artificial intelligence blockchain building energy management building information modelling data analytics data mining energy efficiency Energy efficient building energy management energy performance indicator flexibility measurement industry 4.0 industry 4.0 maturity assessment Internet of Things knowledge management linked data machine learning manufacturing Ontology ontology engineering ontology population product configuration product lifecycle management production planning and control requirement engineering resource efficient manufacturing smart cities Supply Chain 4.0 sustainability virtual engineering
2023 |
Sukoco Badri Munir; Putra, Rizky Ananda.; Muqaffi Humam Nur; Lutfian Muhammad Vinka; Wicaksono Hendro Comparative Study of ASEAN Research Productivity Journal Article SAGE Open, 13 (1), pp. 21582440221145157, 2023. Abstract | Links | BibTeX | Tags: data analytics, research @article{Sukoco2023, title = {Comparative Study of ASEAN Research Productivity}, author = {Sukoco, Badri Munir; Putra, Rizky Ananda.; Muqaffi, Humam Nur; Lutfian, Muhammad Vinka; Wicaksono, Hendro}, url = {https://journals.sagepub.com/doi/full/10.1177/21582440221145157}, doi = { https://doi.org/10.1177/21582440221145157}, year = {2023}, date = {2023-01-03}, journal = {SAGE Open}, volume = {13}, number = {1}, pages = {21582440221145157}, abstract = {Research productivity has become one of the main indicators used by higher education institutions (HEIs) as well as the country to support their innovation capability. This study purposely describes the research productivity among ASEAN countries, which is considered to be the world’s current economic hotspot. By using SciVal database to examine the literature over the last 10 years, we describe productivity, citation impact, and economic impact metrics. The findings indicate that Singapore is superior in terms of publication quality (citation) and patents while Malaysia is leading in terms of the number of scientific research. Interestingly, Indonesia’s scientific publication growth has the highest percentage. Furthermore, Engineering & Technology and Life Sciences & Medicine are the two major contributors to ASEAN research productivity. These subjects could be the major locomotives for ASEAN countries to use to sustain their competitiveness if the leaders can transform it into successful commercialization.}, keywords = {data analytics, research}, pubstate = {published}, tppubtype = {article} } Research productivity has become one of the main indicators used by higher education institutions (HEIs) as well as the country to support their innovation capability. This study purposely describes the research productivity among ASEAN countries, which is considered to be the world’s current economic hotspot. By using SciVal database to examine the literature over the last 10 years, we describe productivity, citation impact, and economic impact metrics. The findings indicate that Singapore is superior in terms of publication quality (citation) and patents while Malaysia is leading in terms of the number of scientific research. Interestingly, Indonesia’s scientific publication growth has the highest percentage. Furthermore, Engineering & Technology and Life Sciences & Medicine are the two major contributors to ASEAN research productivity. These subjects could be the major locomotives for ASEAN countries to use to sustain their competitiveness if the leaders can transform it into successful commercialization. |
2021 |
Ahmadi Elham; Fatahi Valilai, Omid; Wicaksono Hendro Extending the Last Mile Delivery Routing Problem for Enhancing Sustainability by Drones Using a Sentiment Analysis Approach Inproceedings 2021. BibTeX | Tags: data analytics, machine learning, sentiment analysis, sustainability @inproceedings{Ahmadi2020, title = {Extending the Last Mile Delivery Routing Problem for Enhancing Sustainability by Drones Using a Sentiment Analysis Approach}, author = {Ahmadi, Elham; Fatahi Valilai, Omid; Wicaksono, Hendro}, year = {2021}, date = {2021-12-14}, keywords = {data analytics, machine learning, sentiment analysis, sustainability}, pubstate = {published}, tppubtype = {inproceedings} } |
Farooq Yousuf; Wicaksono, Hendro Advancing on the analysis of causes and consequences of green skepticism Journal Article Journal of Cleaner Production, 320 , pp. 128927, 2021. Abstract | Links | BibTeX | Tags: data analytics, green skepticism, structural equation modelling, sustainability @article{Farooq2020, title = {Advancing on the analysis of causes and consequences of green skepticism}, author = {Farooq, Yousuf; Wicaksono, Hendro}, doi = {https://doi.org/10.1016/j.jclepro.2021.128927}, year = {2021}, date = {2021-10-20}, journal = {Journal of Cleaner Production}, volume = {320}, pages = {128927}, abstract = {With the increasing trend toward sustainable purchasing, companies invest vast sums of money advertising their sustainability. Yet there are also companies doing the exact opposite for fear of consumer skepticism toward sustainability claims. Consumer skepticism can have adverse effects on company image and performance. Therefore, for the success of a company's sustainability