Status Quo Bias – Samuelson & Zeckhauser (1988)

Why was the model made?

Samuelson and Zeckhauser developed their theory of status quo bias in response to a striking and consistent empirical observation: individuals demonstrably deviate from predictions of rational choice theory by disproportionately selecting existing alternatives in decision-making situations. Across numerous decision contexts—from consumer choices to investment decisions to public policy preferences—individuals exhibit a systematic tendency to maintain existing states of affairs even when rational analysis suggests superior alternatives are available. The motivation emerged from careful observation of real-world decision patterns that contradicted classical economic theory’s predictions. While expected utility theory and rational choice models predict that individuals should select options maximizing their expected utility regardless of current state, empirical evidence revealed persistent patterns inconsistent with this prediction. Individuals retained possessions longer than predicted, maintained insurance policies with suboptimal coverage, held stock portfolios unchanged despite information suggesting superior allocations, and made identical public policy choices year after year despite changing circumstances.

Samuelson and Zeckhauser noted that economists and decision theorists had insufficiently explained why substantial portions of decision-makers made choices that appeared to contradict their own stated preferences or to be inconsistent with rational economic principles. While previous research acknowledged the existence of status quo bias, no systematic theoretical framework had comprehensively explained the phenomenon’s prevalence, underlying causes, or extent across diverse decision contexts. The research was motivated by the conviction that understanding status quo bias required rigorous investigation distinguishing between alternative explanations. Status quo bias might reflect rational economic responses to transition costs and uncertainty, it might stem from cognitive limitations and misperceptions, or it might derive from psychological commitment and identity consistency motives. Each explanation had different theoretical implications and practical consequences.

Documenting which explanations actually drove status quo bias in different contexts was essential for developing sound theories of decision-making. The paper emerged from recognizing that decision-making models inadequately incorporated psychological and behavioral realities. Classical rational choice models operated as if decisions were costless, information was perfect, and preferences were independent of the decision context. Real decisions, by contrast, involved costs to changing alternatives, uncertainty about consequences, and psychological commitment to prior choices. Samuelson and Zeckhauser sought to develop a more realistic understanding of decision-making that acknowledged these factors while maintaining analytical rigor.

How was the model’s internal validity tested?

Samuelson and Zeckhauser employed multiple methodological approaches to test status quo bias’s prevalence and strength across diverse decision contexts. The comprehensive research program included controlled laboratory experiments, analysis of field data from major real-world decisions, and computational modeling examining different theoretical explanations. Laboratory experiments involved presenting decision-making tasks to student subjects in controlled settings. In a foundational experiment, subjects were asked to make hypothetical decisions about managing portfolios of investments. The critical methodological feature involved manipulating decision frames: some subjects were told they currently held a particular fleet composition and were asked what fleet they would maintain; other subjects were told they were making an initial fleet selection without current holdings. If decision-makers were truly rational, the same portfolio should be selected regardless of framing.

Instead, results showed that subjects significantly more often chose their current holdings when asked what they would maintain than when making initial selections. This finding demonstrated status quo bias in controlled experimental conditions. In another illustrative laboratory experiment, subjects faced choices involving selecting among health insurance plans. The status quo group selected among insurance plan options, told which plan they currently held. The control group selected among identical plans without reference to a current holding. Results showed the status quo group significantly more often selected the named status quo option regardless of its objective characteristics. This experimental evidence, replicated across numerous decision tasks, provided controlled confirmation that status quo bias operates in decision-making situations. Field studies analyzing real-world decisions provided additional validity evidence.

Samuelson and Zeckhauser examined Harvard University employees’ health insurance decisions across multiple years. The study documented that employees overwhelmingly maintained their existing health plan choices year to year, despite substantial changes in available plans and plan characteristics. Significantly, this persistence held even when objective analysis suggested switching would be economically superior. The researchers stratified analysis by age to account for changing preferences, but even age-controlled analysis revealed substantial status quo persistence. In another field study, the researchers examined allocation decisions in Teachers Insurance and Annuity Association (TIAA) and College Retirement Equities Fund (CREF) retirement plans. Participants allocated contributions between TIAA (a portfolio of bonds and mortgages) and CREF (a broadly diversified common stock fund). Results showed extraordinary stability in allocations year to year, with only a small percentage of participants changing their allocation despite substantial variability in plan performance and economic circumstances.

