The purpose of the study was to investigate cross-language effects in verbal fluency tasks where participants name in English as many exemplars of a target as they can in one minute. A series of multiple regression models were used that employed predictors such as self-rated proficiency in English, self-rated proficiency in a language other than English, a picture naming task used to measure productive vocabulary, the percentage of English use, and the frequency of language switching. The main findings showed that self-rated proficiency in the non-English language accounted for unique variance in verbal fluency that was not accounted for directly by self-rated proficiency in English. This outcome is consistent with cross-language interference, but is also consistent with an account that assumes bilingual disadvantages in verbal fluency and picture naming are due to bilinguals having weaker links between semantic concepts and their phonological form. The present study is also discussed in terms of a broader framework that questions whether domain-general inhibition exists and also whether it plays an important role in bilingual language control. Both category fluency  and letter fluency  were tested in English. Like the vast majority of studies  language should predict unique variance in verbal fluency performance apart from that accounted for by the degree of proficiency in English.The weaker links account of bilingual disadvantages in picture naming and verbal fluency would make the same prediction, namely, that L2 proficiency has a negative effect on L1 verbal fluency and accounts for unique variance beyond that accounted for directly by L1 proficiency. The weaker links account relies on the cumulative history of language use prior to a verbal fluency test. That is, using L2 steals time from using L1 and results in L1 links that are weaker in bilinguals compared to monolinguals. Furthermore, as L2 use increases (and L2 proficiency improves) the strength of the L1 links will lag further behind those for monolinguals. Thus, the weaker links perspective also predicts that increases in L2 proficiency will be associated with decreases in L1 verbal fluency—not because activated L2 representations compete, but because L2 practice comes at the expense of more L1 use.A problem with testing this prediction is how to obtain a pure measure of lexical knowledge in both English and the Other language that is not influenced by additional factors. To advance the argument, assume, for now, that self-ratings by bilinguals for general proficiency in English and their Other language provided reasonably valid measures of lexical knowledge   correlatAlthough bilingualism researchers routinely use self-ratings of proficiency in understanding, speaking, and reading  these seThe current study reports new analyses of an existing dataset and, consequently, many additional details are presented in the original study .Participants in  were 236In the verbal fluency tasks, participants were asked to produce as many words as they could in one minute that followed the prescribed rule. There were two trials of single-letter fluency  and two trials of single-category fluency . The target categories were selected on the basis of previous work with the intent to have targets of comparable and intermediate difficulty. They were not explicitly told to respond in English, but all participants did so. Between trials, bilinguals would occasionally mention that they thought of the name of a vegetable or instrument in their other language, but did not know it or could not think of the word for it in English.For completeness, we show the mean number of correct responses, the number of intrusions from an incorrect category, and the mean number of repeated responses for each of four verbal fluency tasks given to each participant. Our analyses focus on the single-category and single-letter trials. Inspection of t(127) = 1.02, p = 0.311. Thus, in all subsequent analyses any reference to verbal fluency refers to the composite mean based on the two categories (instruments and vegetables) and the two letters (“F” and “A”).The distinction between category- and letter-fluency has been of theoretical interest because of the twin hypotheses that there is a bilingual advantage in executive functioning (EF) and that letter-fluency may require more EF. If both hypotheses were correct, then any bilingual disadvantage in category fluency might be reduced in the letter-fluency task because bilinguals could recruit their superior EF to close or eliminate the performance gap. Although some earlier studies reported a Group  by Task (category fluency versus letter fluency) interaction with just such a pattern, Paap et al.’s review  showed tThe primary hypothesis of this study is that Other language proficiency can account for unique variance in an English-language test of verbal fluency when English language proficiency is included in the regression model. Model 1. English Proficiency and Other Proficiency are the two predictors. Tested as a single bivariate correlation, it is not surprising that self-rated proficiency in English predicts the number of correct responses in a test of English verbal fluency, r(128) = +0.27, p = 0.001. In regression Model 1, the two predictors combined to account for about 