campaign, it is essential that they are familiar with the factors resulting in consumer skepticism. This research has investigated these factors. Through a survey-based approach and analysis using structural equation modeling, it has been found that a main cause of consumer skepticism is previous incidents of greenwashing. Furthermore, consumers are more skeptical of large companies than smaller companies. The research also indicates that consumer skepticism towards a company is industry-specific, with the oil industry being the least trusted. The effect of demographics was also studied, finding that women are more skeptical. Contrary to previous literature, collectivist cultures were found to be more skeptical than individualistic cultures. This research has also explored consumer perspectives towards silent sustainability, finding that highly skeptical consumers prefer companies to limit their sustainability advertisements. Companies silent about their sustainability invoke less consumer skepticism than those advertising sustainability. This research has filled major research gaps in the field of consumer skepticism and silent sustainability and carries important implications for companies advertising in today's market, as well as for policy makers.}, keywords = {data analytics, green skepticism, structural equation modelling, sustainability}, pubstate = {published}, tppubtype = {article} } With the increasing trend toward sustainable purchasing, companies invest vast sums of money advertising their sustainability. Yet there are also companies doing the exact opposite for fear of consumer skepticism toward sustainability claims. Consumer skepticism can have adverse effects on company image and performance. Therefore, for the success of a company's sustainability campaign, it is essential that they are familiar with the factors resulting in consumer skepticism. This research has investigated these factors. Through a survey-based approach and analysis using structural equation modeling, it has been found that a main cause of consumer skepticism is previous incidents of greenwashing. Furthermore, consumers are more skeptical of large companies than smaller companies. The research also indicates that consumer skepticism towards a company is industry-specific, with the oil industry being the least trusted. The effect of demographics was also studied, finding that women are more skeptical. Contrary to previous literature, collectivist cultures were found to be more skeptical than individualistic cultures. This research has also explored consumer perspectives towards silent sustainability, finding that highly skeptical consumers prefer companies to limit their sustainability advertisements. Companies silent about their sustainability invoke less consumer skepticism than those advertising sustainability. This research has filled major research gaps in the field of consumer skepticism and silent sustainability and carries important implications for companies advertising in today's market, as well as for policy makers. |
Wicaksono, Hendro Accelerating Energy Transition to Green Electricity through Artificial Intelligence Presentation 24.08.2021. Abstract | Links | BibTeX | Tags: artificial intelligence, data analytics, energy transition, machine learning @misc{Wicaksono2021c, title = {Accelerating Energy Transition to Green Electricity through Artificial Intelligence}, author = {Wicaksono, Hendro }, doi = {10.31219/osf.io/tcrkh}, year = {2021}, date = {2021-08-24}, abstract = {The presentation focuses on the role of artificial intelligence in accelerating the transition to green electricity in Germany. It discusses the challenges in the transition towards green electricity in Germany and the role of digitalization through smart metering. One of the methods to adopt and disseminate the use of green electricity is demand response. The presentation explains the definition of demand response concept and gives an example of projects that applies neural network to forecast power generation and consumption to enable calculation of dynamic electricity price. Finally, the presentation explores the adoption of green electricity in broader contexts, e.g., cities and districts, through a data-driven smart energy platform.}, keywords = {artificial intelligence, data analytics, energy transition, machine learning}, pubstate = {published}, tppubtype = {presentation} } The presentation focuses on the role of artificial intelligence in accelerating the transition to green electricity in Germany. It discusses the challenges in the transition towards green electricity in Germany and the role of digitalization through smart metering. One of the methods to adopt and disseminate the use of green electricity is demand response. The presentation explains the definition of demand response concept and gives an example of projects that applies neural network to forecast power generation and consumption to enable calculation of dynamic electricity price. Finally, the presentation explores the adoption of green electricity in broader contexts, e.g., cities and districts, through a data-driven smart energy platform. |