General Psychology Article Critique VII

Functional Divergence of Two Threat-Induced Emotions: Fear-Based Versus Anxiety-Based Cybersecurity Preferences

Violet Cheung-Blunden, Kiefer Cropper, Aleesa Panis, and Kamilah Davis University of San Francisco

Two threat-induced emotions and their respective ability to sway cybersecurity preferences were investigated after a cyberattack on financial institutions. Our theoretical aim was to advance the functionalist claim and differentiate between fear and anxiety by their action tendencies. The emotions were expected to have unique motivation power and thus show mutually exclusive ties to the three types of safety behaviors emerged in our study. Avoidance would be uniquely embraced by fearful participants, whereas surveillance and vigilance would uniquely appeal to anxious participants. Study 1 (N � 199) used a cross-sectional design and found full support for the hypothesis regarding anxiety but only partial support for the hypothesis regarding fear. Study 2 (N � 304), an experiment of fearful, anxious, and relaxed groups, did not yield significant results but did offer methodological recommendations. The quasi-experiments in Study 3 (N � 120) and Study 4 (N � 156) supported the hypotheses on fear and anxiety. Our results in the novel domain of cyber threat brought new evidence to bear on the mixed literature on fear and anxiety. A discussion is offered on the methodological challenges of differentiating two closely related emotions as well as implications for the emerging debate of cybersecurity solutions.

Keywords: security motivation, action tendencies, cybersecurity, hypervigilance, general anxiety disorder

Fear and anxiety are often synonymous in spoken English—for example, when someone says “I fear for a future event” they usually mean “I worry about a future event”—but the functionalist approach regards these two emotions as characteristically distinct (Barlow, 2000; Blakemore & Vuilleumier, 2017; Darwin, 1872; Fontaine & Scherer, 2013; Frijda, 2007; Lazarus, 1999; Levenson, 2011; Shuman, Clark-Polner, Meuleman, Sander, & Scherer, 2017). The functionalist approach aims to build a unique profile for each emotion by connecting certain types of antecedent events to a set of phenomenology, a host of bodily sensations, and a class of behavioral outcomes. Past empirical studies have had difficulty differentiating the profiles of fear and anxiety because of their well-documented comorbidity (Helbig-Lang & Petermann, 2010; Perkins & Corr, 2006; Shen & Bigsby, 2010; van Uijen & Toffolo, 2015). A concise understanding of two-closely related emotions can benefit many fields, especially sentiment analysis where every nuance counts for its predictive value. Treating fear and anxiety as synonyms has resulted in many missed opportunities with regard to appreciating the tangible ramifications of two most common threat responses (Barlow, 2000; Beck, Emery, & Greenberg, 2005; Mason, Stevenson, & Freedman, 2014; Perkins, Cooper, Abdelall, Smillie, & Corr, 2010; Sylvers, Lilienfeld, & LaPrairie, 2011; Tovote, Fadok, & Lüthi, 2015).

The new domain of cyberinsecurity provides a valuable plat- form to differentiate the functions of fear and anxiety. With cy- berattacks on the rise, fearful and anxious sentiments have the potential to spur public behavior en masse as well as influence policy direction (Woody & Szechtman, 2013, 2016). This is not the first time in history when technological advances have intro- duced new risks and motivated new safety-seeking behaviors. If the early 20th century was a dangerous time to drive on the road (Loomis, 2015), then the early 21st century is a dangerous time to surf the Internet. Current newspaper headlines, such as “Computer Security is Broken From Top to Bottom” (Computer Security is Broken, 2017) and “Cybersecurity: A Race Without a Finish Line” (DiPietro, 2016), are reincarnations of an earlier sense of desper- ation about automobile hazards. Desperation can spur actions that are not necessarily based on rational thinking but rather on an emotional reaction (Cybersecurity National Action Plan, 2016; Cyber Warfare, 2012; Macaskill & Dance, 2013; Preston, 2014; Zetter, 2015). Functional differences between fear and anxiety may be capitalized upon in order to more accurately predict individual and collective action in a growing terrain of cyber threats (Cohrs, Moschner, Maes, & Kielmann, 2005; Druckman & McDermott, 2008; Maitner, Mackie, & Smith, 2006; Mason et al., 2014; Sahar, 2008; Yzerbyt, Dumont, Wigboldus, & Gordijn, 2003).

Functionalist Claims of Fear and Anxiety

Ever since Darwin (1872), emotions have been seen as products of evolutionary processes. Each emotion corresponds to a distinc- tive biological system that addresses a recurrent adaptive problem with a phylogenetically tested solution (Ekman, 1992; Frijda, 2007; Lazarus, 1999; Levenson, 2011; Tooby & Cosmides, 1990).

This article was published Online First November 26, 2018. Violet Cheung-Blunden, Kiefer Cropper, Aleesa Panis, and Kamilah

Davis, Department of Psychology, University of San Francisco. Correspondence concerning this article should be addressed to Violet

Cheung-Blunden, Department of Psychology, University of San Francisco, 2130 Fulton Street, San Francisco, CA 94117. E-mail: [email protected]

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Emotion © 2018 American Psychological Association 2019, Vol. 19, No. 8, 1353–1365 1528-3542/19/$12.00 http://dx.doi.org/10.1037/emo0000508

1353

Such a system must be able to detect situation cues, place the cues in the proper category, coordinate among biological circuitries and precipitate a specific action tendency. The coherence within each antecedent-behavior chain, nonredundant across discrete emotions, is at the center of the functionalist argument (Blakemore & Vuil- leumier, 2017; Shuman et al., 2017).

The functionalist tenet of distinctive emotion systems is partic- ularly applicable to basic emotions (Ekman, 1992; Levenson, 2011). As a basic emotion, fear was described by Darwin (1872) on several occasions—originating from the threat of physical harm and resulting in an uncontrollable urge to flee. Researchers have since found that the typical triggers for fear are imminent threats, and the typical behavioral outcomes are avoiding, running away, freezing, and hiding (Amir, Kuckertz, & Najmi, 2013; Frijda, Kuipers, & ter Schure, 1989; Glotzbach, Ewald, Andreatta, Pauli, & Mühlberger, 2012; Roseman, Wiest, & Swartz, 1994). The coherence between antecedents and behaviors is apparent in the case of fear. As an age-old mechanism, the fear circuitry detects immediate dangers and motivates a person to engage in distance- increasing strategies (Stein & Bouwer, 1997; Tooby & Cosmides, 1990).