Participants appeared locked into initial allocation decisions made years previously, providing field evidence that status quo bias persists in consequential financial decisions. The researchers also examined housing choice data at Harvard University, where employees could choose among different housing options. Results showed significant persistence in housing choices year to year, even when circumstances changed (such as family composition changes) that would rationally warrant different housing selections. This field evidence across multiple contexts demonstrated that status quo bias operates consistently in consequential real-world decisions. The research tested internal validity by examining whether observed status quo persistence could be explained through alternative rational mechanisms. The analysis distinguished between status quo anchoring (a rational explanation based on transition costs and uncertainty) and psychological commitment (an explanation based on cognitive dissonance and psychological factors).

By examining patterns in the data—such as whether persistence varied with the magnitude of available alternatives, whether persistence was stronger in initial decisions, and whether persistence reflected information limitations—the researchers assessed which explanations accounted for observed patterns.

How was the model’s external validity tested?

Samuelson and Zeckhauser employed several strategies to establish that status quo bias generalizes across diverse contexts and decision types: First, the use of multiple methodological approaches (laboratory experiments, field studies of real decisions, computational analysis) provided triangulation suggesting findings generalize beyond any single method. Laboratory evidence of status quo bias in controlled settings was corroborated by field studies of real consequential decisions. This methodological diversity strengthened confidence that status quo bias represents a genuine and generalizable phenomenon rather than an artifact of experimental procedure. Second, the examination of status quo bias across diverse decision contexts (health insurance choices, retirement investment allocations, housing selections, health plan selections, airline mileage program participation) demonstrated that the phenomenon is not limited to narrow decision types.

The breadth of contexts where status quo bias appears suggests broad applicability rather than context-specific effects. The fact that bias appears in both hypothetical and consequential decisions, in individual and organizational contexts, in choices with monetary consequences and less tangible consequences, supports generalization. Third, the inclusion of field studies examining actual decisions where participants faced real consequences (choices affecting insurance coverage, retirement savings, housing situations) demonstrated that status quo bias operates in consequential contexts where individuals’ economic incentives are strong. This matters because, if status quo bias appeared only in hypothetical scenarios with no real consequences, generalization to actual decision-making would be questionable. The persistence of status quo bias in situations where individuals bear consequences of their choices enhances confidence in external validity.

Fourth, the theoretical analysis examining alternative explanations for status quo bias (rational decision-making with transition costs, cognitive limitations, psychological commitment) strengthens external validity by identifying underlying mechanisms. If status quo bias stems from identified psychological and economic mechanisms (rather than being an idiosyncratic laboratory phenomenon), then insights should generalize to other contexts where these mechanisms operate. Fifth, the examination of status quo bias across different age cohorts and demographic groups provided evidence of generality across population segments. The findings were not limited to particular demographic groups but appeared across diverse populations, enhancing confidence in broad applicability.

How is the model intended to be used in practice?

The status quo bias framework provides valuable guidance for understanding and addressing decisions in diverse practical contexts: For managers and organizational decision-makers, understanding status quo bias helps explain employee behavior and develop strategies to promote adaptive decision-making. Employees’ reluctance to change health insurance plans, retirement savings allocations, or work arrangements often reflects status quo bias rather than genuine satisfaction with current arrangements. Managers can address this by actively facilitating choice— creating decision moments where employees must actively select arrangements rather than allowing defaults to persist. Organizations can also address status quo bias by ensuring that current default arrangements are optimal, recognizing that defaults will persist due to status quo bias. For public policy contexts, understanding status quo bias illuminates why policy initiatives often face resistance and why status quo policies persist despite changing circumstances.

The framework suggests that policymakers seeking to change established practices should acknowledge that individuals’ reluctance to change may stem not from substantive disagreement but from status quo bias. This understanding might justify stronger government action to overcome inertia. Alternatively, it might suggest that policymakers can leverage status quo bias strategically by establishing new defaults aligned with desired outcomes. For business strategy and consumer markets, understanding status quo bias helps explain brand loyalty and consumer behavior. Customers’ continued purchase of familiar brands despite the availability of superior alternatives often reflects status quo bias. This understanding helps marketers recognize that switching costs and psychological inertia, not merely product superiority, influence market share. Companies can build competitive advantage by understanding that customers remain with current suppliers and brands partly due to bias, not only preference.

