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With digital technologies shaping competition in many industries, predicting the future of potentially disruptive technologies becomes an essential task of business leaders concerned with the survival and success of their organizations. Looking into the future of disruptive technologies requires a philosophical stance and a practical method that accommodates the inherent uncertainty and nonlinearity of the path of disruptive innovations. Unfortunately, much of the current thinking in relation to adoption and diffusion of innovations is rather linear and deterministic. This article proposes a set of philosophical principles, together with a practical brainstorming method, for glimpsing into the future of disruptive technologies. The method of alternative histories is based on the traditional brainstorming techniques and the philosophical ideas of Imre Lakatos, Roy Bhaskar, Bruno Latour, and Nassim Taleb.

With the rapid development of digital technologies, many supply chain professionals are wondering how to move forward. Three technologies are poised to change supply chain business processes: robotic process automation (RPA), artificial intelligence (AI), machine learning (ML), and blockchain. Based on interviews with supply chain professionals in 14 large, mature manufacturing and service organizations, we outline the promise of each process and forecast their broad-scale adoption potential. Organizations should take the following measures to ensure their readiness to adopt and effectively use one or more of these technologies: (1) identify a supply chain technology visionary who can lead through the maze of technologies and the changing digital landscape, (2) develop a digital technology roadmap for their supply chain processes, and (3) update foundational information systems.

Digital transformation encompasses an organization's ability to adapt, respond, and position itself for success in the face of rapid technology evolution. A critical structure in achieving successful digital transformation is the digital project team, yet there is little research to study how they are created and developed. Much of the focus has been on agile methodologies and processes but there is less research to inform organizations on digital team dynamics such as roles, empowerment, learning, and leadership development. This article describes our findings from a multi-level study of the IT function of 60 companies, including fieldwork at 5 company sites involving more than 130 semistructured interviews with senior level IT professionals. We identify four essential team-based levers that enable digital transformation: (1) diverse and targeted team composition, (2) iterative goal setting, (3) continuous learning, and (4) talent management. Using company examples and practices, we describe how digital leaders are using these levers to propel their organizations forward in the journey toward digital transformation.

Recruiting talent has moved from a tactical HR activity to a strategic business priority. This has been driven by shifts in the source of firm value and competitive advantage and the critical role of human capital in those shifts. Technological advances have moved digital, AI-enabled recruiting from a peripheral curiosity to a critical capability. However, we know little about candidates'reactions to AI-enabled recruiting. Consequently, in this study, we examine the role of social media use, intrinsic rewards, fair treatment, and perceived trendiness on the intentions of prospective employees to engage with and complete digital, AI-enabled recruiting processes. The positive relationships between these factors and candidates'engagement with AI-enabled recruiting have several important practical implications for managers. We also examine the larger implications and make general recommendations to firms about using AI-enabled recruiting technology and tools.

Artificial intelligence (AI) is emerging as a potential growth area for facilitating the improvement and development of teams in the workplace. AI, as used in the team context, is currently underdeveloped and limited, thus reducing the wide-scale adoption and implementation of AI to improve team effectiveness. The use of AI to provide team diagnostics and improvements represents a significant shift in the approach organizations currently use to facilitate and strengthen effective teamwork. We describe the challenges involved in developing team effectiveness in organizations and the potential application of AI to improve teamwork. Further, we report on our experiences using AI in business school student project teams, the important advantages and disadvantages that emerged from this, and insights for future consideration when adopting and implementing AI in the workplace. Based on our use of AI and our experience training high-performing teams, we propose a multistep process for analyzing and improving teams in organizations.

Technological progress-including the development of online channels and universal access to the internet via mobile devices-has advanced both the quantity and the quality of data that companies can acquire. Private information such as this may be considered a type of fuel to be processed through the use of technologies, and represents a competitive market advantage. This article describes situations in which consumers tend to disclose personal information to companies and explores factors that encourage them to do so. The empirical studies and examples of market activities described herein illustrate to managers just how rewards work and how important contextual integrity is to customer digital privacy expectations. Companies' success in obtaining client data depends largely on three Ts: transparency, type of data, and trust. These three Ts-which, combined, constitute a main T (i.e., the transfer of personal data)-deserve attention when seeking customer information that can be converted to competitive advantage and market success.

Digital transformation recently converged on organizations as a new paradigm-a must-have exemplar-to enable competitive advantage. While the effects of digital transformation and their analytics, along with platform technologies, are becoming pronounced in companies, there is still a need to examine their implications on higher education. In light of the dynamics of digital transformation, how can higher education better manage the shift toward newer competencies and the need for innovation presented by the emergence of digital technologies? In this article, I examine the issues around the need for this balance-often defined in strategy as ambidexterity, or the need to address both competency with innovation-by outlining the historical trajectories that led to this problem in higher education, identifying three common pitfalls that higher education programs and administrators face, and tying these issues to higher education's absorptive capacity. To resolve these pitfalls, this article builds upon absorptive capacity frameworks for education practitioners and strategies as a prospective change management tool.

