How do consumers engage with proactive service robots? The roles of interaction orientation and corporate reputation

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Abstract

Purpose

This study aims to apply the elaboration likelihood model to explore when, how and why robotic services increase customer engagement.

Design/methodology/approach

A field survey and two experiments were conducted to examine the proposed theoretical framework.

Findings

The robots’ proactive behavior encouraged customers to trust and engage with them. The influence of this behavior on customer engagement increased for highly interaction-oriented customers or when the reputations of companies were poor.

Practical implications

The findings can inform the efficient management of customer–robot interactions and thus support firms’ relationship marketing objectives.

Originality/value

The literature on robotic services has recognized that robots should be proactive to ensure positive customer experiences, but few studies have explored the relational outcomes of proactive robotic services. The authors’ in-depth empirical examination thus extends research into the role these services can play in fostering customer engagement.

Related collections

Most cited references 70

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Common method biases in behavioral research: A critical review of the literature and recommended remedies.

Philip M Podsakoff, Scott MacKenzie, Jeong-Yeon Lee … (2003)

Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.

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Risk as analysis and risk as feelings: some thoughts about affect, reason, risk, and rationality.

Paul Slovic, Melissa L Finucane, Ellen Peters … (2004)

Modern theories in cognitive psychology and neuroscience indicate that there are two fundamental ways in which human beings comprehend risk. The "analytic system" uses algorithms and normative rules, such as probability calculus, formal logic, and risk assessment. It is relatively slow, effortful, and requires conscious control. The "experiential system" is intuitive, fast, mostly automatic, and not very accessible to conscious awareness. The experiential system enabled human beings to survive during their long period of evolution and remains today the most natural and most common way to respond to risk. It relies on images and associations, linked by experience to emotion and affect (a feeling that something is good or bad). This system represents risk as a feeling that tells us whether it is safe to walk down this dark street or drink this strange-smelling water. Proponents of formal risk analysis tend to view affective responses to risk as irrational. Current wisdom disputes this view. The rational and the experiential systems operate in parallel and each seems to depend on the other for guidance. Studies have demonstrated that analytic reasoning cannot be effective unless it is guided by emotion and affect. Rational decision making requires proper integration of both modes of thought. Both systems have their advantages, biases, and limitations. Now that we are beginning to understand the complex interplay between emotion and reason that is essential to rational behavior, the challenge before us is to think creatively about what this means for managing risk. On the one hand, how do we apply reason to temper the strong emotions engendered by some risk events? On the other hand, how do we infuse needed "doses of feeling" into circumstances where lack of experience may otherwise leave us too "coldly rational"? This article addresses these important questions.