War produces devastating impacts on humankind, especially in terms of lives and livelihoods. While war is a widely studied topic in history, it remains relatively understudied in business. To address this gap, this article explores the impact of war for business and society. To do so, this article undertakes a prospective evaluation of the Ukraine and Russia conflict as a recent case of war. In doing so, this article reveals that a war can impact society within (e.g., limit access to basic necessities and monetary resources, increase unemployment and reduce purchasing power, and increase asylum seekers and refugees) and outside (e.g., supply shortage and inflation and threat of false information) as well as business within (e.g., threat of cyberattacks, threat to digital and sustainable growth, and shortterm and long-term sanctions and support) and outside (e.g., test of business ethics and moral obligations and test of brand management) war-torn countries. The article concludes with an agenda for future research involving war, business, and society.
Agriculture is an industry that contributes to the economic growth and social progress of many countries worldwide, as well as positive impacts to the environment. However, the agricultural industry also faces many challenges such as the quality of crops and land available for farming activities, climate change, poor economic conditions for farmers, and lack of technology. As the agricultural trend is towards achieving food security, improving nourishment, and advancing sustainable agriculture, Smart Farming harnesses the potentials of Industry 4.0 revolution to achieve the goals outlined. The critical consideration would the intention of farmers to integrate and adopt these smart, connected technologies in their farming activities. This study examined the behavioral intention to use Smart Farming technologies from the perspective of farmers using the Unified Theory of Acceptance and Use of Technology (UTAUT). A cross-sectional study was conducted using quantitative method. Data were derived from farmers in Malaysia via a face-to-face survey in 2021 (n = 381). Partial Least Squares (PLS) regression was applied for model and hypothesis testing. The results indicated that performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC) influenced the behavioral intention to adopt SFT. Social influence (SI) was found to be the strongest predictor of behavioral intention. This study contributes to the theoretical understanding of applying UTAUT to examine the behavioral intention to adopt Smart Farming among farmers. In practice, this study also provides implications for the Sarawak government to advance digital inclusion for all communities to achieve high income and advanced status by 2030.
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