For financial advisory contexts, understanding status quo bias helps advisors recognize why clients resist beneficial portfolio changes. Investors holding unchanged portfolios often do so despite unconfirmed expectations or outdated allocations. Financial advisors can address this by recognizing status quo bias as a common psychological pattern and by developing strategies to help clients overcome inertia and make beneficial adjustments. For system design in organizational and consumer contexts, understanding status quo bias suggests that default choices substantially influence outcomes. Because defaults exert strong status quo effects, selecting optimal defaults becomes critical. Organizational systems should establish defaults aligned with desired outcomes, recognizing that inertia will cause individuals to maintain defaults even if they would prefer alternatives if actively chosen.

What does the model measure?

The status quo bias framework measures the strength and prevalence of individuals’ tendency to maintain existing alternatives in decision situations rather than switching to new alternatives. Measurement approaches include: Persistence metrics, measured in field studies, track the proportion of decision-makers who maintain existing choices in repeated decision situations. For example, if 80% of health insurance plan members maintain their existing plan selection when given annual opportunities to switch, this 80% retention represents status quo persistence. By comparing retention rates to predicted switching rates (based on plan characteristics and changing circumstances), researchers quantify the magnitude of status quo bias. Behavioral comparison metrics examine differences between status quo and control conditions. In experiments manipulating decision frames, researchers measure the percentage difference between status quo-named alternative selection rates and control condition selection rates.

Large differences indicate strong status quo bias, while small differences indicate weaker bias. Decision consistency metrics examine whether individuals’ stated preferences align with their revealed choices. If individuals express preferences for alternative arrangements but maintain status quo choices despite opportunities to switch, this inconsistency demonstrates status quo bias. Anchor strength measures examine how strongly current holdings influence new decisions. Regression analysis can quantify whether current holdings are strong predictors of future holdings, even after controlling for rational factors that should influence decisions (such as plan characteristics, risk preferences, or financial circumstances). Risk tolerance and preference heterogeneity measures assess whether status quo persistence reflects genuine preference for current arrangements or bias. By examining whether individuals’ stated preferences align with observed choices, researchers distinguish between status quo choices reflecting genuine preference versus bias-driven persistence.

The model also measures psychological factors contributing to status quo bias through various mechanisms: - Cognitive dissonance measures capture individuals’ resistance to information contradicting prior choices - Self- perception metrics examine whether individuals adjust attitudes to justify prior decisions - Sunk cost sensitivity measures assess whether prior investments influence current decisions - Regret avoidance metrics examine whether fear of regretting changes deters switching

What are the main strengths of the model?

Status quo bias theory demonstrates several substantial strengths: First, the model has extraordinary breadth of empirical support. Samuelson and Zeckhauser’s original article demonstrated status quo bias across numerous decision contexts (health insurance, retirement investments, housing, job selection, color preferences, technology choices, and many others). Subsequent research has confirmed status quo bias effects in diverse decision domains, from consumer product choices to major policy decisions. This breadth of empirical confirmation in real-world contexts provides robust validity evidence. Second, the theoretical framework carefully distinguishes between alternative explanations for status quo bias. Rather than attributing all status quo persistence to irrational psychology, the analysis considers whether rational economic factors (transition costs, uncertainty) might account for observed patterns. This intellectual honesty—recognizing that some status quo persistence reflects rational decision-making—strengthens the theoretical framework by identifying when bias operates and when persistence reflects optimal behavior.

Third, the model provides practical applicability across numerous contexts. Organizations, marketers, policymakers, and financial advisors can apply status quo bias insights to understand behavior and develop effective strategies. The framework’s explanatory power in real-world contexts makes it valuable to practitioners, not merely academics. Fourth, the combination of laboratory experimental evidence, field study evidence, and computational modeling provides multiple forms of confirmation. Laboratory experiments demonstrate that status quo bias operates in controlled conditions; field studies confirm that bias persists in real consequential decisions; and computational analysis examines whether different theoretical mechanisms can explain observed patterns. This methodological triangulation strengthens confidence in the findings. Fifth, the model illuminates psychological mechanisms underlying decision biases. By examining cognitive dissonance, self-perception, sunk cost fallacies, and regret avoidance as mechanisms creating status quo bias, the framework connects decision-making behavior to established psychological principles.