Barlow (2000) extended the functionalist argument from basic emotions to a more complex emotion: anxiety. Anxiety is a shadow of intelligence: Humans’ ability to plan for the future is the reason why members of our species worry about looming threats. This view of anxiety, which centers on how a species meets its survival demands with a particular emotional faculty, is a func- tionalist argument (Stein & Bouwer, 1997; Tooby & Cosmides, 1990). Barlow (2000) must have considered action tendency as the crux of the functionalist argument (Fontaine & Scherer, 2013) and defined anxiety as a prolonged hypervigilance in response to, or in anticipation of, a non-imminent threat. Basic research of anxiety in laboratories and applied research on voter anxiety found not only hyper-attentiveness to threats but also a tendency to proactively search for threatening information (Gadarian & Albertson, 2014; Ladd & Lenz, 2008; Marcus, MacKuen, & Neuman, 2011; White, Skokin, Carlos, & Weaver, 2016).

Woody and Szechtman (2011, 2013) conceptualized a security motivation system and offered a detailed account of how the anxiety system operates differently from the fear system. The security system is a hardwired module, dedicated to the assessment and management of potential risks, whereas the fear system is responsible for imminent threats. Therefore, anxiety is triggered by subtle signs of hidden risk, in the absence of the obvious signs of danger in the case of fear. The ambiguity of the risk calls for information gathering, checking the environment for additional cues, and taking precautions, rather than avoidance behaviors typical of fear.

The motivation security system, as a product of our evolutionary past, is particularly relevant to today’s Internet-connected world (Woody & Szechtman, 2013, 2016). The kind of antecedents that trigger anxiety are abundant on the Internet, with a nearly limitless supply of fragmented, uncertain threat cues ranging from terrorist attacks to cyber breaches. Consumers of this kind of information may find their security system activated and unable to deactivate the system because of a lack of technical know-how to implement effective countermeasures. The consumer is left with few options but to share their sense of insecurity online while simultaneously reading similar posts from peers in their social network. The

positive feedback loop offers a psychological insight into how inaccurate news about potential threats spreads on the Internet.

Co-occurrence of Fear and Anxiety as a Barrier to Functionalist Studies

A critique of many previous studies of fear and anxiety is that they overlooked the tendency for the two emotions to co-occur in both state and trait forms. A state emotion arises primarily because of an instigating event in the outside world, whereas a trait emotion occurs primary because of a preexisting propensity within a person (Spielberger, 1983). For example, in their state forms, fearful and anxious responses to the threat of terrorism are treated synony- mously by the extended parallel process model of fear communi- cation (Ruiter, Abraham, & Kok, 2001; Witte, 1992). Similarly, in an analysis of emotional communication using an automated tool to count emotion-related keywords, “anxiety,” “fear,” and “scared” were placed in the umbrella category of “fear” (Soroka, Young, & Balmas, 2015). In their trait forms, clinical research found simul- taneous experiences of exaggerated fear and anxiety among pa- tients with a variety of disorders. The current edition of the Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric Association, 2013) has yet to successfully untangle the comorbidity of fear-based and anxiety-based disorders (Sharma, Woolfson, & Hunter, 2013). The co-occurrence of fear and anxiety has even garnered theoretical attention. One claim is that any differences between the two emotions are only microscopic or a matter of degree (Kim et al., 2008; Öhman, 2008; Ruiter et al., 2001).

The coexistence of fear and anxiety puts the result of many functionalist studies in question, especially if a study only mea- sured fear without anxiety, or vice versa (e.g., Cheung-Blunden & Blunden, 2008; Frijda et al., 1989; Marcus & MacKuen, 1993; Roseman et al., 1994). In retrospect, an open question is whether the topic of these studies was in fact fear or anxiety. Strictly speaking, any behavioral outcomes of these studies could be equally attributed to either emotion. One way to address the co-occurrence of fear and anxiety would be to look at comparative studies which are rare both in terms of review articles and empir- ical studies.

From Neural Networks to Behavioral Manifestations

Two review articles compared the neurobiology of fear and anxiety with an explicit expectation that neurobiology lays the foundation for the preparation, execution, and control of voluntary actions (Blakemore & Vuilleumier, 2017). Tovote et al. (2015) concluded that the two emotions share overlapping neural sub- strates but thought it promising to differentiate the neurobiology of fear and anxiety by investigating how long-range neural projec- tions interact with local microcircuits. An earlier review article by Sylvers et al. (2011) acknowledged the neural similarities but also pointed to a key difference—fear is represented on a less sophis- ticated neural level, whereas anxiety is represented in much wider and more inclusive neural pathways (Davis, Walker, Miles, & Grillon, 2010; de Wit, Kosaki, Balleine, & Dickinson, 2006; Furmark, Fischer, Wik, Larsson, & Fredrikson, 1997; Gray, 1982; Kalisch et al., 2004; McNaughton & Corr, 2004; Ruiz-Padial & Vila, 2007).

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1354 CHEUNG-BLUNDEN, CROPPER, PANIS, AND DAVIS

One interpretation of the less complex fear module and the more complex anxiety module is that they serve very different functions (Woody & Szechtman, 2013). Despite some shared neural sub- strates, each module solves a specific type of adaptive problem by getting the body into a specific state of readiness. Although the fear module engenders a “violent display of energy” by fully mobilizing sympathetic activity, the anxiety system requires atten- tion to novelty, which in physiological terms is to attenuate vagal influences over viscera and remove the antagonistic parasympa- thetic influence. Therefore, a shift in respiratory sinus arrhythmia (RSA) to lower variability ought to be a unique biomarker for the activation of the anxiety system (Gray, 1982; McNaughton & Gray, 2000). In support of this notion, Hinds et al. (2010) docu- mented the biomarker in an ambiguously threatening situation (rather than an imminently threatening situation), which was when participants touched a low-intensity putative contagion (rather than a high-intensity stimulus). Of particular interest to the present study is the precautionary behavior effective at deactivating the anxiety system, which was when participants washed their hands in running water (rather than sham washing).

