 6- “Nature-Inspired Algorithms for Strategic Intelligence” represents novel problem-solving and optimization methodologies that are illumed by natural phenomena and processes. These methods include evolutionary algorithms (Biswas et al ., 2022), particle swarm optimization (Liu et al ., 2022a, b, c, d, e; Zhang et al ., 2022c), ant colony algorithm (Yu et al ., 2022), spider monkey optimization (Zhu et al ., 2022), white shark optimization (Braik et al ., 2022), firefly algorithm (Thang and Binh, 2022), artificial bee colony algorithm (Ye et al ., 2022) and many other. Nature-inspired algorithms are mostly used for solving complex business problems related to multi-dimensional and multi-modal resource optimization (Chen et al ., 2022c; Hou et al ., 2022).…”

“…Topic 3- “Deep Learning Models for Detection and Diagnosis” deals with the application of deep learning models in various business contexts, such as fault diagnosis under operating uncertainties (Li et al ., 2022a, b, c; Liu et al ., 2022a, b, c, d, e; Wang et al ., 2022a, b, c, d), detecting intrusions in cloud environments (Chen et al ., 2022a; Geetha and Deepa, 2022), discovering structural information like communities of users and objects in complex networks (Cai et al ., 2022) and detecting health problems from medical records such as ECG images (Murat et al ., 2021) and chest X-ray images (Muhammad et al ., 2022). Moreover, contemporary works also focus on object identification, such as road detection by a running autonomous vehicle Ahn et al.…”

“…Moreover, contemporary works also focus on object identification, such as road detection by a running autonomous vehicle Ahn et al. (2022) and object detection for a running train (Liu et al ., 2022a, b, c, d, e).…”

“…The transition to Industry 4.0 requires the transformation of business processes, including logistics and supply chain (Mastrocinque et al ., 2022; Shang et al ., 2022). Soft computing techniques are found to be efficient in the selection and evaluation of sustainable circular suppliers (Liu et al ., 2022a, b, c, d, e), sustainable logistics providers (Chen et al ., 2022b) and sustainable partner selection (Wu et al ., 2022a, b, c, d). Moreover, literature has confirmed that AI-based decision-making techniques are found to be the most promising techniques for developing resilient supply chains (Belhadi et al ., 2022) and improving overall operational performance (Al-Surmi et al ., 2022).…”

“…Contemporary research encourages the use of TM techniques, such as structured topic models (STMs), to map the conceptual structure of published research (Sharma et al ., 2021, 2022a, b; Liu et al ., 2022a, b, c, d, e; Das et al ., 2023; Singh et al ., 2023). STM highlight predominant latent topics from the research corpus and model the evolution of trends over time by including year of publication as a metadata variable in the modelling process.…”