16.4% of variance in the composite verbal fluency scores, R = 0.406, p < 0.001). The standardized coefficients showed that both were significant predictors, β = +0.18, p = 0.033 for English fluency and β = −0.32, p <0.001 for Other Proficiency. As predicted, the correlation for the test language (English) was positive, but negative for the competing language (Other). Observing a significant negative effect of Other Proficiency is consistent with the hypothesis that there is cross-language interference. Impressively, it is numerically more potent than proficiency in the test language. Given that English Proficiency and Other Proficiency are correlated with one another, r(128) = −0.27, p = 0.001, it is prudent to assess collinearity. Following the guidelines provided by Field [Model 2. Many of our bilinguals  do not write or read their non-English language. In our past publications, we have considered proficiency in spoken language and have ignored reading. In this past work, if we had included reading and writing in our measure of other-language proficiency, then some native and highly fluent speakers would have mediocre proficiency scores even though they were native speakers and listeners. Thus, the analyses in the original paper [r(125) = +0.57, p < 0.001. However, does Other Proficiency still account for unique variance in verbal fluency when both written and spoken proficiency are included in the model? Yes, self-related proficiency in the Other language accounts for unique variance in English verbal fluency even when both spoken English proficiency and reading English proficiency are included in the model, β= −0.31, p < 0.001. Thus, Model 2 (like Model 1) is consistent with the hypothesis that cross-language effects play a significant role in verbal fluency.al paper  and in MModel 3. Researchers in bilingual language control have emphasized the importance of several facets of current use and these include the relative use of each language and the frequency of language switching [p = 0.117 and for Frequency-of-Language Switching, β= +0.22, p = 0.09. Again, the most important result is that Other Proficiency accounts for unique variance even when English Proficiency, percentage of current English use, and frequency-of-switching are taken into account, β= −0.35, p = 0.004.witching ,7. To thModel 4. The original dataset includes another measure that is arguably more directly related to English vocabulary knowledge and that is the number of correct responses on the Multilingual Naming Task (MINT) [p < 0.001. Furthermore, Other Proficiency does not account for any unique variance not accounted for by MINT, β= −0.10, p = 0.206.k (MINT) . Model 4Model 5. Model 4 potentially upsets the apple cart. It clearly calls into question the conclusion that the earlier models implicate cross-language interference as Other Proficiency does not account for unique variance when MINT scores are the other predictor. This might be expected if MINT scores were a more valid measure of vocabulary knowledge than self-ratings of general English proficiency. On the surface, this seems reasonable as self-ratings are often viewed as subjective, fuzzy, and open to bias. Furthermore, picture naming (MINT) is an objective measure that enjoys face validity as a measure of productive vocabulary. Perhaps the independent contribution of Other Proficiency observed with Models 1 to 3 was enabled by a mediocre measure of English vocabulary, namely, self-rated English Proficiency.p < 0.001.Although the preceding argument seems quite plausible, it fails to consider a potentially critical aspect of picture naming , namely that picture naming may also be influenced by cross-language interference. If MINT performance already reflects cross-language interference, then there would be little or no unique variance for Other proficiency to account for when predicting verbal fluency. If this is the case, then Other Proficiency should strongly predict MINT scores when they trade places compared to Model 4. Thus, Model 5 used English Proficiency and Other Proficiency as predictors, but uses MINT scores as the outcome variable. The key result is that Other Proficiency accounts for unique variance in MINT scores, β= −0.33, The first three regression models established that Other proficiency accounts for unique variance in English verbal fluency beyond that accounted for by English proficiency. This implicates a cross-language effect in verbal fluency in a single-language context. However, Model 4 replaced self-rated English proficiency with MINT English scores and the inclusion of Other proficiency no longer improved the model. If the outcome of Model 4 was simply due to MINT being a more reliable measure of English proficiency compared to self-ratings, then this would undermine the evidence for cross-language effects obtained in Models 1 to 3. Alternatively, we suggested that MINT may not be a more reliable measure, but rather one that is, itself, influenced by cross language interference. If MINT scores are a composite of benefits from greater English proficiency and cost from greater Other proficiency, then self-rated Other proficiency is less likely to account for additional unique variance in English verbal fluency. The coherence of this account rests on the assumption that self-rated English