Abstract. This research paper conducts a comprehensive analysis of the complex relationship between CO2 emissions and economic factors, specifically investigating the Environmental Kuznets Curve (EKC) theory in Malaysia. Using data from 1991 to 2020, the study applies the Autoregressive Distributed Lag (ARDL) modelling approach developed by Pesaran et al. (2001). The results demonstrate a significant long-term connection between Gross Domestic Product (GDP), trade, and carbon emissions, indicating that economic development plays a crucial role in influencing Malaysia's carbon footprint. Additionally, the inclusion of institutional quality in the model adds another layer of complexity, highlighting the multifaceted nature of the relationship between economic progress and environmental outcomes. Furthermore, examining short-term dynamics using the ARDL model reveals diverse effects over time for variables such as renewable energy and institutional quality, providing a more nuanced understanding of these relationships. These detailed insights are essential for policymakers dealing with the challenges of promoting economic progress while ensuring environmental sustainability. The findings contribute to a deeper understanding of the interplay between economic variables and CO2 emissions, offering valuable guidance for policymakers striving to strike a balance between economic growth and environmental conservation in Malaysia. Keywords: Malaysia, autoregressive distributed lag (ardl) modelling, environmental kuznets curve (ekc), carbon dioxide (co2) emissions, gross domestic product (gdp), renewable energy (ren), institutional quality (iq), trade (tr)

Abstract. Halal entrepreneurship is relevant for national economic growth, with the application of the AAOIFI transaction standard (ATS) having diverse impacts on halal digital entrepreneurship engagement. Despite the significant halal market value, ATS influence on business practices presents significant opportunities and challenges for halal digital entrepreneurship engagement, particularly in Indonesia. This qualitative study investigates the relevance and applicability of ATS in Indonesia’s halal digital entrepreneurship engagements through 13 purposive sampling semi-structured interviews analyzed thematically (open coding and axial coding) using NVIVO software. Findings accompanied by negative cases highlight the practical theme of strategic engagement and application of the ATS, contributing new perspectives to halal entrepreneurship theory and practice in halal digital business that harnesses the ATS. This theoretical implication extends the understanding of halal digital entrepreneurship and conforms to the importance of integrating AAOIFI into the halal ecosystem. Practical implications include leveraging ATS for digital business growth while upholding Islamic values, extending ATS application to the real sector, both globally and digitally, and understanding ATS as crucial for success in halal digital businesses. Keywords: Halal entrepreneurship, entrepreneurship engagement, AAOIFI transaction standard, Indonesia’s halal digital business, thematic analysis

Artificial intelligence has emerged as one of the biggest disruptive technologies of the new era and will continue to evolve in the future. AI takes an important role in marketing processes, and the use of AI in marketing is massively increasing and can be easily found. Beauty industry is one of the industries where most companies develop their AI technology to attract customers. This innovation utilizes AI technology in providing solutions to problems related to makeup and/or skincare as experienced by the customers. This study aims to analyze the relationship between technology readiness, technology acceptance model and subjective norm as potential factors in encouraging the customer to use AI technology in the beauty industry. The data was collected using a google form questionnaire. The sampling technique for this research was purposive random sampling. A total of 155 responses were collated and only 127 responses are qualified with the criteria ‘ever using AI Technology in the beauty industry’. The results showed that while perceived usefulness had no significant impact on the intention to utilize AI technology, perceived comfort and subjective standards did. Additionally, the perceived advantages and ease of use are affected differently by each element of technology readiness.

Since 2020, when the coronavirus epidemic was at its peak, the Indonesian Ministry of Health's social media accounts have been constantly followed by a big number of individuals. The Indonesian Ministry of Health account is a fantastic resource for social media users, particularly Twitter users. The Republic of Indonesia's Ministry of Health's Twitter account publishes a wide range of content at random. As a result, it is usually difficult for Twitter users to determine the type of information provided by the Ministry of Health of the Republic of Indonesia. The positive and negative responses of Twitter users to material released by the Indonesian Ministry of Health's Twitter account are frequently noted. The decision tree algorithm is tree-based, similar to the extreme gradient boosting method (XGBoost). The extreme gradient boosting approach has been successfully implemented with high performance in the classification process. This classification is separated into two primary categories: general and essential information categorization and sentiment analysis, which is classified into three classes: positive, neutral, and negative. Both the classification work and the sentiment analysis produced outstanding accuracy levels. Based on 2243 tweets, an accuracy rate of 89.35% has been achieved for classification, supported by a precision of 88.76% and a recall value of 88.58% when using 80 data training and 20 data testing. Similarly, the maximum accuracy in sentiment analysis was achieved utilizing the same 80-20 data partitioning, with a 91.22% accuracy rate. Using 304 comments data, accuracy was calculated to be 89.17% and recall was calculated to be 89.06%. It's worth noting that an 80-20 split for training and testing consistently produced the best results for both the sentiment analysis and classification tasks.

