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The present research proposes the hierarchical linear modeling model (HLM) that describe how green social responsibility (GSR) predict the environmental strategy (ES) of agricultural technology manufacturing companies by the intermediary effects of the supervisor’s green promise (GP) based on symbolic context theory. This study collected data with 150 supervisors from 50 different agricultural technology companies in Taiwan to analyze the HLM. The results suggest that vendors of agricultural technology companies should establish GSR to increase GP, which consequently can increase the companies’ adoption of the ES. It is now the first to establish a milestone, propose a novel adoption model—GP and its antecedents through the HLM to predict the adoption of ES. These findings can upgrade the related literature of agriculture and can provide the procedure in implementing ES in agricultural technology companies.
Stanley Y. B. Huang; Shih-Chin Lee; Yue-Shi Lee. Why Can Green Social Responsibility Drive Agricultural Technology Manufacturing Company to Do Good Things? A Novel Adoption Model of Environmental Strategy. Agronomy 2021, 11, 1673 .
AMA StyleStanley Y. B. Huang, Shih-Chin Lee, Yue-Shi Lee. Why Can Green Social Responsibility Drive Agricultural Technology Manufacturing Company to Do Good Things? A Novel Adoption Model of Environmental Strategy. Agronomy. 2021; 11 (8):1673.
Chicago/Turabian StyleStanley Y. B. Huang; Shih-Chin Lee; Yue-Shi Lee. 2021. "Why Can Green Social Responsibility Drive Agricultural Technology Manufacturing Company to Do Good Things? A Novel Adoption Model of Environmental Strategy." Agronomy 11, no. 8: 1673.
To fill in the literature flaws that have not been detected in previous studies, this research, therefore, examines the driving factors of proactive environmental strategy (PES). First, this research proposes how corporate social responsibility (CSR) predicts the agricultural company’s PES through the intermediary mechanism of green organization identification (GOI) of the top management team (TMT) according to symbolic context and theory of high-level echelon, to solve the first gap in exploring what factors can drive the PES. Second, this research proposes a multi-level growth curve model (MGCM) to solve how individuals adjust their behavioral intentions over time according to their translation and understanding of their use environment, because past studies consist of almost cross-sectional properties. Third, past research has also neglected the multi-level framework, leading to hierarchical reasoning bias. Therefore, this research believes that the MGCM can fill in the multi-level gap. Finally, this research collected 400 TMT employees from 100 different agricultural companies in Taiwan in three-stage time for six months. The results show that CSR will significantly lead to more growth in GOI, and more growth in GOI will lead to more growth in PES adoption. The research results can not only advance the agricultural sustainability literature but also serve as a guide for agricultural companies to implement PES.
Stanley Y. B. Huang; Shih-Chin Lee; Yue-Shi Lee. Constructing an Adoption Model of Proactive Environmental Strategy: A Novel Quantitative Method of the Multi-Level Growth Curve Model. Mathematics 2021, 9, 1962 .
AMA StyleStanley Y. B. Huang, Shih-Chin Lee, Yue-Shi Lee. Constructing an Adoption Model of Proactive Environmental Strategy: A Novel Quantitative Method of the Multi-Level Growth Curve Model. Mathematics. 2021; 9 (16):1962.
Chicago/Turabian StyleStanley Y. B. Huang; Shih-Chin Lee; Yue-Shi Lee. 2021. "Constructing an Adoption Model of Proactive Environmental Strategy: A Novel Quantitative Method of the Multi-Level Growth Curve Model." Mathematics 9, no. 16: 1962.
Job burnout is a continuing concern for human resource management and mental health at work, as it affects employee productivity and well-being. The present study conceptualizes Kahn’s job engagement theory to predict job burnout through a latent growth model. To test the proposed model, data were collected by surveying 710 employees of R&D departments of financial information technology firms of Taiwan at multiple points in time over 6 months. Therein, this study found that as employees perceived more ethical leadership, corporate social responsibility, and self-efficacy at Time 1, they were more likely to show increases in job engagement development behavior over time. Further, increases in job engagement development behavior demonstrate their positive relationship with the decrease in job burnout development behavior over time. These findings highlight that the potential dynamic consequences of organizational behaviors can lead to employee career development and occupational mental health.
Stanley Huang; Yu-Ming Fei; Yue-Shi Lee. Predicting Job Burnout and Its Antecedents: Evidence from Financial Information Technology Firms. Sustainability 2021, 13, 4680 .
AMA StyleStanley Huang, Yu-Ming Fei, Yue-Shi Lee. Predicting Job Burnout and Its Antecedents: Evidence from Financial Information Technology Firms. Sustainability. 2021; 13 (9):4680.
Chicago/Turabian StyleStanley Huang; Yu-Ming Fei; Yue-Shi Lee. 2021. "Predicting Job Burnout and Its Antecedents: Evidence from Financial Information Technology Firms." Sustainability 13, no. 9: 4680.
Mining sequential patterns is to find the sequential purchasing behaviors for most of the customers in a transaction database. By using sequential patterns, it is possible to predict which products will be purchased in the future after the customer purchases certain commodities. Nowadays, transaction data is continuously added to the database. It is an important issue to update the sequence pattern efficiently in this environment. The previous efficient approach is to store the transactions in a tree structure. When the transactions were added, the tree structure could be updated according to the newly added items. It still needs to re-find the sequential patterns from the updated tree structure and re-scan the original transactions, without using the previous patterns. Therefore, we propose two algorithms for mining and maintaining the discovered sequential patterns when the transactions are added into the database. Our algorithms do not need to re-scan the original transactions and re-generate the existing sequential patterns, which just need to process the added transactions to update the existing sequential patterns. The experimental results also show that our algorithms outperforms the previous approaches.