proficiency is less effected by Other proficiency (and hence Other proficiency can scoop up variance unaccounted for by English proficiency) but that MINT scores are influenced by Other proficiency. We consider next the plausibility of this account by speculating on how bilinguals self-rate their proficiencies and linking these speculations to work on self-ratings recently published by Tomoschuk and colleagues .The subjective experience of proficiency or fluency for a bilingual could rely on three different frames of reference. One framework for a bilingual is to compare fluency in one language to that of the other. Tomoschuk et al. tap into this type of comparison when they explicitly ask bilinguals to rate the degree to which one of their languages is stronger and conclude that these dominance ratings are more closely aligned with their MINT scores than numerically rating individual languages on a seven-point scale. Thus, in order to explain the difference between absolute ratings of proficiency and dominance ratings, one must conclude that self-ratings do not heavily rely on the relative comparison of fluency in one language compared to the other.bilinguals who have English as a native language”“My dominant language is English and I am very fluent AND just as good as other . However, we know that, on average, there are bilingual disadvantages compared to monolinguals in verbal fluency and picture naming. Thus, self-reflecting on comparisons to other bilinguals will lead to an overestimation of proficiency compared to MINT. We suggest that the overestimate is caused by MINT’s sensitivity to cross-language effects and the absence of such effects when other bilinguals serve as the frame of reference for self-ratings. This agrees with Tomoschuk et al.‘s assertion that “One possible explanation for this pattern of results is that different participants have different frames of reference that they use to evaluate their language proficiency” p. 532.A second source of information about proficiency would be a bilingual’s perception of their proficiency relative to others. If the others are also bilinguals, the effects of cross-language interference may be neutralized. For example, the feeling that “My dominant language is Chinese and I am very fluent, but not quite as good as my peers who speak only Chinese.” Considering this evidence should reduce the tendency to overestimate Chinese proficiency. In support of this argument, Tomoschuk et al. show striking differences between Chinese–English bilinguals raised in the USA compared to a group who immigrated to the USA relatively recently. The recently immigrated group  self-rated their Chinese fluency in closer alignment to their MINT scores, in other words, they had lower self-ratings. Tomoschuk et al. suggest that recently immigrated speakers are comparing themselves to family and friends in China, many of whom are native eiChinese speaking monolinguals who, by definition, have not been adversely influenced by acquiring or using an L2.Only if bilinguals have rich opportunities to compare their performance in their dominant language to the performance of monolingual speakers of that language would they have evidence that their proficiency was, on average, less than monolinguals. For example, from the perspective of a bilingual: unique variance in verbal fluency for L2 proficiency to account for. Because the evidence drawn from Tomoschuk et al. is somewhat circumstantial, we emphasize that we are speculating, not claiming, that the self-ratings of our bilinguals are dominated by adopting a frame-of-reference of other bilinguals. It may be worthwhile to explore the possibility that bilinguals could be instructed to use the three different frames of reference and generate reliability different self-ratings.A conjecture offered here is that self-rated English proficiency in a bilingual is a weighted average of: (1) comparing my languages to each other, (2) comparing my proficiency to that of other bilinguals, and (3) comparing my proficiency to that of monolinguals. The first two will lead to an overestimation of MINT scores that are caused by failing to take adverse cross-language effects into account. This provides a valid interpretation for Model 1: Other proficiency predicts unique variance in English verbal fluency beyond that directly accounted for by English proficiency. This would not be the case if self-ratings of English proficiency fully reflected the adverse effects accruing from an L2. If so, there would be no One account of the bilingual disadvantage in verbal fluency is cross-language interference. This is intuitive if one thinks of Model 1 as reflecting online causal relationships during a verbal fluency task. That is, what happens when a bilingual is asked to generate names of vegetables? At some point, attention may gravitate toward the concept for CUCUMBER, but the word form “pepino” may compete with the word form “cucumber” and render “cucumber” inaccessible. Thinking of Model 1 as an online model of verbal fluency, English Proficiency reflects the strength of English word forms and Other Proficiency the strength of competing words from the non-target lexicon. Thus, cross-language interference will increase as Other Proficiency increases.In contrast, Model 1 can be thought of in terms of