The unique biomarker no doubt allowed an in-depth look at the operations of the anxiety system, but empirical comparisons of emotional systems ought to offer additional insights. McNaughton and Corr (2004) hypothesized that the respective neural networks of fear and anxiety are meant to carry out behavioral functions with markedly different “defensive directions.” While fear causes behaviors that increase the distance between the self and the threat, anxiety causes behaviors that decrease the distance. Empirical studies on defensive direction, however, have not always sup- ported the theoretical prediction. Perkins and colleagues (2010) found consistent correlations between trait fear and the tendency to orient away from the threat, but they found inconsistent correla- tions between trait anxiety and the tendency to orient toward the threat (Perkins & Corr, 2006).

Clinical Behaviors of Fear and Anxiety

The largest body of comparative studies comes from the past effort of clarifying the taxonomy of fear-based and anxiety-based disorders (Brown & Barlow, 2009; Brown, Chorpita, & Barlow, 1998; Gros, McCabe, & Antony, 2013; Gros, Simms, & Antony, 2011). Several notable factor analytical studies used behavioral symptoms as the basis of classification but derived mixed findings (Kendler, Prescott, Myers, & Neale, 2003; Kotov, Perlman, Gá- mez, & Watson, 2015; Slade & Watson, 2006; Waters, Bradley, & Mogg, 2014). On the one hand, Kendler et al. (2003) examined the diagnostic interview data of 5600 twins with 10 common psychi- atric and substance use disorders. Their analysis revealed two lower-order factors. The anxiety-misery factor pertained to major depression and generalized anxiety disorder, and the other factor pertained to animal and situational phobia. On the other hand, a longitudinal study by Kotov et al. (2015) showed mixed factors with no clear boundaries between anxiety and fear. Their factors were distress (depression, generalized anxiety, posttraumatic stress, irritability, and panic syndrome), fear (social anxiety, agoraphobia, specific phobias, and obsessive–compulsive disorders), and bipolar disorders (mania and obsessive-compulsive disorders).

An effort to classify psychopathologies based on safety behav- iors alone was carried out by Helbig-Lang and Petermann (2010).

Five subcategories of behaviors emerged: escape, reassurance- seeking, distraction, compulsive behavior, and threat neutraliza- tion. The authors did not tag the subcategories as fear-based or anxiety-based. In theory, escape and threat neutralization seem suitable behavioral strategies to address an immediate threat in fearful episodes. Reassurance-seeking and compulsive behavior seem to be aligned with a strategy of hypervigilance to address looming threats in anxious episodes.

The Present Study

In sum, the functional difference between fear and anxiety can be difficult to discern due to the comorbidity of the two emotions. Some neuroscientists argued that the smaller circuitry of fear and the larger circuitry of anxiety were supposed to prepare a person to carry out behaviors in opposite defensive directions. Their com- parative studies were more successful at linking fear to “moving away” behaviors than linking anxiety to “moving toward” behav- iors. We went beyond the question of directionality and cross referenced an array of clinical literature to identify specific behav- ioral tendencies for the two emotions.

We hypothesized that participants who were gripped by fear or anxiety would rally behind different types of cybersecurity solu- tions. We expected fearful participants to adopt avoidance or escape strategies to address cyber threats; they would avoid e-commerce, shun social media, and adopt air-gap defensive mea- sures. We expected anxious participants to gravitate toward hy- pervigilance, reassurance-seeking, and compulsive checking to counter cyberinsecurity; their solutions might range from information-seeking to embracing mass surveillance policies.

Study 1

This cross-sectional study examined the common types of cy- bersecurity strategies and uncovered their emotional appeals in a convenient Internet sample.

Method

Participants. The participants were 199 American adults between 18 and 68 years of age (M � 35.5 years, SD � 12.1 years). The sample consisted of 62.3% males and 37.7% fe- males. A few participants were students (15.1%), whereas the rest were working adults. Most participants held a bachelor’s degree (39.2%), followed by high school diploma (30.2%), associate degree (15.1%), master’s degree (12.6%), doctoral degree (1.5%), and some high school education (1.4%). The ethnic composition was primarily Caucasian (62.8%), followed by Indian (10.6%), other Asians (8.0%), African American (6.0%), Latino (6.0%), Eastern European (2.5%), and a “mixed/ other” category (4.1%). Besides the 43.7% participants self- identified as atheist or spiritual, the rest of the participants were Christian (36.7%), Hindu (9.0%), Buddhist/Taoist (3.0%), Mus- lim (3.0%), and “other” (4.6%). Political affiliation consisted of 38.2% Democrat, 31.2% independent, 17.6% Republican, 5.5% Libertarian, 2.5% American Independent Party, 1.5% Green Party, 1.5% Tea Party, and 2.0% “other.” All of the participants were screened for U.S. citizenship, with a majority residing in the United States at the time of the study (88.9%).

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1355TWO THREAT-INDUCED EMOTIONS

Procedure. The participants were recruited from an online survey service. In light of the possible utility of the present findings in democratic participation, the criteria were being an American citizen at least 18 years old. After giving their consent, participants followed a web link and completed a multimedia survey. The participants responded to demographic questions, watched a news report of a cyberattack, responded to questions about cybersecurity measures, and reported their current levels of state anxiety and state fear. Each participant received $1 upon completion of the study. All procedures were approved by the University of San Francisco Institutional Review Board (IRB) and were conducted according with American Psychological Associa- tion (APA) ethical conduct of research with human subjects.

Instruments. Demographics. The participants reported their age, sex, edu-

cation, ethnicity, religion, political affiliation, citizenship, and country of residence.

Multimedia news report. The hacking collective known as the Anonymous Group retaliated against a decision by the State De- partment and launched a Distributed Denial of Service attack on MasterCard by posting 10,000 credit card numbers online. A news report by MSNBC was chosen and was edited for clarity. The event details were delivered in approximately 550 words over 1.5 min.

State version of State–Trait Anxiety Inventory, Form Y (Spiel- berger, 1983). The original State–Trait Anxiety Inventory (STAI) provided 20 items to assess state anxiety. Kvaal, Laake, and Engedal (2001) replicated the two-factor solution which placed the negatively worded items in the “nervousness” factor and the positively worded items in the “well-being” factor. The present study chose the “nervousness” factor and further excluded the item “I feel frightened” because of its convergence with fear. The nine items from the STAI were administered with a four-point scale (1 � not at all to 4 � very much). Sample items include “I feel nervous” and “I’m worried.” Kvaal et al. (2001) used seven of the negatively worded items and reported a Cronbach’s alpha of .56. The nine-item scale in the present study reported an alpha value of .94.