This theoretical grounding in psychology strengthens the explanatory power of the model. Sixth, the model’s recognition that bias varies with decision context and individual factors demonstrates sophistication. The analysis does not claim that status quo bias operates equally in all circumstances but recognizes that bias is stronger in initial decisions, where uncertainty is high, where transition costs are substantial, or where psychological commitment is stronger. This contextual sensitivity enhances the model’s nuance and applicability.

What are the main weaknesses of the model?

Status quo bias theory also exhibits notable limitations: First, the model may sometimes conflate status quo persistence with rational decision-making. While Samuelson and Zeckhauser distinguish between rational persistence (based on transition costs and uncertainty) and bias-driven persistence, the empirical literature sometimes treats all status quo persistence as bias. This conflation potentially leads to misattribution of rational economic behavior to psychological bias. Second, the model may not adequately capture how status quo positions change over time. The analysis focuses on status quo effects at particular points in time, treating current alternatives as fixed. However, in dynamic environments where alternatives and circumstances continuously change, status quo positions themselves change. The model may not fully address how status quo bias operates when the status quo itself is shifting.

Third, the laboratory experimental evidence relies on hypothetical decisions or small-stakes decisions that may not generalize to high-stakes consequential decisions. While field studies corroborate laboratory findings, some experimental evidence uses contexts where participants have little genuine interest in outcomes. Generalization from these controlled settings to actual decisions where participants have strong preferences and consequences is not automatic. Fourth, the model may underspecify individual differences in status quo bias susceptibility. While the analysis notes that bias varies across individuals, the model does not thoroughly characterize which individual characteristics (personality, age, experience, expertise, preferences) predict status quo bias magnitude. Understanding who is most susceptible to status quo bias would enhance practical applicability. Fifth, the measurement of status quo bias presents conceptual challenges.

Field studies measure status quo persistence through revealed preference (observing that individuals maintain choices), but this persistence might reflect multiple causes beyond bias—genuine preference satisfaction, high switching costs, limited awareness of alternatives, or rational updating of beliefs. Disentangling these causes from the observational data is difficult, potentially leading to overestimation of bias magnitude. Sixth, the model may not adequately address how information quality and decision transparency affect status quo bias. Decisions made with transparent information about available alternatives and choice characteristics might show different status quo effects than decisions made with limited or unclear information. The model does not thoroughly examine how information environments shape bias.

How does this model differ from older models?

Status quo bias theory represents a significant departure from rational choice theory in several important ways: First, while rational choice theory predicts that decision outcomes depend only on individuals’ preferences and available options, status quo bias theory proposes that current positions significantly influence choice independent of preferences. This shift recognizes that psychological and contextual factors beyond preference affect decisions, violating rational choice axioms. Second, status quo bias theory explicitly incorporates psychological mechanisms (cognitive dissonance, sunk cost fallacies, regret avoidance) as decision drivers, whereas classical rational choice theory minimizes or ignores such psychological factors. This represents a fundamental shift toward behavioral economics acknowledging psychological reality. Third, status quo bias theory recognizes that reference points and decision frames influence choices, whereas rational choice theory predicted context- independence.

The insight that individuals’ current holdings serve as reference points against which alternatives are evaluated represents a significant theoretical innovation. Fourth, status quo bias theory incorporates decision costs and uncertainty as integral to understanding decisions, whereas classical theory often treated these as peripheral. By examining how transition costs and uncertainty contribute to status quo persistence, the theory provides a more realistic model of decision-making under imperfect conditions. Fifth, status quo bias theory predicts systematic deviations from rational predictions in predictable patterns (toward status quo maintenance), whereas earlier models treated such deviations as random error or individual idiosyncrasy. By articulating systematic patterns of bias, the theory provides predictive power absent from previous frameworks. Sixth, status quo bias theory explicitly considers that individuals’ psychological commitment to past decisions influences current choices.

Earlier theories treated decisions as independent; status quo bias theory recognizes that prior commitment creates inertia affecting future decisions. 6. Barriers Identification Section:

What Barriers to Technology Adoption does the model identify?