the lexical structures that have been acquired over the bilingual’s lifetime and not-so-much as a description of online processing. This opens the door to explanations of the bilingual disadvantage in verbal fluency that appeal to weaker links rather than interference. Model 1 showed that Other Proficiency degrades verbal fluency. Although this could be due to interference, the “damage” could have been caused by past language experience. That is, if a bilingual is a native speaker of Spanish, acquires English when she starts school, and uses mostly Spanish at home and with friends, then she accrues far less experience in traversing links like the one from CUCUMBER to “cucumber” compared to a monolingual English-speaking peer. The weaker link alone may render “cucumber” inaccessible without suffering direct inhibition or suppression at the hands of “pepino”. The relationship between English Proficiency and Other Proficiency may reveal the importance of the history between the two measures rather than their interaction at the time of testing. That is, Other Proficiency is associated with lower verbal fluency not because it interferes more with finding correct English words, but because those individuals with higher proficiency, in say Spanish, have been practicing Spanish at the expense of English and, consequently, have forged weaker English links. Thus, although Model 1 shows that Other Proficiency accounts for unique variance in English verbal fluency apart from that directly accounted for by English Proficiency, there is nothing in the data considered to this point that rules out an explanation based on weaker links.The most direct evidence supporting the weaker links interpretation is that the bilingual disadvantage in picture naming is greater when producing low-frequency than high-frequency picture names . As niceHowever, in contrast to the above, when translation equivalents (that are not cognates) exist in both lexicons, low-frequency words may be more vulnerable to interference than high-frequency words. For example, an English dominant bilingual growing up in the self-proclaimed artichoke capital of the world  may know “alchachofa” but suffer intense interference because “artichoke” has very high frequency. All things considered, it is not clear (at least to us) that cross-language interference in picture naming  should be more prevalent for high-frequency picture names compared to low-frequency, especially when a non-target competitor enjoys a frequency advantage over the target. It appears that both weaker links and cross-language interference continue to compete as explanations for the bilingual disadvantage in verbal fluency. Of course, they could both contribute to the overall effect.Given its extraordinary influence in bilingualism research, it is worthwhile to consider our results in the context of Green’s Inhibitory Control Model (ICM) . The ICMHowever, the ICM makes an interesting and more specific assumption, namely, that the actual inhibition is not applied until competition for production occurs. Consider a picture-naming task where a pre-cue indicates whether the upcoming picture should be named in English or Spanish. When the pre-cue requires a switch, say from Spanish to English, the new schema (“name-in-English”) must be selected and this sets the stage for the global inhibition of all the lemmas with Spanish tags. However, the actual inhibition is not applied until: (1) the picture appears, (2) the participant initiates plans to name the picture, and (3) response competition is detected. For example, if the stimulus is a picture of a CAT, then the competition between the correct response “cat” and the competitor “gato” will (4) trigger inhibition of all Spanish lemmas in proportion to the degree that they were activated by the picture. Thus, “cat” would be inhibited more than unrelated English words like “table”. In summary, Green’s ICM assumes that competition between the lexicons is controlled via inhibition and that inhibition is reactive, that is, in proportion to the amount of competing activation that needs to be neutralized.What does the ICM predict for verbal fluency? If the task language is English and the memory search starts to hone in on a potential exemplar  then “cat” and “gato” will compete. If the task schema is “speak-English”, then all the Spanish lemmas will receive inhibition, but “gato” will receive the most because CAT has led to the coactivation of both “cat” and “gato”. However, the cross-language interference account of the bilingual disadvantage in verbal fluency relies on the inhibition flowing in the opposite direction, namely, from “gato” to “cat”. That is, might there be occasions when “gato” gets the jump on “cat” (despite the fact that the “speak-English” schema is selected) and suppresses “cat” to the point where it is inaccessible? Although it is easy to imagine that the initial competition delays the production of “cat”, it seems far less likely that “gato” can completely block the access of “cat”. Thus, we argue that the interference account of the bilingual disadvantage in verbal fluency is not very compelling within the framework of Green’s ICM.