State Fear Inventory (Cheung-Blunden & Ju, 2016). The nine-item State Fear Inventory gauges the degree of fearful ex- pressions to a threat scenario. A four-point response scale was used (1 � not at all to 4 � very much). Sample items include “I feel as if I cannot move” and “I feel a cold sweat.” The alpha coefficient was .95 in Cheung-Blunden and Ju (2016) and .94 in the present study.

Surveillance, vigilance, and avoidance cybersecurity measures. On the basis of the news report of alleged and actually imple- mented cybersecurity measures (Cybersecurity National Action Plan, 2016; Macaskill & Dance, 2013; Preston, 2014; Zetter, 2015), we drafted 10 items to measure participants’ support for surveillance policies (see Table 1). We also asked participants in a pilot study to describe their own strategies for ensuring cybersecurity (e.g., Mason et al., 2014). The popular answers were captured by the eight items on vigilance behaviors and the eight items on avoidance behaviors in Table 1. All cybersecu- rity items were administered on a five-point scale (1 � not at all to 5 � very much).

Results

An exploratory factor analysis was conducted on the cyberse- curity measures using principle components analysis and Varimax rotation. A five-factor solution was recommended based on an eigenvalue of one, accounting for about 74.9% of the variance (see Table 1). Factor I pertained to surveillance, with all of the surveil- lance items showing a minimum loading of .70 and an internal consistency of .95. Factor II pertained to vigilance, with all items showing a minimum loading of .66 and an overall Cronbach’s alpha of .94. Factors III, IV, and V pertained to various avoidance behaviors. Internal reliability was used to decide the best compo- sition of the avoidance scale, and the highest Cronbach’s alpha of .82 was achieved by including items 1, 2, 3, 4, 5, and 6.

Prior to hypothesis testing, we explored the effect of demo- graphics on all three dependent variables. Results showed that support for surveillance was higher among older participants, r(194) � .28, p � .000, females, t(194) � 3.17, p � .002, Christians or Hindus (simple comparisons: tChristian(194) � 2.54, p � .012; tHindu(194) � 5.88, p � .000), but was lower among participants who resided outside the United States at the time of study, t(194) � 3.88, p � .000, as well as those who stated “spiritual” as their religion, t(194) � 3.47, p � .001. The preference for vigilance measures was lower among Cauca- sians, t(194) � 2.64, p � .009, and students, t(194) � 2.36, p � .019; but higher among older participants, r(194) � .16, p � .027, females, t(194) � 1.98, p � .049, Christians, t(194) � 2.40, p � .017, Hindus, t(194) � 5.80, p � .000, Asians, t(194) � 2.02, p � .045, as well as those who resided outside the United States, t(194) � 6.24, p � .000. In terms of avoid- ance strategies, the only demographic predictor was the positive effect of residence outside the United States, t(194) � 2.40, p � .017.

After establishing the significance of demographics in each dependent variable, we added both fear and anxiety as predictors in the regression analysis for each cybersecurity measure. Our hypothesis regarding anxiety gained support in the analyses of surveillance and vigilance measures. Specifically, a significant predictor for surveillance cyber policies, F(8, 185) � 12.97, p � .000, R2 � 36.9%, was state anxiety (� � .27, p � .001) but not state fear (� � .12, p � .138). Similarly, a significant predictor for vigilance behaviors, F(10, 183) � 12.30, p � .000, R2 � 41.6%, was state anxiety (� � .55, p � .000) but not state fear (� � �.08, p � .363). Although our anxiety hypothesis was fully supported, our fear hypothesis was only partially supported. Results showed that avoidance behaviors, F(3, 184) � 17.28, p � .000, R2 � 22.3%, were significantly predicted by both state fear (� � .25, p � .004) and state anxiety (� � .28, p � .001).

Study 2

Because relaxation is considered a state of mind opposite to a myriad of negative emotions (e.g., Ergene, 2003; Spielberger, 1983), the present study used relaxation as a baseline to study the effects of fear and anxiety. We randomly assigned participants to a relaxed, fearful, or anxious group and then compared their cybersecurity preferences.

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1356 CHEUNG-BLUNDEN, CROPPER, PANIS, AND DAVIS

Method

Participants. Participants were 304 college students (M � 19.0 years, SD � 2.1 years) enrolled in various psychology courses in a university in the San Francisco Bay Area. The sample con- sisted of 75.3% female students and 24.7% male students. More than half of the sample were freshmen (68.1%), followed by sophomores (16.4%), juniors (8.6%), and seniors (6.9%). The ethnic composition was 31.6% Asian, 24.7% Caucasian, 21.7% Latino, 4.3% African American, 3.6% Pacific Islander, 3.0% Mid- dle Eastern, and 11.1% various other ethnic origins. In terms of common religious affiliations, 53.3% self-identified as Christian, 21.1% were atheist, and 14.1% were spiritual. One-third of the participants did not have a political affiliation (31.9%), whereas the rest were primarily Democrat (54.9%), Republican (9.9%), or Libertarian (1.6%). The sample consisted of mostly U.S. citizens (91%) who were born in the United States (82.6%).

Procedure. The participants were recruited from psychology classes on a voluntary basis. Participants reported their trait fear and trait anxiety in a preliminary questionnaire to make sure the two groups did not have any a priori differences in the target emotions. Regardless of their traits, they were randomly assigned into three groups where they were induced to feel fearful, anxious,

and relaxed, respectively. Specifically, our mood induction was conducted in three stages to maximize the effect of the manipula- tion. First, a 2-min video was used to prime relaxation (a sunset on the beach), fear (a clip from The Shining: Gross & Levenson, 1995), and anxiety (news report of natural disasters). Second, behavioral tasks further enhanced relaxation (name 10 vacation spots), fear (subtract 7 from 2,000 for 10 consecutive times), and anxiety (name 10 topics in an upcoming exam). Third, participants wrote a 200-word paragraph to describe their own emotional experiences congruent with their group assignments (Lerner, Gon- zalez, Small, & Fischhoff, 2003). After the mood induction, par- ticipants watched a video of a cyberattack and then responded to questions regarding their cybersecurity preferences.