As the underlying technology of bitcoin, blockchain is expected to create a new economic system by revolutionizing the way we communicate over the internet. Blockchain seeks to improve information security and transparency by sharing encrypted data among peer-to-peer (P2P) networks. Due to its emphasis on security and trust, there is increased demand for blockchain's application in a variety of business sectors. The decentralized nature of blockchain creates the new concept of a token economy in which the community's revenue can be allocated to the actual content producers and service users who create value. This article looks at how blockchain technology and cryptocurrencies are evolving and interconnected, creating a token economy through different business models. Blockchain is expected to be a key technology that enables new protocols for the establishment of a token economy in the future, leading to a new economic paradigm.

Chatbots are used frequently in business to facilitate various processes, particularly those related to customer service and personalization. In this article, we propose novel methods of tracking human-chatbot interactions and measuring chatbot performance that take into consideration ethical concerns, particularly trust. Our proposed methodology links neuroscientific methods, text mining, and machine learning. We argue that trust is the focal point of successful human-chatbot interaction and assess how trust as a relevant category is being redefined with the advent of deep learning supported chatbots. We propose a novel method of analyzing the content of messages produced in human-chatbot interactions, using the Condor Tribefinder system we developed for text mining that is based on a machine learning classification engine. Our results will help build better social bots for interaction in business or commercial environments.

As companies become increasingly digital, growth hacking emerged as a new way of scaling businesses. While the term is fashionable in business, many executives remain confused about the concept. Even if firms have an idea of what growth hacking is, they may still be puzzled as to how to do it, creating a strategy-execution gap. Our article assists firms by bridging the growth hacking strategy-execution gap. First, we provide a growth hacking framework and deconstruct its building blocks: marketing, data analysis, coding, and the lean startup philosophy. We then present a taxonomy of 34 growth hacking patterns along the customer life cycle of acquisition, activation, revenue, retention, and referral; categorize them on the two dimensions of resource intensity and time lag; and provide an example of how to apply the taxonomy in the case of a fitness application. Finally, we discuss seven opportunities and challenges of growth hacking that firms should keep in mind.

Due to its intrinsic characteristics, artificial intelligence (AI) can be considered a general-purpose technology (GPT) in the digital era. Most studies in the field focus on the ex-post recognition and classification of GPT but in this article, we look at a GPT design ex-ante by reviewing the extreme and inspiring example of IBM's Watson. Our objective is to shed light on how companies can create value through AI. In particular, our longitudinal case study highlights the strategic decisions IBM took to create value in two dimensions: internal development and external collaborations. We offer relevant implications for practitioners and academics eager to know more about AI in the digital world.

This case introduces students to the history of women in the venture capital industry. It explores the impact of gender diversity on both company performance and fund returns, describes the lack of funding for female founders, and highlights examples of women-led venture capital firms.

For 160 years, Singapore Post had been one of Singapore's main postal service providers, delivering trusted and reliable postal services to homes and businesses. However, in 2019, Singapore Post was plagued by recent service lapses and operational problems, which had elicited customer complaints and concern from various stakeholders. Singapore Post was also facing increasing pressure from rising customer expectations, surging mail volumes, and the growing popularity of e-commerce. In response, Singapore Post pursued several initiatives to improve service operations and maintain its competitiveness in the postal industry. However, the company needed to devise a long-term plan to address recent problems, market changes, and deeply-rooted operational issues-and to regain consumer confidence over the long term.

In March 2017, the director of operations at Namratha Oil Refineries Pvt. Ltd., a leading manufacturer and distributor of high-quality packaged coconut oil in southwest India, faced a challenge. Because of inefficiencies in the company's packaging division, pouches, jars, and bottles were being overfilled with coconut oil, leading to an "oil giveaway" that had weakened the company's bottom line. The director analyzed the current processes and discovered improper data recording, frequent breakdowns, poor allocation of the maintenance crew, and an improper inventory management system. His experience told him that the existing production facilities were sufficient to meet the company's requirements, and therefore, the existing processes needed to be streamlined to ensure minimal or no loss of oil. What strategy should he recommend to reduce losses and restore the company's profitability?

Emmanuel Faber, Chairman and CEO of the food and beverage company Danone, believed that humankind had only ten years to bend the curve on climate change and restore the biodiversity that the global food and agricultural ecosystem was critically dependent on. Upon becoming CEO in 2014, he had built on Danone's long history of CSR-engagement to give a boost to the company's mission to bring health through food to as many people as possible. In September 2019, he reflected on the progress achieved thus far, including efforts to support regenerative agriculture through new contract types for famers in their milk division. Still, many questions remained in his journey to fix what he saw as the food industry's broken system: How could they manage the desired long-term transition to a sustainable system while also meeting the company's short-term financial targets? What was the role of the private sector? What economic model could support an inclusive transition? How to engage partners and consumers to embark on this journey?

