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This study aims to investigate Malaysian stock market efficiency from the view of Sharīʿah-compliant and conventional stocks based on the effectiveness of technical trading strategies.

This study uses unconventional trading strategies that mix buy recommendations of Bursa Malaysia analysts with sell signals generated from 10 selected technical trading strategies (simple moving average, moving average envelopes, Bollinger Bands, momentum, commodity channel index, relative strength index, stochastic, Williams percentage range, moving average convergence divergence oscillator and shooting star) that are detected using ChartNexus. The period from 1 January 2013 until 31 December 2015 produces a total sample consisting of 1,265 buy recommendations of 125 Sharīʿah-compliant stocks and 400 buy recommendations of conventional stocks. The study period is extended until 31 March 2016 to provide an ample time for detecting the sell signal especially for buy recommendations that are released towards the end of 2015.

The resulting Jensen’s alpha show 8 out of 10 strategies are effective in generating abnormal returns in Sharīʿah-compliant samples while only 3 out of 10 strategies are effective in conventional samples. Prominent effectiveness of technical trading strategies in Sharīʿah-compliant stocks implies clear inefficiency in that stock market segment as opposed to those of the conventional stocks.

The results based on unconventional trading strategies provide new insights of Malaysian stock market efficiency especially in Sharīʿah-compliant and conventional stocks. The paper provides more robust findings on market efficiency as firms’ equity level data were focussed together with analysts’ buy recommendations from Bursa Malaysia.

Climate change has become an increasingly important issue globally, and organisations are being urged to be more carbon friendly by taking initiatives to reduce carbon emissions in their business operations. The purpose of this paper is to examine the extent to which climate change has been addressed and the influence of financial strength and corporate governance structure on the disclosure of carbon information.

The research process consists of an investigation via content analysis of the annual and sustainability reports of the top 100 public-listed companies in Malaysia for the year 2017.

The results of the study revealed that carbon information on carbon emissions accounting had the highest disclosure and that climate change risks and opportunities had the lowest disclosure. The results of the multiple regression analysis revealed that profitability is positively significant with carbon disclosure while leverage is negatively significant. However, the governance structure does not seem to have any influence on the disclosure of carbon information.

The conclusions drawn from the study must be interpreted with caution as the sample companies only comprise of the top 100 public-listed companies in Malaysia to provide an initial insight into the situation in Malaysia. Furthermore, the interpretations and conclusions drawn from this study are based solely on a cross-sectional analysis of the data for only one year.

This finding is a significant contribution to regulatory bodies and policymakers regarding the drivers of climate change initiatives in an emerging economy such as Malaysia. This finding suggests that in the Malaysian setting, financial structure influence decisions on climate change initiatives.

The commitment by business leaders of the impact on climate from the production processes would contribute towards a low carbon economy and subsequently improve the quality of life of the community.

The findings of the study provide insight of the business attitude towards climate change in an emerging economy such as Malaysia.

Artificial Intelligence (AI) is a growing technology impacting several business fields. The agricultural sector is facing several challenges, which may be supported by the use of such a new advanced technology. The aim of the paper is to map the state-of-the-art of AI applications in agriculture, their advantages, barriers, implications and the ability to lead to new business models, depicting a future research agenda.

A structured literature review has been conducted, and 37 contributions have been analyzed and coded using a detailed research framework.

Findings underline the multiple uses and advantages of AI in agriculture and the potential impacts for farmers and entrepreneurs, even from a sustainability perspective. Several applications and algorithms are being developed and tested, but many barriers arise, starting from the lack of understanding by farmers and the need for global investments. A collaboration between scholars and practitioners is advocated to share best practices and lead to practical solutions and policies. The promising topic of new business models is still under-investigated and deserves more attention from scholars and practitioners.

The paper reports the state-of-the-art of AI in agriculture and its impact on the development of new business models. Several new research avenues have been identified.