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Research suggests that consumers are averse to relying on algorithms to perform tasks that are typically done by humans, despite the fact that algorithms often perform better. The authors explore when and why this is true in a wide variety of domains. They find that algorithms are trusted and relied on less for tasks that seem subjective (vs. objective) in nature. However, they show that perceived task objectivity is malleable and that increasing a task's perceived objectivity increases trust in and use of algorithms for that task. Consumers mistakenly believe that algorithms lack the abilities required to perform subjective tasks. Increasing algorithms' perceived affective human-likeness is therefore effective at increasing the use of algorithms for subjective tasks. These findings are supported by the results of four online lab studies with over 1,400 participants and two online field studies with over 56,000 participants. The results provide insights into when and why consumers are likely to use algorithms and how marketers can increase their use when they outperform humans.

Technologies are becoming increasingly autonomous, able to make decisions and complete tasks on behalf of consumers. Virtual assistants already take care of grocery shopping by replenishing used up ingredients while cooking machines prepare these ingredients and implement recipes. In the future, consumers will be able to delegate substantial parts of the shopping process to autonomous shopping systems. Whereas the functional benefits of these systems are evident, they challenge psychological consumption motives and ingrained human–machine relationships due to the delegation of decisions and tasks to technology. The authors take a cross-disciplinary approach drawing from research in marketing, psychology, and human–computer interaction to examine barriers to adoption of autonomous shopping systems. They identify different types of psychological and cultural barriers, and suggest ways to craft the online and bricks-and-mortar retail environment to overcome these barriers along the consumer journey. The article finishes with implications for policy makers and a future research agenda for researchers examining autonomous technologies.

The study explored the adoption of information communication technologies (ICT) and its integration in Africa as an e-Government system. The article contributes to the continuing debate regarding the constraints that developing-country e-government systems face when it comes to the implementation and adoption of ICT as e-Governance for service delivery and the realization of socio-economic development. The study covers the various stakeholders who may have an impact on the implementation of e-Government at the grassroots level. It also highlights the barriers cited by other scholars of e-Governance that require local government attention. The impediments include challenges with governance, access to resources, leadership, ICT skills and funding. The study primarily relies on secondary data that is available in both the private and public domain to produce qualitative primary data. The research observations are on the essential role that local municipalities play in pursuing e-Government by using inductive thematic data analysis. The research concluded that the planning and implementation of e-governance should focus on finding methods to address a variety of issues which includes amongst other ensuring that the existing e-Governance initiatives gives valuable insight into what works and what does not, and provides meaningful guidance in developing and refining e-governance.

Can targeting information to network-central farmers induce more adoption of a new agricultural technology? By combining social network data and a field experiment in 200 villages in Malawi, we find that targeting central farmers is important to spur the diffusion process. We also provide evidence of one explanation for why centrality matters: a diffusion process governed by complex contagion. Our results are consistent with a model in which many farmers need to learn from multiple people before they adopt themselves. This means that without proper targeting of information, the diffusion process can stall and technology adoption remains perpetually low.

China is the world's largest user of industrial robots. In 2016, sales of industrial robots in China reached 87,000 units, accounting for around 30 percent of the global market. To put this number in perspective, robot sales in all of Europe and the Americas in 2016 reached 97,300 units (according to data from the International Federation of Robotics). Between 2005 and 2016, the operational stock of industrial robots in China increased at an annual average rate of 38 percent. In this paper, we describe the adoption of robots by China's manufacturers using both aggregate industry-level and firm-level data, and we provide possible explanations from both the supply and demand sides for why robot use has risen so quickly in China. A key contribution of this paper is that we have collected some of the world's first data on firms' robot adoption behaviors with our China Employer-Employee Survey (CEES), which contains the first firm-level data that is representative of the entire Chinese manufacturing sector.