Before data analysis, two independent coders conducted com- pliance checks and flagged problematic essays on a three-point scale (0 � full compliance, 1 � slight incompliance, 2 � obvious incompliance). The specific coding for the fear/anxious essays was 0 � descriptions of target emotion only, 1 � notable mentions of the other emotion, 2 � obvious mentions of the other emotion. The coding for the relaxed essays was 0 � descriptions of relaxation only, 1 � relaxation was discussed in the context of stress man- agement, 2 � mentions of unwillingness or inability to relax. With

Table 1 Factor Structure of Cybersecurity Measures in Study 1

Factor

Items I II III IV V

Surveillance cybersecurity measure

1. Keep track of Internet traffic in order to locate cyber attackers .82 .21 .10 .08 .04 2. Record the online activities of suspected cyber attackers .85 .23 .02 .18 .07 3. Demand Twitter to deny service to cyber attackers .84 .20 .02 .10 .24 4. Order YouTube to take down a video that serves as a tutorial for cyber attackers .78 .17 .04 .16 .29 5. Ask Facebook to turn over the accounts of the suspected cyber attackers .87 .16 .05 .18 .22 6. Monitor search engines to track users’ browsing habits .79 .27 .17 �.08 �.25 7. Ask software companies like Microsoft and Apple to grant the government secret access to their

users’ machines through back doors .70 .22 .32 �.18 �.25 8. Offer Internet companies financial support for their collaboration with the government .77 .26 .26 �.10 �.20 9. Use mass interception to detect immediate threats (in real–time without storing any data) .80 .20 .09 .02 �.01

10. Store mass Internet communication data for future use .85 .14 .10 �.15 �.11

Vigilance cybersecurity measure

1. Keep a close eye on my credit card accounts .26 .80 .02 .30 .04 2. Search for news to stay informed about recent cyberattacks .20 .85 .18 .00 .04 3. Check my bank statements every month .27 .72 �.01 .46 �.02 4. Safeguard my passwords to prevent them from falling into the wrong hands .24 .66 .00 .53 .09 5. Conduct research on the level of security at my financial institutions .17 .83 .12 .04 .08 6. Call credit card companies and make sure nothing is happening to me .24 .79 .26 �.17 .11 7. Change the passwords on my online accounts at least once a month .19 .84 .16 .07 .03 8. Keep 1–800 numbers handy in case I need to cancel my cards quickly .29 .78 .10 .09 .07

Avoidance cybersecurity measure

1. Cancel some credit cards .19 .22 .75 �.04 �.03 2. Stop purchasing things online .17 .15 .87 .00 .01 3. Avoid using PayPal .00 �.05 .73 .31 �.11 4. Use cash as much as possible .02 .16 .54 .30 .43 5. Stop using online banking .19 .20 .71 .03 .30 6. Keep the number of credit cards to a minimum .07 .09 .45 .61 .28 7. Stop sharing personal information online .01 .12 .08 .03 .74 8. Refuse to open suspicious e–mails �.03 .19 .12 .81 �.02

Note. Values in boldface type indicate the highest loading among the five factors.

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an intercoder reliability of .84, the flags from the two coders were added. The data from 11 participants were excluded from data analysis as they received two or more flags from the coders. All procedures were approved by the University of San Francisco IRB and were conducted according with APA ethical conduct of re- search with human subjects.

Instruments. Demographics, state emotions, and cybersecurity measures.

These were the same as in Study 1. The Cronbach’s alpha in the present study was .92 for State Fear Inventory, .93 for state anxiety scale, .85 for avoidance scale, .90 for surveillance scale, and .90 for vigilance scale.

Trait version of State–Trait Anxiety Inventory, Form Y (Spiel- berger, 1983). The positively worded items on the scale were not used due to concerns of their content validity: a lack of calmness can imply a wide range of negative emotional states beyond anxiety (Bieling, Antony, & Swinson, 1998). The negatively worded items were used on a four-point scale (1 � not at all to 4 � very much). Sample items include “I worry too much over some- thing that really doesn’t matter” and “I have disturbing thoughts.” The Cronbach’s alpha reported by Spielberger (1983) was .85 for the entire scale, and the present study found an alpha value of .87 for the subset used.

Fear Questionnaire (Marks & Mathews, 1979). Beck, Stan- ley, and Zebb (1996) recommended three sets of five-item sub- scales of the Fear Questionnaire (FQ) to tap into agoraphobia, blood/injury phobia, and social phobia. Because the social phobia subscale has shown a comorbidity with social anxiety in the past (Kashdan, Elhai, & Breen, 2008; Oei, Moylan, & Evans, 1991), we only used the agoraphobia and blood/injury phobia subscales (0 � would not avoid it to 8 � always avoid it). A sample item for agoraphobia is “traveling alone by bus or coach,” and a sample item for blood/injury is “going to the dentist.” Oei et al. (1991) found a Cronbach’s alpha between .71 and .83 for the 15-item scale, and the present study found a value of .76 for the 10-item scale.

Results

The group assignments were conducted on a proper basis with- out any a priori differences in trait fear, F(2, 292) � 0.04, p � .964, or trait anxiety, F(2, 292) � 0.36, p � .695. However, the manipulation resulted in no significant group differences in the induced emotions (fear: F(2, 292) � 1.38, p � .253; anxiety: F(2, 292) � 1.40, p � .248) or in the cybersecurity preferences (avoid- ance: F(2, 292) � 0.73, p � .481; surveillance: F(2, 292) � 0.36, p � .696; vigilance: F(2, 292) � 0.19, p � .823). Post hoc analyses were conducted to improve the research design for the upcoming studies. To explore a matching state–trait methodology that has been used elsewhere (e.g., Reinholdt-Dunne, Mogg, Esb- jorn, & Bradley, 2012; Ruscio & Borkovec, 2004; White et al., 2016), a two-way ANOVA examined how participants with dif- ferent predispositions responded to the three types of mood induc- tions. Figure 1 illustrates the effects of mood inductions on par- ticipants with or without a phobic tendency and the largest �p2 was found when applying fearful mood induction on phobic partici- pants. In a similar analysis of trait anxiety in Figure 2, the largest �p2 was found when applying anxious mood induction on anxiety- ridden participants. Most of all, the relaxed condition effectively

reduced the preexisting trait differences and seemed a suitable control condition in the upcoming studies.

