The purpose of this article is to navigate the intricate balance between innovation and sustainability, pinpointing facilitating factors for their harmonious coexistence. It aims to guide firms toward a future where innovation drives progress while safeguarding the planet’s and its inhabitants’ well-being. Synthesizing the existing research, this study explores the impacts of various factors on sustainable innovation performance. The results highlight the positive associations of leadership roles, dynamic capabilities, stakeholder engagement, and social and environmental aspects with the firm’s innovation, providing insights for fostering sustainable innovation practices. This study proposes a framework that illustrates how factors impacting sustainable innovation intersect with theories studied in this article, such as the triple bottom line (TBL) and sustainability development theory (SDT). A systematic literature review (SLR) methodology is adopted to synthesize the knowledge and validate the research outcome to reach a conclusion. It will be beneficial to include sustainability both as a critical strategy for any firm’s growth and as a key part of the academic curriculum to create knowledge that will develop responsible leaders for a sustainable world. Future studies may utilize quantitative methods, like surveys, to empirically validate outcomes, offering numerical insights into factors influencing sustainable innovation within firms and enriching understanding of their impact and relationships.

The purpose of this study is to find the influence of various macroeconomic factors on the volatility index, as macroeconomic factors affect stock market volatility, resulting in an impact on the VIX Index, representing the risk in the stock market. To estimate the significance and importance of the U.S. macroeconomic variables on stock market volatility and risk, classification problems from machine learning are constructed to predict the daily and weekly trends of the VIX Index. Data from May 2007 to December 2021 is considered for analysis. The selected models are trained with twenty-four daily features and twenty-four plus nine weekly features. The outcomes suggest that the decisions made by the Light GBM and XG Boost on ranking features can be significantly accepted over logistic regression. It is found from the results that economic policy uncertainty indices, gold price, the USD Index, and crude oil are signified as strong predictors. The Financial Stress Index, initial claims, M2, TED spread, Fed rate, and credit spread are also strong predictors, while various yields on fixed income securities make a little less impact on the VIX Index. The TED spread, Financial Stress Index, and Equity Market Volatility (Infectious Disease Tracker) are positively associated with the VIX.

An empirical study was conducted to determine the impact of different types of risk on the performance management of credit rating agencies (CRAs). The different types of risks were classified as operational, market, business, financial, and credit. All these five variables were analysed to ascertain their impact on the performance of CRAs. In addition, apart from identifying the significant variables, the study focused on setting out a structured framework for future research. The five independent variables were tested statistically using structural equation modelling (SEM). The results indicated that market risk, financial risk, and credit risk have a significant impact on the performance of CRAs, whereas operational risk and business risk, though important, do not have a significant influence. This finding has a significant implication for the examination and inter-firm evaluation of CRAs.

This research study delves into the integration of IoT within the Australian manufacturing sector, analyzing five key factors influencing adoption and drawing comparisons with Rogers’ innovation theory. Its objectives encompass assessing how competitiveness, regulatory support, management support, manufacturing growth, and cost savings affect IoT adoption. Methodologically, the research journey includes defining the problem, conducting a thorough literature review, crafting a research proposal, executing pilot and main studies, validating findings, and ultimately composing the final thesis. Quantitative data obtained from a large pool of over 500 respondents shed light on the positive impacts of competitiveness, regulatory support, and cost savings on IoT adoption. Additionally, the study employs Importance-Performance Map Analysis (IPMA) to underscore the significance of management support in successful IoT implementation. Despite facing limitations such as common method bias and constraints on generalizability, the research underscores the transformative potential of IoT in enhancing operational efficiency and decision-making within Australian manufacturing. Overall, the study’s findings confirm the significant influence of key factors on IoT adoption, affirming its potential to drive competitiveness, regulatory compliance, cost efficiency, and growth within the Australian manufacturing landscape. Received: 17 January 2024 | Revised: 20 March 2024 | Accepted: 31 March 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support this work are available upon reasonable request to the corresponding author.