“stop thinking about inhibition” in that way. They required each participant to complete six tasks assumed to reflect Inhibition of Prepotent Responses and five assumed to reflect Resistance to Distraction . Rather than examining only the fit of various models, Bayesian hypothesis testing was also conducted. These additional tests showed that the data are ambiguous as to: (a) whether there is one inhibition factor or two and (b) if there are two factors, whether they are correlated or not. Another problem pointed out by Rey-Mermet et al., both in their data and in other latent-variable analyses of the inhibition construct, is that for each latent variable, the loading for one task tends to dominate the others. Consequently, each latent variable represents mainly the variance of one task with the remaining tasks burdened with high error variances. These problems led Rey-Mermet et al. to suggest that all latent-variable models of the inhibition construct have poor explanatory power. From their view of this evidence, Rey-Mermet et al. suggested that nonverbal interference tasks do not measure a common underlying latent variable associated with general inhibitory-control, but rather a highly task-specific ability to resolve the type of conflict instantiated in each task.Inhibition is usually viewed as a domain-general ability to suppress competing or irrelevant information during goal-directed behavior. Recently, Rey-Mermet, Gade, and Oberauer  have urgIn Opposition to Inhibition, provided alternatives to inhibition for several classic demonstrations of “inhibition” including inhibition of return (IOR), Stroop interference, and negative priming. This begs the question: Is inhibition needed to account for bilingual language control?If inhibition is task specific, as argued by Rey-Mermet et al. (2018), then there would be no basis for a bilingual advantage in domain-general inhibitory control  is always in one language and the lexical decision (the probe event) in the other language.The evidence for local inhibition accrues from the presence of negative priming (NP) and this reliably occurs when the prime event is in L1, the probe event in L2, and the prime distractor  becomes the probe target . Lexical decisions to cat are slower when CAT was the distractor compared to an unrelated control. To be more specific, in order for NP to occur (according to the interference account) CAT must be automatically activated despite the intention to ignore it. If CAT becomes sufficiently activated, then it will compete with the to-be-named target  and will trigger reactive inhibition of CAT to resolve the conflict. If the inhibition of CAT persists into the probe event and cat appears as the probe target, then the suppressed activation will result in a slower lexical decision compared to a control trial.Although inhibition is an intuitively appealing explanation for NP, it is one of several phenomena  that MacLeod and colleagues  showed cThe critical contrast in Neumann’s experiments is that NP occurs when the consistent order of tasks is L1 (naming) then L2 , but not the reverse. This is what would be predicted from the interference perspective if inhibition is reactive and in proportion to need. If an L1 distractor is harder to ignore (more automatic) than an L2 distractor and is more highly activated, then it will receive more inhibition. If the inhibition actually drives the activation of the distractor below the normal baseline, then it will take longer to make the lexical decision when that distractor becomes the probe target. Indeed, NP occurs in the L1-then-L2 order. However, if the order is L2-then-L1 the amount of inhibition applied to the distractor in the initial naming task should be less because less is needed. If the amount is insufficient to carry over into the probe task, then significant NP may not occur.This interference account of NP is usually presented as intuitive and fully following from Green’s ICM. As an aside, we suggest that in the absence of a computational model, there are implicit parameter decisions that are needed to predict the L1/L2 asymmetry and these may not enjoy high plausibility. To illustrate, consider the assumption that the amount of inhibition is in proportion to need. If this could be precisely titrated, then “need” might be defined as the amount of inhibition needed to return the distractor to the normal baseline. If L1 distractors are driven back down to baseline by substantial inhibition and L2 distractors driven to baseline by more moderate amount of inhibition, then it would not matter if the prime task used L1 or L2. In fact, there would be no negative priming because L1 distractors, L2 distractors, and control distractors would all be at baseline at the start of the probe trial. Thus, we suggest that an interference account of NP requires that inhibition is applied proportionately and beyond what is needed such that L1 distractors are driven below normal baseline and L2 distractors just to baseline. Thus, the interference account of NP may be more of a “just so” story than generally acknowledged.Neumann and colleagues assert that their between-language findings “undermine” episodic retrieval models, in part, because they do not predict the L1/L2 asymmetry in NP effects. This is true if you restrict the episodic retrieval models to a single parameter specifying whether the response codes either match or mismatch. However, just like interference models, there