Our upcoming studies would screen participants for trait sever- ity and then administer a matching mood induction. A problem with screening for fearful and anxious participants in a single comparative study was that many participants would show comor- bid fearful and anxious traits and they could not be easily assigned to the fearful or the anxious group. We therefore opted for an alternative approach of conducting two separate studies by con- trasting fearful and relaxed participants in one study, and compar- ing anxious and relaxed participants in the other study. This design retained the ability to keep track of both fear and anxiety, and ascertain not only the presence of the proper emotion-action ten- dency pair but also the absence of the improper emotion-action tendency pair. The various �p2 in Figure 1 and Figure 2 were used to plan our sample size. With a numerator of �SSeffects � SSmain trait effect � SSmain mood effect � SSinteraction of matched condition and a denominator of SStotal � �SSeffects � SSerror, the effect sizes to be detected should be in the medium range (�fear2 � .050; �anxiety2 � .059). Cohen (1988) recommended a sample size of about 60 participants per group to detect such an effect with a power of .80.

Study 3

This quasi-experiment contrasted the cybersecurity preferences of participants from an anxious and a relaxed state of mind. The groups were created by a matching state–trait method. Trait anx- iety scores were used as a basis to assign participants to receive a corresponding mood induction (e.g., Reinholdt-Dunne et al., 2012; Ruscio & Borkovec, 2004; White et al., 2016) and group differ- ences were expected along the line of anxiety but not fear.

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Figure 1. Fearful emotion as a function of preexisting trait and mood induction in Study 2. The main effect of trait fear approached significance, F(1, 292) � 3.55, p � .061, �p2 � .012; the main effect of mood induction was not significant, F(2, 292) � 1.60, p � .203, �p2 � .011; their interaction approached significance, F(2, 292) � 2.62, p � .074. Simple comparisons found that participants with different fearful propensities responded simi- larly to the relaxed mood induction, F(1, 287) � 0.32, p � .572, �p2 � .001, and to the anxious mood induction, F(1, 287) � 1.63, p � .203, �p2 � .006; but they responded differently to the fearful mood induction, F(1, 287) � 8.33, p � .004, �p2 � .028. See the online article for the color version of this figure.

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1358 CHEUNG-BLUNDEN, CROPPER, PANIS, AND DAVIS

Method

Participants. The participants were 120 college students (M � 18.8 years, SD � 1.2 years) enrolled in various psychol- ogy courses in a university in the San Francisco Bay Area. The sample consisted of 71.8% female students and 28.2% male students. More than half of the sample were freshmen (58.2%), followed by sophomores (23.6%), juniors (10.0%), and seniors (8.2%). The ethnic composition was 36.4% Caucasian, 31.8% Asian, 13.6% Latino, 3.6% Native American, 1.8% Eastern European, 1.8% Middle Eastern, 11.0% various other ethnic origins. A third of the participants were not religious (20.0% atheists and 11.8% claiming spiritual), whereas the remaining two thirds were mostly Christian (56.4%) followed by Buddhist (4.5%). Nearly half of the participants claimed they had no political affiliations (43.6%), and the rest were primarily Dem- ocrat (40.9%), Republican (10.9%), and Libertarian (2.7%). All participants were U.S. citizens, and a majority was born in the United States (90.0%).

Procedure. The participants were recruited from psychol- ogy classes on a voluntary basis. An anxious experimental group and a relaxed control group were created by screening participants for trait anxiety at Time 1. Depending on their scores, a corresponding mood induction procedure was admin- istered at Time 2. From their respective states of minds, the experiment and control group watched the news report of the cyberattack and then voiced their cybersecurity preferences.

Specifically, one week prior to the study session, each par- ticipant received a web link. The questionnaire at Time 1 contained a consent form, questions on demographics, filler questions, as well as questions concerning the two traits of our interest: trait worry (PSWQ) and trait fear (FQ). The PSWQ

data were analyzed in a similar manner as in Ruscio and Borkovec (2004). A total PSWQ score was calculated, and a score of 54 was used to assign participants to either the anxious condition (greater or equal to 54) or the relaxed condition (less than 54). The FQ was also administered to check for divergent validity/comorbidity between trait anxiety and trait fear. When the participants arrived at the lab a week later, each received a web link appropriate for their group assignment. The trait STAI scale was administered at the start of Time 2 to verify group assignment. Mood induction was carried out by following the same threefold procedure as Study 2, before the participants reported their cybersecurity preferences and their state emo- tions. Two coders followed a similar procedure as Study 2 and conducted compliance checks on the essays. With an intercoder reliability of .75, the data from 10 participants were excluded. All procedures were approved by the University of San Fran- cisco IRB and were conducted according with APA ethical conduct of research with human subjects.

Instruments. Demographics, trait emotions, state emotions, and cybersecu-

rity measures. These were the same as Study 1 and 2. The Cronbach’s alpha from the present study was .93 for State Fear Inventory, .89 for state anxiety scale, .83 for avoidance scale, .93 for surveillance scale, .91 for vigilance scale, .65 for trait STAI, and .78 for FQ.

Penn State Worry Questionnaire (Meyer, Miller, Metzger, & Borkovec, 1990). The 16 items of PSWQ were designed to measure the generality, excessiveness, and uncontrollability char- acteristics of pathological worry. Sample items include “My wor- ries overwhelm me” and “I’ve been a worrier all my life.” Each item was administered on a five-point scale (1 � not at all to 5 � very typical), and the total score ranged from 16 to 80. The scale had a Cronbach’s alpha between .90 to .95 in Meyer et al. (1990), and a value of .91 in the present study.

Results

Anxiety (but not fear) characterized the experimental group according to the compliance checks in the first set of compar- isons presented in Table 2. Specifically, significant group dif- ferences were found in trait worry (PSWQ), trait anxiety (Trait STAI), and state anxiety (State STAI), but not in trait fear (FQ) or state fear (State STAI). In the second set of group compar- isons presented in Table 2, our hypotheses regarding the three dependent variables were supported. The anxious group signif- icantly gravitated toward surveillance cybersecurity policies and favored vigilance behaviors more than the relaxed group. More importantly, the groups were not significantly different in their affinity for avoidance.

Study 4

This quasi-experiment contrasted the cybersecurity preferences from a fearful or a relaxed frame of mind. The study design was similar to Study 3 except that trait fear scores were used as the basis for group assignment.

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Figure 2. Anxious emotion as a function of preexisting trait and mood induction in Study 2. The main effect of trait anxiety was significant, F(1, 292) � 5.73, p � .017, �p2 � .020; the main effect of mood induction was not significant, F(2, 292) � 1.44, p � .239, �p2 � .010; their interaction effect approached significance, F(2, 292) � 2.81, p � .062. Simple comparisons found that participants with different anxious propensities responded similarly to the relaxed mood induction, F(1, 287) � 0.25, p � .619. �p2 � .001; they responded somewhat differently, at a near significant level, to the fearful mood induction, F(1, 287) � 2.83, p � .051, �p2 � .013, and responded significantly differently to the anxious mood induction, F(1, 287) � 9.47, p � .002, �p2 � .032. See the online article for the color version of this figure.