Since its establishment in 2005, Hamburg's Martini Klinik had single-mindedly focused on prostate cancer care with a commitment to measure long term health outcomes for every patient. A wholly owned subsidiary of the University Hospital Hamburg, Martini Klinik was a "hospital in a hospital" in close proximity to other medical departments and services. By 2019, Martini Klinik had become the largest prostate cancer treatment program in the world with 8,000 outpatient cases and more than 2,500 surgical cases annually. Patients came from all over Germany and from around the world. A new and expanded facility was under construction to have the capacity to manage more patients with prostate cancer.

After reading and analyzing the case, students will be able to leverage product and brand opportunities to drive growth against multiple audience segments; understand the relationship between managing and maintaining strong brands, sustaining brand image clarity, and pursuing revenue growth; understand the key sources of customer value and develop a sustainable value proposition.

People can be irrational in the financial market setting, leading to catastrophic mistakes and costing millions of dollars. We tend to take credit for our successes but disown our failures. We have limited powers of attention, preventing us from noticing critical factors in investment analysis. Memories can lead to faulty assumptions that serve as the foundation for financial decisions. Emotions often lead to giving inappropriate weight to some factors while discounting others. Researchers have discovered over 100 predictable biases that lead people to make consistent errors. The chapters cover financial biases most relevant to investing, evidence of how they develop, and practical strategies that can help investors improve their performance by minimizing bias's negative influence. This book offers readers a guide to cognitive bias as it occurs in real financial situations and to categorizing the different biases based on fundamental brain science while emphasizing practical implications of financial decision making and providing a scientific basis for adjusting investment practices to avoid cognitive traps. Chapter 1 starts with a discussion of the relationship between and an evaluation of heuristics and biases, heuristics deriving from biases we develop. Biases grow from the brain's limited information processing power, developing into mental blind spots. An overview of the brain is then given, including explanations of neurons, axons, grey and white matter, lobes, and hemispheres. Behavioral biases are presented in three categories: attention biases (emerging from the way our brains process incentives), memory biases (since human memory is flawed), and knowledge biases (stemming from the interactions between new information and what we have experienced in the past). The book's organization is broken down, and the chapter ends with a reminder that our own biases are the hardest to understand.

People can be irrational in the financial market setting, leading to catastrophic mistakes and costing millions of dollars. We tend to take credit for our successes but disown our failures. We have limited powers of attention, preventing us from noticing critical factors in investment analysis. Memories can lead to faulty assumptions that serve as the foundation for financial decisions. Emotions often lead to giving inappropriate weight to some factors while discounting others. Researchers have discovered over 100 predictable biases that lead people to make consistent errors. The chapters cover financial biases most relevant to investing, evidence of how they develop, and practical strategies that can help investors improve their performance by minimizing bias's negative influence. This book offers readers a guide to cognitive bias as it occurs in real financial situations and to categorizing the different biases based on fundamental brain science while emphasizing practical implications of financial decision making and providing a scientific basis for adjusting investment practices to avoid cognitive traps. This chapter describes modes of thinking and the brain. Three modes of thinking that are key to investing are instinct, intuition, and reasoning, each of which is supported by different brain networks. The amygdala supports instinct; the default mode network and basal ganglia support intuition; the task network (external thinking), default mode network (internal thinking), and areas specialized in language, perception, and memory support reason. However, context is extremely important in determining which mode of thinking will be used in a specific situation. When reason is not used and intuition and instinct are relied on too much, we develop cognitive biases.

People can be irrational in the financial market setting, leading to catastrophic mistakes and costing millions of dollars. We tend to take credit for our successes but disown our failures. We have limited powers of attention, preventing us from noticing critical factors in investment analysis. Memories can lead to faulty assumptions that serve as the foundation for financial decisions. Emotions often lead to giving inappropriate weight to some factors while discounting others. Researchers have discovered over 100 predictable biases that lead people to make consistent errors. The chapters cover financial biases most relevant to investing, evidence of how they develop, and practical strategies that can help investors improve their performance by minimizing bias's negative influence. This book offers readers a guide to cognitive bias as it occurs in real financial situations and to categorizing the different biases based on fundamental brain science while emphasizing practical implications of financial decision making and providing a scientific basis for adjusting investment practices to avoid cognitive traps. Chapter 3 is an in-depth look at attention biases. Building on chapter 2, many attention biases span intuition and reason, marked by failures to attend to key information while devoting too much attention toward other information. This chapter starts by discussing focused attention biases: salience bias, default effect, optimism bias, ostrich effect, framing bias, hindsight bias, in-group bias, and hot hand effect. Next, cognitive illusions related to attention are unpacked, including illusion of control, Gambler's fallacy, and decision fatigue. These biases stem from interactions between our attention systems and our conscious beliefs. Relevant research, examples, and relevant brain mechanisms to each bias are also described as well as important remedies to each bias when it comes to investing.