While Samuelson and Zeckhauser’s research predates contemporary technology adoption theory, the status quo bias framework identifies fundamental psychological and economic barriers to technology adoption that apply across technological change contexts: Status quo inertia and preference for existing technology represents the primary barrier. Individuals demonstrate systematic tendencies to maintain existing technology choices (familiar products, established processes, accustomed tools) even when superior alternatives are objectively available. This barrier operates through multiple mechanisms. First, individuals may engage in biased evaluation of alternatives, selectively focusing on advantages of current technology while emphasizing disadvantages of new options. Current technology benefits are known and confirmed through experience; alternative technology benefits are uncertain and hypothetical. This asymmetry creates bias favoring status quo. Second, sunk cost commitment contributes—individuals who have invested time learning current technology, invested money in equipment and complementary systems, or developed expertise around existing technology are reluctant to abandon these investments.

Third, psychological commitment creates attachment to familiar alternatives, making transition to new technology psychologically costly beyond direct economic costs. Transition costs and switching barriers prevent adoption of superior alternatives. Even when new technology offers objective advantages, the costs to transition from existing technology may exceed perceived benefits. Transition costs include direct financial costs (purchasing new equipment, paying switching or setup fees), time costs (learning new systems, training, disruption during changeover), and compatibility costs (integrating new technology with existing systems, managing incompatibility during transition periods). These substantial transition costs create rational economic barriers to adoption that appear as status quo bias through the lens of simplified rational choice theory. Uncertainty about new technology creates aversion to change. Novel technologies involve uncertainty about actual performance, capabilities, reliability, and suitability for specific needs.

Decision-makers facing this uncertainty may rationally choose to maintain known, proven technologies rather than gamble on uncertain alternatives. This barrier reflects rational decision-making in the face of uncertainty, not merely psychological bias. However, the research suggests that uncertainty often exceeds objective risk levels due to psychological amplification—individuals overestimate risks of unfamiliar alternatives while underestimating risks of familiar options. Loss aversion and reference-dependent preferences create barriers to technology change. Individuals weigh potential losses from switching technology (loss of familiar capabilities, disruption to established workflows, risk of worse performance) more heavily than potential gains from adopting superior technology. This asymmetry—where losses loom larger than comparable gains—creates systematic bias against technological change even when objective analysis suggests gains exceed losses. Cognitive limitations and analysis costs inhibit thorough evaluation of alternatives.

Evaluating new technology thoroughly requires substantial cognitive effort: gathering information about alternatives, understanding their capabilities, assessing how they would perform in specific contexts, and comparing these to existing technology. These analysis costs may be prohibitive, leading decision-makers to maintain status quo by default rather than investing substantial effort in comprehensive evaluation. Misperception of sunk costs and prior investments creates bias against new technology adoption. Individuals often irrationally consider past investments in existing technology when deciding whether to adopt new technology, viewing these past costs as justification for continued use despite inferior current performance. This sunk cost fallacy represents a psychological barrier where past investments that should be irrelevant to forward-looking decisions continue to influence choices. Psychological commitment to prior technology choices creates attachment and resistance to change.

Individuals who have previously chosen technologies often rationalize their choices, develop positive attitudes toward chosen alternatives, and resist information contradicting the adequacy of their prior choices. This psychological commitment to prior decisions creates barriers to recognizing when new technology offers genuine advantages. Risk aversion specific to new technology represents another barrier. Even for individuals who are generally willing to accept risk, new technology adoption may feel riskier than maintaining status quo. The unknown risks of new technology (unknown failure modes, unforeseen compatibility issues, unexpected learning curves) may appear more threatening than the known risks of established technology.

What does the model instruct leaders to do in order to reduce these barriers?

The status quo bias framework suggests multiple strategies that leaders— including technology developers, organizational managers, marketers, and policymakers—can employ to reduce adoption barriers and promote technology change: To overcome status quo inertia, leaders should create decision contexts where individuals actively choose technology rather than allowing defaults to persist. Decision moments that force active selection—removing the option to maintain status quo without conscious choice—can interrupt psychological inertia. For organizational technology adoption, this might involve eliminating legacy systems, creating decision deadlines, or requiring active reselection during system transitions. For consumer technology, forcing active selection during renewal or replacement moments can break status quo inertia by requiring individuals to consciously choose alternatives rather than drifting with existing technology. To address transition costs as adoption barriers, leaders should actively work to reduce switching costs and facilitate transitions.