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Method

Participants. The participants were 156 college students (M � 19.97 years, SD � 1.6 years) enrolled in various psy- chology courses in a university in the San Francisco Bay Area. The sample consisted of 78.2% female students and 21.8% male students. All undergraduate class levels were represented, with 27.6% freshmen, 26.30% juniors, 23.7% sophomores, and 21.8% seniors. The ethnic composition was 32.7% Asian, 30.1% Caucasian, 18.6% Latino, 4.5% African American, 2.6% Native American, 1.9% Indian, 1.9% Middle Eastern, and 7.7% various other ethnic origins. A third of the participants were not religious (20.0% spiritual and 17.4% atheists), whereas the remaining two thirds practiced Christianity (52.9%), Islam (3.2%), Judaism (2.6%), Buddhism (1.9%), or other religions (2.0%). Nearly half of the participants claimed they had no political affiliations (42.9%), and the rest were Democrat (41.7%), Republican (7.1%), Libertarian (1.9%), and “other” (6.4%). A great majority of the participants were U.S. citizens (88.0%) and/or were born in the United States. (85.3%).

Procedure. The participants were recruited from psychol- ogy classes on a voluntary basis. In a preliminary questionnaire administered approximately a week prior to the study, the 10 agoraphobia and blood/injury phobia FQ items were included and a cutoff score of 33 was used to assign participants into groups. Trait PSWQ was also administered at this time to check for divergent validity in order to guard against the possibility that the fearful group was also anxious. When the participants arrived at the lab a week later, the fear subscale of Rothbart’s (1986) Temperament Questionnaire was used to verify the group assignment. Two independent coders flagged problematic essays and reached an intercoder reliability of .75. The data from 29 participants were excluded from analysis based on the same criterion as in Study 3. All procedures were approved by the University of San Francisco IRB and were conducted ac-

cording with APA ethical conduct of research with human subjects.

Instruments. Demographics, trait emotions, state emotions and cybersecu-

rity measures. These were the same as in Studies 1, 2, and 3. The Cronbach’s alpha was .80 for FQ, .93 for PSWQ, .91 for state STAI, .92 for State Fear Inventory, .91 for surveillance scale, .90 for vigilance scale, and .86 for avoidance scale.

Fear subscale of Adult Temperament Questionnaire (Roth- bart, 1986). The seven-item scale measures trait fear on a scale (1 � extremely untrue of you to 7 � extremely true of you). Sample items include “Sometimes, I feel a sense of panic or terror for no apparent reason” and “I become easily frightened.” The last item “When I try something new, I am rarely concerned about the possibility of failing” was excluded because its face validity was judged to be more aligned with anxiety than with fear. The Cronbach’s alpha was .76 in Rothbart (1986) and .58 in the present study.

Results

Our compliance checks showed that the experimental group scored significantly higher on trait fear measures (both in terms of FQ and fear temperament) and on state fear measure than the control group (see Table 3). However, the experimental group also showed greater signs of anxiety (in terms of trait PSWQ and state anxiety) than the control group. Among all the compliance checks, FQ had the largest effect size, suggesting that fear was a notable feature of the experimental group.

The results on the three dependent variables were less ambigu- ous and clearly supported our hypotheses. The experimental group endorsed avoidance cybersecurity measures significantly more than the control group. The effect size of avoidance measures is particularly noteworthy: the popularity of avoidance measures was about half a standard deviation higher in the fearful group than in the relaxed group. Moreover, the rest of the results presented in

Table 2 Comparison Between Anxious Group and Relaxed Group in Study 3

Measure

Anxious Relaxed 95% CI

M (SD) M (SD) t(108) LL UL Cohen’s d

Traits

PSWQ 60.82 (4.60) 42.10 (7.68) 15.21��� 16.28 21.16 2.93 FQ 3.61 (1.32) 3.40 (1.39) .82 �.30 .26 .16 Trait STAI 2.39 (.34) 2.13 (.35) 3.74��� .07 .39 .72

State emotions

State STAI 2.39 (.91) 1.91 (.70) 3.03�� .16 .78 .59 State Fear 1.25 (.47) 1.19 (.53) .69 �.13 .26 .13

Action tendencies

Surveillance 2.77 (1.01) 2.38 (.98) 2.01� .00 .77 .39 Vigilance 3.34 (.88) 2.89 (.97) 2.54� .10 .81 .49 Avoidance 2.08 (.73) 2.05 (.69) .25 �.30 .24 .05

Note. CI � confidence interval; LL � lower limit; UL � upper limit; PSWQ � Penn State Worry Questionnaire; FQ � Fear Questionnaire; STAI � State–Trait Anxiety Inventory. � p � .05. �� p � .01. ��� p � .001.

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1360 CHEUNG-BLUNDEN, CROPPER, PANIS, AND DAVIS

Table 3 support the uniqueness claim of functionalists with a negative proof where the fearful group did not gravitate toward surveillance or vigilance measures.

General Discussion

Little is known about how the public minimizes cyber risks at a time when society is increasingly dependent on technology. If history from the start of the 20th century is any indication (Loomis, 2015), decades could go by before any democratic society could settle on a set of safety measures. In the interim, governments and private citizens experiment with new cyber safety measures when their old physical safety measures seem no longer germane. These new behaviors could offer fresh evidence to bear on the functionalist approach to emotions, especially in their ongoing bids to differentiate between fear and anxiety.

Therefore, the first aim in our research was to find the behavioral categories in cybersecurity solutions by surveying the open-ended responses in a pilot study as well as the ongoing congressional debates. Three types of strategies to counter cyberinsecurity have emerged in Study 1. The first two behav- ioral categories pertain to surveillance policies and vigilance behaviors, respectively. The third behavioral category is con- sidered to be avoidance tactics according to the scale’s contents. Two items (“not share personal information online” or “refuse to open some e-mails”) were eliminated from the scale, as participants did not see the coherence between these two items and the prior six items. One explanation for this result is that avoiding high-tech platforms is no longer a viable lifestyle, and thus participants could not realistically endorse all items at once. They may have to choose specific avoidance strategies based on the nature of the threat and their life circumstances. Although most of the behavioral strategies in our study are reminiscent of the safety behaviors from Helbig-Lang and Pe- termann (2010), we did not find threat neutralization or distrac-

tion. The average consumer may be unable to devise technical threat neutralization strategies or enter into a state of denial when cyberattacks are frequently reported in the news.

