Why do employees resist knowledge management systems? An empirical study from the status quo bias and inertia perspectives

Li, Jia; Liu, Minghui; Liu, Xuan

doi:10.1016/j.chb.2016.08.028

Cited by 68 publications

(43 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Recent studies have noted customers’ AI aversion in lab experiments, showing that humanoid robots and AI automation threaten customers’ own identity (Mende et al 2019), personal consumption experience (Castelo, Bos, and Lehmann 2019; Leung, Paolacci, and Paolacci 2018), and perceived uniqueness (Longoni, Bonezzi, and Morewedge 2019). Incidentally, on the employee side, lab studies have also noted the negative views among workers toward computer algorithms (Dietvorst, Simmons, and Massey 2015, 2018; Li, Liu, and Liu 2016). Warning against the “dark side” of AI, Acemoglu and Restrepo (2017) and Frey and Osborne (2017) point out the concerns of AI displacing human jobs.…”

Section: Field Experiments 3: the Ai–human Coach Assemblagementioning

confidence: 99%

Artificial Intelligence Coaches for Sales Agents: Caveats and Solutions

Luo¹,

Qin²,

Fang³

et al. 2020

Journal of Marketing

162

View full text Add to dashboard Cite

Firms are exploiting artificial intelligence (AI) coaches to provide training to sales agents and improve their job skills. The authors present several caveats associated with such practices based on a series of randomized field experiments. Experiment 1 shows that the incremental benefit of the AI coach over human managers is heterogeneous across agents in an inverted-U shape: whereas middle-ranked agents improve their performance by the largest amount, both bottom- and top-ranked agents show limited incremental gains. This pattern is driven by a learning-based mechanism in which bottom-ranked agents encounter the most severe information overload problem with the AI versus human coach, while top-ranked agents hold the strongest aversion to the AI relative to a human coach. To alleviate the challenge faced by bottom-ranked agents, Experiment 2 redesigns the AI coach by restricting the training feedback level and shows a significant improvement in agent performance. Experiment 3 reveals that the AI–human coach assemblage outperforms either the AI or human coach alone. This assemblage can harness the hard data skills of the AI coach and soft interpersonal skills of human managers, solving both problems faced by bottom- and top-ranked agents. These findings offer novel insights into AI coaches for researchers and managers alike.

show abstract

Section: Field Experiments 3: the Ai–human Coach Assemblagementioning

confidence: 99%

Artificial Intelligence Coaches for Sales Agents: Caveats and Solutions

Luo¹,

Qin²,

Fang³

et al. 2020

Journal of Marketing

162

View full text Add to dashboard Cite

show abstract

“…In the current studies we found that choice-inertia for selecting inferior options increased when switching costs increased. Similarly, the switching costs have also been suggested to influence adverse choice-inertia in resistance to information sharing via management systems [ 33 ], when deciding on changing healthcare plans [ 34 ], and the failure of cryptocurrencies to become a serious competitor for traditional fiat currencies [ 35 ]. The similar impact of switching costs in each of these contexts suggests a likely relationship between the underlying factors leading to choice-inertia in each, highlighting a potentially fruitful line of research: Investigating whether choice-inertia in these varied decision contexts develops (manifests) similarly within an individual.…”

Section: Discussionmentioning

confidence: 99%

The effect of switching costs on choice-inertia and its consequences

Ashby¹,

Teodorescu²

2019

PLoS ONE

View full text Add to dashboard Cite

In two studies we provide a novel investigation into the effects of monetary switching costs on choice-inertia (i.e., selection of the same option on consecutive choices). Study 1 employed a static decisions-from-feedback task and found that the introduction of, as well as larger, monetary switching costs led to increases in choice-inertia. While experience and decreases in the similarity of options average payouts (expected value: EV) increased choice-inertia for the option with a higher EV (the EV maximizing option), switching costs increased choice-inertia for the inferior option (the lower EV option): The proportion of total participants showing choice-inertia for the EV maximizing option also increased with switching costs. Study 2 employed a dynamic decisions-from-feedback task where halfway through the task the EV maximizing option became the inferior option. The effect of switching costs increasing choice-inertia for both the EV maximizing and the inferior option was replicated with little impact of the change in options values being detected. In sum, decision makers appear to be sensitive to switching costs, and this sensitivity can bias them towards inferior or superior options, revealing the good and the bad of choice-inertia.

show abstract

“…Hartman, Doane, and Woo [42] showed the effect of status quo bias by examining electric power services that differed in reliability and price. The concept of status quo bias also has been used to explain why new services or policies are not accepted by people [43,44]. Further, Kreutzer [6] argued that loss aversion and status quo bias can inhibit emissions trading.…”

Section: Status Quo Biasmentioning

confidence: 99%

Cognitive Bias in Emissions Trading

Song

Ahn

2019

Sustainability

View full text Add to dashboard Cite

This study investigates whether cognitive biases such as the endowment effect and status quo bias occur in emissions trading. Such cognitive biases can serve as a barrier to trade. This study’s survey-based experiments, which include hypothetical emissions trading scenarios, show that the endowment effect does occur in emissions trading. The status quo bias occurs in only one of the three experiments. This study also investigates whether accumulation of experience can reduce cognitive bias as discovered preference hypothesis expects. The results indicate that practitioners who are supposed to have more experience show no evidence of having less cognitive bias. Contrary to the conventional expectation, the practitioners show significantly higher level of endowment effect than students and only the practitioners show a significant status quo bias. A consignment auction situation, which is used in California’s cap-and-trade program, is also tested; no significant difference between general permission trading and consignment auctions is found.

show abstract

Why do employees resist knowledge management systems? An empirical study from the status quo bias and inertia perspectives

Cited by 68 publications

References 53 publications

Artificial Intelligence Coaches for Sales Agents: Caveats and Solutions

Artificial Intelligence Coaches for Sales Agents: Caveats and Solutions

The effect of switching costs on choice-inertia and its consequences

Cognitive Bias in Emissions Trading

Contact Info

Product

Resources

About