New products can evoke anticipatory emotions such as hope and anxiety. On the one hand, consumers might hope that innovative offerings will produce goal-congruent outcomes; on the other hand, they might also be anxious about possible outcomes that are goal-incongruent. The authors demonstrate the provocative and counterintuitive finding that strong anxiety about potentially goal-incongruent outcomes from a new product actually enhances (vs. weakens) consequential adoption intentions (Study 1) and actual adoption (Studies 2 and 3) when hope is also strong. The authors test action planning (a form of elaboration) and perceived control over outcomes as serial mediators to explain this effect. They find that the proposed mechanism holds even after they consider alternative explanations, including pain/gain inferences, confidence in achieving goal-congruent outcomes, global elaboration, affective forecasts, and motivated reasoning. Managerially, the findings suggest that when bringing a new product to market, new product adoption may be greatest when hope and anxiety are both strong. The findings also point to ways in which marketers might enhance hope and/or anxiety, and they suggest that the use of potentially anxiety-inducing tactics such as disclaimers in ads and on packages might not deter adoption when hope is also strong.

Although technologies spurred by the “Internet of things” are increasingly being introduced in homes, only a few studies have examined the adoption or diffusion of such household technologies. One particular area of interest in this context is electricity consumption, especially the introduction of smart metering technology (SMT) in households. Despite its growing prominence, SMT implementation has met with various challenges across the world, including limited adoption by consumers. Thus, this study empirically examines the antecedents of SMT adoption by potential consumers. Using a mixed-methods design, the study first unearths the SMT-specific antecedents, then develops a contextualized model by drawing on theories from motivational psychology and the antecedents identified earlier, and finally tests this model using a large-scale survey of German consumers. The results provide support for many of the hypotheses and highlight the importance of motivational factors and some household demographic, privacy, and innovation-related factors on consumers’ intention to adopt SMT.

Behavioral research shows that reasons for and reasons against adopting innovations differ qualitatively, and they influence consumers’ decisions in dissimilar ways. This has important implications for theorists and managers, as overcoming barriers that cause resistance to innovation calls for marketing approaches other than promoting reasons for adoption of new products and services. Consumer behavior frameworks in diffusion of innovation (DOI) studies have largely failed to distinctly account for reasons against adoption. Indeed, no study to date has tested the relative influence of adoption and resistance factors in a single framework. This research aims to address this shortcoming by applying a novel consumer behavior model (i.e., behavioral reasoning theory) to test the relative influence of both reasons for and, importantly, reasons against adoption in consumers’ innovation adoption decisions. Based on two empirical studies, one with a product and a second with a service innovation, findings demonstrate that behavioral reasoning theory provides a suitable framework to model the mental processing of innovation adoption. Implications for managers and researchers are discussed.

Once artificial intelligence (AI) is indistinguishable from human intelligence, and robots are highly similar in appearance and behavior to humans, there should be no reason to treat AI and robots differently from humans. However, even perfect AI and robots may still be subject to a bias (referred to as speciesism in this article), which will disadvantage them and be a barrier to their commercial adoption as chatbots, decision and recommendation systems, and staff in retail and service settings. The author calls for future research that determines causes and psychological consequences of speciesism, assesses the effect of speciesism on the adoption of new products and technologies and identifies ways to overcome it.

With the proliferation and ubiquity of information and communication technologies (ICTs), it is becoming imperative for individuals to constantly engage with these technologies in order to get work accomplished.Academic literature, popular press, and anecdotal evidence suggest that ICTs are responsible for increased stress levels in individuals (known as technostress). However, despite the influence of stress on health costs and productivity, it is not very clear which characteristics of ICTs create stress. We draw from IS and stress research to build and test a model of technostress. The person-environment fit model is used as a theoretical lens. The research model proposes that certain technology characteristics — like usability (usefulness, complexity, and reliability), intrusiveness (presenteeism, anonymity), and dynamism (pace of change) — are related to Stressors (work overload, role ambiguity, invasion of privacy, work-home conflict, and job insecurity). Field data from 661 working professionals was obtained and analyzed. The results clearly suggest the prevalence oftechnostress and the hypotheses from the model are generally supported. Work overload and role ambiguity are found to be the two most dominant Stressors, whereas intrusive technology characteristics are found to be the dominant predictors of Stressors. The results open up new avenues for research by highlighting the incidence oftechnostress in organizations and possible interventions to alleviate it.