Our second research aim was to seek the emotional impetus behind each type of cybersecurity strategy. The results of our final two studies found demarcations around avoidance and fear on the one hand, and around surveillance, vigilance, and anxiety on the other hand. It is noteworthy that we aimed to show not only the presence of the proper emotion–action tendency pairs (e.g., fear- avoidance), but also the absence of the improper pairs (e.g., fear-vigilance). Although our final two studies verified the pres- ence of the proper pairs, Study 3 was able to rule out the improper pairs with more confidence than Study 4. A reduction in sample size should be considered in the future to avoid detecting the smaller effects of the improper pairs in Study 4. We started with 156 participants and then excluded 29 essays, making our final sample size 127. Future studies may consider smaller group size than the recommended 60 participants per group.

Our results from the novel domain of cyber threats add to the current bid to differentiate between fear and anxiety in neuro- science and clinical research. These findings bring Barlow’s (2000) theory of anxiety and Woody and Szechtman’s (2011, 2013) security motivation system into the fold of classic func- tionalism, such that anxiety is not just a synonym or an exten- sion of fear but rather a discrete emotion in its own right. The distinct behavioral manifestations of fear and anxiety are given concrete forms in the context of cyber threats. A fearful public would disengage from e-commerce and other high-tech plat- forms, not unlike the mass exodus from physical economic activities post 9/11 when airports were shunned and shopping malls were vacant. An anxiety-ridden public brings a different outlook to America. The implication of expansive governmental power could spell a loss of privacy or even curtailed freedom (Preston, 2014). These projections may or may not materialize, but they illustrate the unique transformational power of mass

Table 3 Comparison Between Fearful Group and Relaxed Group in Study 4

Measure

Fearful Relaxed 95% CI

M (SD) M (SD) t(125) LL UL Cohen’s d

Traits

PSWQ 52.22 (11.42) 46.37 (13.95) 2.09� .26 9.44 .38 FQ 47.56 (9.60) 21.83 (6.78) 16.89��� 22.71 28.76 3.50 Fear Temp 4.13 (.72) 3.82 (.81) 2.24� .20 .83 .59

State emotions

State STAI 2.27 (.89) 1.87 (.72) 2.73�� .11 .70 .54 State Fear 1.67 (.73) 1.29 (.40) 3.24�� .10 .55 .74

Action tendencies

Surveillance 2.72 (.88) 2.51 (.89) 1.26 .12 .53 .23 Vigilance 3.31 (.95) 3.01 (.90) 1.81 .03 .63 .33 Avoidance 2.93 (1.01) 2.56 (.97) 2.05� .01 .73 .58

Note. CI � confidence interval; LL � lower limit; UL � upper limit; PSWQ � Penn State Worry Questionnaire; FQ � Fear Questionnaire; Fear Temp � Fear subscale of the Adult Temperament Questionnaire; STAI � State–Trait Anxiety Inventory. � p � .05. �� p � .01. ��� p � .001.

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fear and mass anxiety, and give purpose to the quest to differ- entiate between the two threat-induced emotions.

Methodological Challenges

We used a variety of study designs from cross-sectional to experimental, but neither extreme was conducive to the endeavor of differentiating two closely related emotions. The findings from our cross-sectional study were similar to those of Perkins and colleagues (2010), where fear showed a unique action tendency, but anxiety did not (Perkins & Corr, 2006). A cross-sectional design typically controls for one emotion in order to reveal the uniqueness of the other emotion. Statistically carving out fear from anxiety is merely an approximation of the neural overlap between the two emotions and does not fully capture the complex neural interaction (e.g., Tovote et al., 2015).

Our experimental design was also inconclusive due to a differ- ent methodological challenge. The efficacy of eliciting target emo- tions was low, despite the threefold induction procedure involving a behavioral task, a film clip, and an essay writing segment. Effective emotion induction relies on participants to transfer their moods from one situation to the next. This mechanism is sensitive to a variety of factors such as the emotional nature, compatibility, and placement of the situations. Göritz and Moser (2006) found small effects if the mood induction procedures are conducted online. Even though our mood induction was embedded in a survey, participants completed the survey in supervised lab ses- sions. Future methodological research is needed in order to under- stand the parameters around using films, vignettes, and imagery to induce emotions in participants at remote locations (Hernandez, Vander Wal, & Spring, 2003; Rosenberg & Ekman, 2000; Stem- mler, 2003; Västfjäll, 2001).

The optimal research strategy seems the middle ground be- tween cross-sectional design and experimental design—a quasi- experiment that combined trait screening and mood induction (Reinholdt-Dunne et al., 2012; Ruscio & Borkovec, 2004; van Uijen, van den Hout, & Engelhard, 2017; White et al., 2016). According to the post hoc analysis from Study 2, trait screening alone was a sufficient condition to precipitate a target emotional response. Trait screening seems to be able to gauge a partici- pant’s habitual appraisal style and promise the same emotional experience to a new threat scenario. Although it was not abso- lutely necessary to follow the trait screening with a mood induction, the additional procedure enhanced the effect (or divergent validity) that is necessary in a study of two closely related emotions. The post hoc analysis in Study 2 verified that a participant was best able to experience a target emotion if they had a predisposition for it and then underwent a corresponding mood induction.

Conclusion

The current American congressional debate around cybersecu- rity and the public uproar against the National Security Agency’s mass surveillance may be the harbinger of a protracted discussion of how to keep ourselves safe in cyberspace. Competing cyberse- curity solutions should be debated based on rational logic rather than being hijacked by emotions. Both fear and anxiety are com- mon reactions to cyber threats. If fear were to take hold, economic

participation would suffer and tarnish commercialism. If anxiety were to take the rein, surveillance policies would win out in the public’s mind and decrease freedom. Materialism and freedom are the defining features of the American way of life. A future Amer- ica without materialism is very different from a future America without freedom. The difference between the two visions is not minute by any account; neither is the functional difference be- tween fear and anxiety.

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Received February 2, 2018 Revision received July 10, 2018

Accepted July 18, 2018 �

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