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This study aims to determine the factors, both financial and non-financial, which influence corporate bond and Sukuk ratings. The results will be useful for companies, investors or related parties as additional information and references for their investment decisions. Using ordinal logistic regression models with SPSS version 21 software, the study analyses the determinants of corporate bond and sukuk ratings listed on the Indonesia Stock Exchange (IDX) for the period 2013-2017. The variables employed are profitability, liquidity, leverage, company size, securities structure and maturity date. The results of the Wald test statistics show that leverage ratio, firm size, security structure, and maturity date are the factors that influence the probability of high or low corporate bond ratings, while profitability and liquidity ratios are factors that have no effect on the level of such ratings. With regard to sukuk, profitability, liquidity, and maturity date are the factors that influence the probability of high or low corporate sukuk ratings, while leverage ratio, company size, and security structure have no effect on the ratings.

Purpose This study aims to analyze the determinants of ratings of corporate bonds and sukuk issued by firms listed on the Indonesia Stock Exchange (IDX) for the 2013–2019 period. Design/methodology/approach This study uses a quantitative approach by testing hypotheses and using logistic regression. Ordinal logistic endogenous (or dependent) variables (Y) in ordinal logistics use data in the form of levels (ordinal scale). Independent (or exogenous) variables (X), include financial and non-financial factors for dependent (or endogenous) variables (Y), namely, of corporate bonds and sukuk ratings. There are two approaches to the study they are Logit and Gompit (Negative Log-Log. The population of the study is Indonesian companies listed on the IDX that issued bonds and sukuk for the 2013–2019 periods. The sampling technique is purposive. In total, 16 corporate companies adhering to the above criteria and issuing bonds and sukuk were chosen. In total, 270 types of bonds and 280 types of sukuk were selected as samples. Findings The results of the Logit and Gompit regression show that leverage ratio, firm size, security structure and maturity date are important determinants of corporate bond ratings while profitability and liquidity ratios appear to have no influence on the rating. In the case of sukuk, profitability, liquidity and maturity date play important roles in influencing the corporate sukuk rating. However, there is no evidence to suggest that leverage ratio, company size and security structure may affect sukuk ratings. Research limitations/implications For both sukuk and bond issuers, it is necessary to pay attention to the factors that may affect the ratings. Specifically, Sukuk issuers need to pay attention to the return of asset, current ratio, growth and structure. On the other hand, bond issuers need to consider depth to equity, structure and maturity. As for investors, the findings of this study reveal that both bond and sukuk ratings reflect their performance. Practical implications This study provides useful information for investors that allows them to assess the risk of sukuk or bonds chosen based on rating and financial performance. Originality/value The novelty of this study lies in its econometric methodology used to identify factors which influence sukuk and bond ratings. Specifically, this study used two different techniques that allow a robust conclusion to be drawn. Furthermore, this study provides a systematic analysis which allows comparison between factors which affect bond and sukuk ratings in Indonesia.

This conceptual paper exclusively focused on how artificial intelligence (AI) serves as a means to identify a target audience. Focusing on the marketing context, a structured discussion of how AI can identify the target customers precisely despite their different behaviors was presented in this paper. The applications of AI in customer targeting and the projected effectiveness throughout the different phases of customer lifecycle were also discussed. Through the historical analysis, behavioral insights of individual customers can be retrieved in a more reliable and efficient way. The review of the literature confirmed the use of technology-driven AI in revolutionizing marketing, where data can be processed at scale via supervised or unsupervised (machine) learning.

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