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Human resource development (HRD) research and practice mostly have focused on performance improvement although HRD fundamentally pursues human development as a whole. The purpose of this study is to conceptualize meaningful work in the context of HRD and provide implications for HRD research and practice.

This study reviewed the literature on topics such as meaningful work, the meaning of work, workplace spirituality, the value of work and work as a calling, to understand the concept of meaningful work. In addition, this study reviewed existing studies on meaningful work in HRD journals to investigate the current status of meaningful work research within the field of HRD. This study reviewed the related literature such as meaningful work, the meaning of work, workplace spirituality, the value of work and work as a calling, to understand the concept of meaningful work. In addition, this study reviewed the existing studies on meaningful work in HRD journals to investigate the current status of meaningful work research in HRD.

The findings of this study identified three main themes in conceptualizing meaningful work, namely, positivity; significance and purpose; and human fulfillment. The authors also suggest that the meaningful work discourse in HRD expands a research boundary of HRD and enables a holistic approach to HRD research and practice.

For future research, the authors recommend that HRD research deepens its understanding of meaningful work and its related concepts. They also recommend studies pursuing empirical evidence to reveal the significance of meaningful work.

Given the limited studies on meaningful work in HRD and a lack of understanding of meaningful work, this study proposes a comprehensive understanding of meaningful work, especially within the HRD context. This study also suggests a holistic approach to HRD by stressing a humanistic perspective beyond the performance-oriented HRD.

This study identifies whether the Internet search index can be used as effective enough data to identify agricultural and livestock product demand and compare the accuracy of the prediction of major agricultural and livestock products purchases between these prediction models using artificial neural network, linear regression and a decision tree.

Artificial neural network, linear regression and decision tree algorithms were used in this study to compare the accuracy of the prediction of major agricultural and livestock products purchases. The analysis data were studied using 10-fold cross validation.

First, the importance of the Internet search index among the 20 explanatory variables was found to be high for most items, so the Internet search index can be used as a variable to explain agricultural and livestock products purchases. Second, as a result of comparing the accuracy of the prediction of six agricultural and livestock purchases using three models, beef was the most predictable, followed by radishes, chicken, Chinese cabbage, garlic and dried peppers, and by model, a decision tree shows the highest accuracy of prediction, followed by linear regression and an artificial neural network.

This study is meaningful in that it analyzes the purchase of agricultural and livestock products using data from actual consumers' purchases of agricultural and livestock products. In addition, the use of data mining techniques and Internet search index in the analysis of agricultural and livestock purchases contributes to improving the accuracy and efficiency of agricultural and livestock purchase predictions.

Open innovation communities are a growing trend across diverse industries because they provide opportunities of collaborating with customers and exploiting their knowledge effectively. Although open innovation communities can be strategic assets that can help firms innovate, firms nonetheless face the challenge of information overload incurred due to the characteristic of the community. The purpose of this paper is to mitigate the problem of information overload in an open innovation environment.

This study chose MyStarbucksIdea.com (MSI) as a target open innovation community in which customers share their ideas. The authors analyzed a large data set collected from MSI utilizing text mining techniques including TF-IDF and sentiment analysis, while considering both term and non-term features of the data set. Those features were used to develop classification models to calculate the adoption probability of each idea.

The results showed that term and non-term features play important roles in predicting the adoptability of ideas and the best classification accuracy was achieved by the hybrid classification models. In most cases, the precisions of classification models decreased as the number of recommendations increased, while the models’ recalls and F1s increased.

This research dealt with the problem of information overload in an open innovation context. A large amount of customer opinions from an innovation community were examined and a recommendation system to mitigate the problem was proposed. Using the proposed system, the firm can get recommendations for ideas that could be valuable for its business innovation in the idea generation phase, thereby resolving the information overload and enhancing the effectiveness of open innovation.