The new age economy is primarily driven by Industry 4.0 and Industry 5.0, which facilitate smartification of organizations by helping them integrate and automate decision making. Recent advances in information and communication technologies, such as the cloud, big data, Internet of things, and artificial intelligence and nanotechnology, have accelerated the adoption of Industry 4.0 and Industry 5.0. Because of these advancements, organizations are now facing new challenges in the form of cybersecurity risks that are partly caused by these technologies. In recent years, there has been a spike in the number of cyberattacks, and organizations are taking steps to minimize the impacts of these attacks. To address this critical issue, in this article, we discuss possible future research directions that production and operations management (POM) researchers can undertake to help organizations, supply chains, and governments develop robust strategies for reducing the number of attacks and their repercussions. In particular, we identify several avenues for future research in the following domains of POM: (1) global operations strategy, (2) healthcare operations management, (3) public policy, (4) management of technology, (5) supply chain management, and (6) disruptive technologies. Research on the topic of cybersecurity is not only an opportunity for operations management researchers but also critical for industry and society to overcome the challenges of cybersecurity risks.

With human brands or individual celebrities in fields ranging from sports to politics increasingly using social media platforms to engage with their audience, it is important to understand the key drivers of online engagement. Using Twitter data from the political domain, we show that positive and negative-toned content receive higher engagement, as measured by retweets, than mixed or neutral toned tweets. However, less popular human brands generate higher social media engagement from positive-toned content compared with more popular human brands. Therefore, we recommend that popular human brands (e.g., popular politicians or chief executive officers) keep their content objective rather than emotional. Furthermore, the tone of related brands (i.e., human brands who belong to the same political party) has a strong reinforcement effect; that is, social media engagement is higher when the tone of the focal human brand and related brands are the same and lower when the tones are different. Therefore, we prescribe that human brands actively coordinate their social media content with related brands to generate higher engagement. From human brands’ perspective, our findings recommend a comprehensive social media strategy, which takes into account the tone of content, tone of related brands’ content, and human brands’ popularity.

Explosive growth in the number of users on various social media platforms has transformed the way firms strategize their marketing activities. To take advantage of the vast size of social networks, firms have now turned their attention to influencer marketing wherein they employ independent influencers to promote their products on social media platforms. Despite the recent growth in influencer marketing, the problem of network seeding (i.e., identification of influencers to optimally post a firm’s message or advertisement) neither has been rigorously studied in the academic literature nor has been carefully addressed in practice. We develop a data-driven optimization framework to help a firm successfully conduct (i) short-horizon and (ii) long-horizon influencer marketing campaigns, for which two models are developed, respectively, to maximize the firm’s benefit. The models are based on the interactions with marketers, observation of firms’ message placements on social media, and model parameters estimated via empirical analysis performed on data from Twitter. Our empirical analysis discovers the effects of collective influence of multiple influencers and finds two important parameters to be included in the models, namely, multiple exposure effect and forgetting effect. For the short-horizon campaign, we develop an optimization model to select influencers and present structural properties for the model. Using these properties, we develop a mathematical programming based polynomial time procedure to provide near-optimal solutions. For the long-horizon problem, we develop an efficient solution procedure to simultaneously select influencers and schedule their message postings over a planning horizon. We demonstrate the superiority of our solution strategies for both short- and long-horizon problems against multiple benchmark methods used in practice. Finally, we present several managerially relevant insights for firms in the influencer marketing context. This paper was accepted by J. George Shanthikumar, big data analytics.

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