are many implicit assumptions that could be instantiated in different versions of a computational model. Consider for example the formation of an event file consisting of a stimulus and the response to that specific stimulus. What governs the probability of encoding and storing an episode or the strength of that memory trace? The distractors are located just above or below the targets and are likely to be encoded even if the participant’s intention is to ignore them. However, it would seem reasonable to propose that the likelihood or strength of distractor activation and its episodic trace would vary as a function of its salience and its propensity for automatic coding. In this framework, L1 distractors should be advantaged compared to L2 distractors just as L1 words generate more Stroop interference compared to L2 words. We suggest that it is not easy to judge which theory has the most parsimonious explanation for the observation that NP occurs in the L1-then-L2 order, but not the reverse.“a surprisingly elusive phenomenon” (p. 274) when primes are in the L2 and targets are in the L1 , but not NP. Based on translation priming in simpler tasks not involving distractors, PP would not have been predicted. As Grainger and colleagues  pointed see also ).Neumann and colleagues do not dwell much on why PP occurs in the L2-then-L1 order for their task, but rather seek to explain why PP disappears in the L1-then-L2 order. Their account invokes a second type of inhibitory control, namely, proactive suppression of the language used in the prime task. This follows from the assumption that the participant knows that the probe task will always be in the other language and that an excellent strategy would be, at the onset of the LD task, to globally inhibit the language used in the naming task. However, if complete and global suppression of the previously used language was possible, why would participants not engage in proactive inhibition in the L2-then-L1 order as well? If proactive inhibition was effortful, it would even require less effort to fully suppress L2.These questions aside and switching to the other perspective, it is clear that episodic retrieval models would not predict the absence of PP in the L1-then-L2 order based on the simple notion of response codes that either match or mismatch because they do match. However, perhaps it is the presence of PP in the L2-then-L1 order that deserves additional scrutiny given its rarity in other translation priming tasks. The fact that the probe task is LD may impose limits on the generality of the findings because the LD task is rather infamous for its vulnerability to strategic influences on the criteria used to classify the target string as a word or nonword ,25.In summary, the series of experiments by Neumann and colleagues offers a tantalizing pattern of results that follow from one instantiation of an interference account of bilingual language control. However, caution is recommended before assuming that the “inhibitory control” reflected in NP is an instance of domain-general inhibitory control. An individual differences examination would be useful ,27. We kr(122) = −0.072, p = 0.43. Thus, there is no evidence from this test that bilinguals with better inhibitory control (and presumably better able to inhibit the interference from the Other language) have better verbal fluency as one would expect if cross-language effects in verbal fluency were, indeed, caused by interference. Furthermore, it is worth noting that switch costs did significantly correlate with MINT scores, r(128) = −0.223, p = 0.01, but not with self-rated English proficiency, r(138) = −0.043, p = 0.62. This pattern is consistent with the hypotheses that inhibitory control affects performance in MINT, but does not influence self-ratings of English proficiency. These types of relationships could be productively studied in future work.An individual differences approach could also be used in a replication of the current study by including a measure of inhibitory control and then correlating that measure with verbal fluency. If a domain-general measure of inhibitory control predicted verbal fluency, this would support the interpretation that part of the bilingual disadvantage in verbal fluency was caused by interference from the other language. This is a challenging recommendation given the controversy regarding the construct of inhibition and claims that there may not be a domain-general inhibitory ability. Be that as it may, our original study did include three different cued switching tasks and the switch costs from those tasks correlated with each other, thus demonstrating convergent validity. Furthermore, switch costs are generally assumed to be caused, at least in part, by having to inhibit the task schema active on the previous trial on “switch trials” in contrast to “repeat trials”. In order to explore possible associations between measures of inhibitory control and verbal fluency, we correlated the switch costs in the color-shape switching task with total verbal fluency, but the correlation was not significant, There are small but fairly consistent bilingual disadvantages in picture naming and tests of verbal fluency. These disadvantages could be due to cross-language interference or weaker-links between semantic concepts and