Show-Jane Yen; Yue-Shi Lee. Efficient Approaches for Updating Sequential Patterns. LATIN 2016: Theoretical Informatics 2020, 553 -564.
AMA StyleShow-Jane Yen, Yue-Shi Lee. Efficient Approaches for Updating Sequential Patterns. LATIN 2016: Theoretical Informatics. 2020; ():553-564.
Chicago/Turabian StyleShow-Jane Yen; Yue-Shi Lee. 2020. "Efficient Approaches for Updating Sequential Patterns." LATIN 2016: Theoretical Informatics , no. : 553-564.
Mining frequent itemset generates frequently purchased itemsets. However, a frequent itemset may not be the itemset with high value. Mining high utility itemset considers both of the profits and purchased quantities for the items, which is to find the itemsets with high utility for the business. However, mining high utility itemsets from the whole database may lose the high utility itemsets in a specific time period. Mining high utility itemsets in different time periods considers when the itemsets will be high utility, which can provide business manager to promote high utility itemsets in a specific time period. In this paper, we propose an approach for mining high utility itemsets in different time periods. We first partition the database according to the user-specified basic time periods, and then combine the transactions in the continuous basic periods to find high utility itemsets in the continuous periods. Besides, we remain the itemsets which are not high utility in the previous periods, but may be high utility after combining the previous periods with the current basic period, such that the high utility itemsets in continuous basic periods will not be lost.
Show-Jane Yen; Yue-Shi Lee. Mining High Utility Patterns in Different Time Periods. Lecture Notes in Electrical Engineering 2015, 779 -789.
AMA StyleShow-Jane Yen, Yue-Shi Lee. Mining High Utility Patterns in Different Time Periods. Lecture Notes in Electrical Engineering. 2015; ():779-789.
Chicago/Turabian StyleShow-Jane Yen; Yue-Shi Lee. 2015. "Mining High Utility Patterns in Different Time Periods." Lecture Notes in Electrical Engineering , no. : 779-789.
Mining frequent patterns is an important task in data mining area, which is to find the itemsets frequently purchased together from a transaction database. However, the transactions will grow rapidly, such that the size of the transaction database becomes bigger and bigger due to the addition of the new transactions. The users may eager for getting the latest frequent patterns from the large database as soon as possible in order to make the best decision. Therefore, it has become an important issue to propose an efficient method for finding the latest frequent patterns when the transactions keep being added into the database. Although tree-based approaches have been recently adopted in most of the studies in this field, they have to re-scan the original database and generate a large tree structure. In this paper, we propose two efficient algorithms which only keep frequent items in a condensed tree structure. When a set of new transactions is added into the database, our algorithms can efficiently update the tree structure without scanning the original database.
Yue-Shi Lee; Show-Jane Yen. Incrementally Mining Frequent Patterns from Large Database. Studies in Big Data 2014, 121 -140.
AMA StyleYue-Shi Lee, Show-Jane Yen. Incrementally Mining Frequent Patterns from Large Database. Studies in Big Data. 2014; ():121-140.
Chicago/Turabian StyleYue-Shi Lee; Show-Jane Yen. 2014. "Incrementally Mining Frequent Patterns from Large Database." Studies in Big Data , no. : 121-140.
The purpose of mining frequent itemsets is to identify the items in groups that always appear together and exceed the user-specified threshold of a transaction database. However, numerous frequent itemsets may exist in a transaction database, hindering decision making. Recently, the mining of frequent closed itemsets has become a major research issue because sets of frequent closed itemsets are condensed yet complete representations of frequent itemsets. Therefore, all frequent itemsets can be derived from a group of frequent closed itemsets. Nonetheless, the number of transactions in a transaction database can increase rapidly in a short time period, and a number of the transactions may be outdated. Thus, frequent closed itemsets may be changed with the addition of new transactions or the deletion of old transactions from the transaction database. Updating previously closed itemsets when transactions are added or removed from the transaction database is challenging. This study proposes an efficient algorithm for incrementally mining frequent closed itemsets without scanning the original database. The proposed algorithm updates closed itemsets by performing several operations on the previously closed itemsets and added/deleted transactions without searching the previously closed itemsets. The experimental results show that the proposed algorithm significantly outperforms previous methods, which require a substantial length of time to search previously closed itemsets.
Show-Jane Yen; Yue-Shi Lee; Chiu-Kuang Wang. An efficient algorithm for incrementally mining frequent closed itemsets. Applied Intelligence 2013, 40, 649 -668.
AMA StyleShow-Jane Yen, Yue-Shi Lee, Chiu-Kuang Wang. An efficient algorithm for incrementally mining frequent closed itemsets. Applied Intelligence. 2013; 40 (4):649-668.
Chicago/Turabian StyleShow-Jane Yen; Yue-Shi Lee; Chiu-Kuang Wang. 2013. "An efficient algorithm for incrementally mining frequent closed itemsets." Applied Intelligence 40, no. 4: 649-668.