Abstract. One major technological development pertains to the service robot industry. In Indonesia, the development of robots is increasing. Telkom will begin to explore new opportunities with companion robots, which can respond to and chat with humans and move around. Therefore, more analysis is needed based on market research and prospective user surveys. The present study aims to obtain information on the features of service robots significantly influencing customers’ desire to purchase them and on the types of service robots they are likely to choose. The discrete choice model method is used to understand and predict a decision from a discrete alternative, based on a set of choices constructed with the help of the Ngene software and the D-efficient design method. We processed questionnaire results using the Python-Biogeme software to look for significant attributes and their elasticity. The significant attributes of companion robots defined by T-test are basic robot look (3.86), battery life (5.45), robot price (9.16), walking-computer look (6.10), and robot weight (8.38). The suggested strategy, among the three types of companion robots, is to focus on the personal assistant robot with the highest T-test score while paying attention to the resources a company already has. Keywords: Service Robot, Discrete Choice Model, Significant Attribute, Elasticity

Abstract. In today’s competition-driven world, competitive advantage is the only way for an organization to survive. There are various ways to gain a competitive edge through the application of knowledge, technology, and science. Indonesian public research and development (R&D) faces numerous challenges, one of which is researchers’ low productivity in terms of international journal publications. To boost research productivity and performance, efforts must be made to streamline knowledge flows within public R&D institutions. Additionally, data from dynamic information flows in networks can be used to evaluate bureaucratic effectiveness and identify potential knowledge talents or actors within R&D organizations. The purpose of this study is to determine the role of managers at all levels in a public R&D organization in coordinating information flow through knowledge networks and bureaucratic relationships. The social network analysis (SNA) technique was used to map managers’ role in aligning knowledge flows in an Indonesian public R&D organization. Individuals were classified according to their type and centrality in the network, and network metrics such as “degree of centrality,” “eigenvector centrality,” and “clustering coefficient” were determined. It was found that the organization has some issues with knowledge flows and power distribution. The two distinct knowledge flow patterns, one for knowledge-seeking and the other for administrative knowledge, do not appear to be synchronized. The director of the R&D center is assumed to be less influential than the top manager in terms of knowledge and bureaucratic influence. At the managerial level, clique tendency is lower, while it is higher at the nonmanagerial level. Such centralization and cliques are dangerous because managing knowledge cannot be handled by a single person in a knowledge-intensive organization such as an R&D institution. Additionally, it was discovered that middle managers are critical for knowledge-seeking purposes, whereas administrative managers dominate the bureaucratic role. The research implications are dependent on deciphering the complexities of knowledge flows among employees and the variables that influence the knowledge-transfer process within an organization. This issue will exacerbate the problem by decreasing productivity and increasing reliance on a small number of people. Additional research is required to generalize the findings to other Indonesian R&D institutions. Keywords: Bureaucracy, knowledge management, social network analysis

Currently, social media is required to inform the public about local government programmes related to smart city. Advances in technology require the government to provide fast, precise and accurate information services. Local government leaders' communication patterns are crucial factors in the successful implementation of the smart city technology. This study aims to map the communication priorities of DKI Jakarta government leaders in publicising the smart city programmes using Instagram. The study, using a qualitative method with a content analysis approach, employs a case study involving an Instagram account owned by Anies Baswedan as the head of the DKI Jakarta regional government, analysing captions posted between February 2019 and February 2020. The ATLAS.Ti program was used for data processing. The study results reveal that the highest priority area for communication regarding the smart city programmes was Smart Economy. Moderate priorities included Smart Living, Smart People, Smart Governance and Smart Mobility. Lastly, Smart Environment had a low priority. This study demonstrates that Instagram can be an effective medium for building interaction between leaders and the community in delivering smart city programmes with balanced communication intensity for each programme. Thus, this study's findings are expected to provide a reference for local government leaders in balancing communication priorities for all smart city programmes. Keywords: Analysis content, instagram, local government leader, smart city, social media

Abstract. Shift is a Muslim youth movement community in Indonesia. As one of the Islamic Brand in Indonesia, Shift starts selling the merchandise to broaden the brand. However, selling the brand's merchandise is not easy work to do. A couple of factors might affect customer purchase intention, such as brand loyalty and brand image. This research aims to find a strategy on how Islamic Brand sell merchandises. This study will discuss several variables, such as brand knowledge, brand satisfaction, brand identification, brand image, brand loyalty, and perceived quality to purchase intention. The data collection uses the questionnaire as the method and analyses it on Partial Least Square Structural Equation Modeling (PLS-SEM). The questionnaires are distributed by online platforms with sample size of 436 respondents who knew about Shift Brand and Shift's Merchandise. The outcomes are brand knowledge, brand satisfaction, brand identification, influence brand image and brand loyalty. Brand loyalty has a positive impact on purchase intention; however, brand image and perceived quality insignificant impact purchase intention. The Shift must develop a strategy to strengthen brand knowledge, brand satisfaction, and brand identification; therefore, it can increase brand loyalty, leading to purchase intention. Keywords: Brand loyalty, brand image, perceived quality, purchase intention, Islamic brand, and partial least square.