• This involves several strategies: providing migration tools and services that ease the transition from old to new technology; offering training and support reducing the learning burden of new technology adoption; creating compatibility bridges between existing and new systems minimizing disruption during transition; providing financial incentives or subsidies that offset direct switching costs; and planning transitions carefully to minimize disruption and business interruption. Organizations might offer transition support packages including extended training, dedicated support teams, and phased implementation reducing the pain of change. To reduce uncertainty about new technology, leaders should provide clear information about technology capabilities, performance characteristics, reliability, and actual performance in relevant contexts. Case studies demonstrating technology performance in similar contexts, testimonials from comparable users, transparent specifications, and trial opportunities allowing hands-on experience with technology can reduce uncertainty. Free trials, extended evaluation periods, or money-back guarantees can reduce the perceived risk of adoption by allowing potential users to experience technology before full commitment. Clear technical specifications, independent testing results, and honest communication about limitations help establish realistic expectations. To address loss aversion, leaders should reframe technology change to emphasize gains rather than losses. Marketing and communication should highlight concrete benefits individuals will gain from technology adoption (improved productivity, reduced costs, enhanced capabilities, better user experience) rather than focusing on what is being given up. Communication should emphasize the positive outcomes new technology enables rather than the displacement of existing technology. By emphasizing gains, leaders can partially overcome the psychological asymmetry between gains and losses. To reduce cognitive barriers and analysis costs, leaders should simplify the decision-making process. Providing clear, understandable comparisons between existing and new technology; offering straightforward recommendations for users with different needs; creating decision support tools that help users identify whether new technology suits their circumstances; and providing easily digestible information summaries can reduce the cognitive effort required for adoption decisions. Rather than requiring individuals to conduct exhaustive independent analysis, organizations can provide curated information and decision support that reduces analysis burden. To address sunk cost biases, leaders should explicitly encourage decision- makers to ignore past investments in existing technology when evaluating adoption decisions. Communication about new technology should emphasize that past investments in existing technology should not influence forward- looking technology decisions. Economic analyses should focus on future benefits and costs, explicitly excluding past sunk costs from decision calculations. Helping decision-makers recognize that past investments are irrelevant to future decisions can partially overcome sunk cost fallacies. To overcome psychological commitment to existing technology, leaders should make it psychologically safe to change technology. This involves creating environments where technology change is normalized and expected, rather than unusual or exceptional. Organizations where regular technology upgrades are standard practice rather than exceptions reduce the psychological pain of individual transitions. Ensuring that changing technology is not perceived as admitting prior poor judgment, but rather as rational response to improved technology availability, helps overcome commitment-based barriers. To address risk aversion toward new technology, leaders should implement risk-reduction strategies. Pilot programs and limited rollouts allow users to test new technology on manageable scales before full commitment. Providing extended warranty periods, strong customer support guarantees, and clear remediation processes if technology performs inadequately can reduce perceived risks of adoption. Gradual migration from existing to new technology reduces concentrated risk compared to abrupt wholesale changes. To leverage social influence against status quo bias, leaders should promote peer effects and community adoption. When early adopters successfully implement new technology and share their experiences with others, social proof can overcome psychological inertia. Communities of users can share experiences, support each other, and collectively normalize new technology adoption. Marketing strategies emphasizing that “peers like you have successfully adopted” can leverage social proof against status quo bias. The model suggests that effective technology adoption strategies must account for the pervasive tendency toward status quo bias. Rather than assuming that superior technology will naturally be adopted if merely made available, leaders must actively work to overcome the psychological and economic barriers that sustain status quo positions. This requires understanding status quo bias as a systematic phenomenon requiring deliberate countermeasures, not merely as individual quirks. 7
• Following Models or Theories: Following Models: Prospect Theory extensions examining reference-dependent preferences Behavioral economics models of choice and decision-making Technology adoption models incorporating status quo effects Innovation diffusion models accounting for adoption barriers Consumer switching cost and loyalty models Following Theories: Behavioral decision research examining individual decision-making Organizational change management theory Economic models incorporating behavioral realism Loss aversion and reference dependence research Psychological commitment and cognitive dissonance theory extensions Series Navigation This article is part of a Technology Adoption literature review series: 1
• A Model of Adoption of Technology in Households: Brown and Venkatesh, 2005 2
• Understanding Information Systems Continuance: An Expectation- Confirmation Model (Bhattacherjee, 2001) 3. Status Quo Bias in Decision Making (Samuelson and Zeckhauser, 1988) References