their phonological form. Both hypotheses lead to the prediction that the degree of proficiency in the nontarget language should be negatively correlated with and account for unique variance in verbal fluency performance apart from the direct effects of degree of proficiency in the target language. Although several regression models were presented that are consistent with this prediction, there are potential complications having to do with the measures of vocabulary knowledge and the degree to which different measures  are sensitive to adverse cross-language effects. In summary, the regression models showing that Other Proficiency is a potent predictor of verbal fluency cannot rule out the alternative explanation that the effect of Other Proficiency is simply due to past language usage that has led to weaker links in the test language.Considering explanations of the bilingual disadvantage in picture naming and verbal fluency that do not involve inhibition led to a general discussion of recent claims that inhibitory control may not be a domain-general cognitive ability and, even more radically, that inhibition may play no role in cognitive control (see  for a mo


With the rapid deployment of mobile technologies and their applications in the healthcare domain, privacy concerns have emerged as one of the most critical issues. Traditional technical and organizational approaches used to address privacy issues ignore economic factors, which are increasingly important in the investment strategy of those responsible for ensuring privacy protection. Taking the mHealth system as the context, this article builds an evolutionary game to model three types of entities  under the conditions of incomplete information and bounded rationality. Given that the various participating entities are often unable to accurately estimate their own profits or costs, we propose a quantified approach to analyzing the optimal strategy of privacy investment and regulation. Numerical examples are provided for illustration and simulation purpose. Based upon these examples, several countermeasures and suggestions for privacy protection are proposed. Our analytical results show that governmental regulation and auditing has a significant impact on the strategic choice of the other two entities involved. In addition, the strategic choices of system providers and hospitals are not only correlated with profits and investment costs, but they are also significantly affected by free riding. If the profit growth coefficients increase to a critical level, mHealth system providers and hospitals will invest in privacy protection even without the imposition of regulations. However, the critical level is dependent on the values of the parameters (variables) in each case of investment and profits. Recently, the significant advances in Internet and mobile communications have had a great impact on wireless networks and mobile applications . By enabCurrently, there are various data protection regulations and cyber security laws in both developed and developing countries. For example, the General Data Protection Regulation (GDPR) is a regulation in EU law on data protection and privacy for all individuals within the European Union (EU) and the European Economic Area (EEA). The GDPR aims primarily to give control to individuals over their personal data and to simplify the regulatory environment for international business by unifying the regulation within the EU . In ChinIn the academia area, the bulk of research on privacy issues in mHealth systems is mainly focused on organizational and technological solutions . HoweverThe weakness of traditional solutions to privacy concerns is their lack of a quantitative decision framework. Game theory is a branch of applied mathematics that formalizes strategic interaction among autonomous agents, which can provide a mathematical framework for modeling privacy problems in which multiplayers with contradictory or collaborative goals are considered . FurtherExisting research on game theoretic approaches primarily considers the interaction of two players or two types of players under a competitive scenario . HoweverEvolutionary game theory forgoes a typical assumption of classical game theory: rationality. Instead, evolutionary game theory, supposes that game players (entities) are naïve optimizers operating under imperfect information. Players can adapt their behavior on their immediate context . EntitieWe review and summarize the features and weaknesses of existing game theoretical approaches to study privacy related issues.We build an evolutionary game model to formulate cooperate interactions and bounded rational confrontation among system providers, hospitals and governments in mHealth system.We analyze the different solutions obtained from evolutionary equilibrium and interpret different outcomes on how they may benefit decision makers.We construct simulation experiments to prove the usefulness of our proposed model.This paper applies an evolutionary game theoretic approach to modeling and analyzing the optimal investment strategy of privacy protection. We take the mHealth system as the context, which involves three types of entities: system providers, hospitals and governments. To begin with, we review some assumptions and define several parameters of the evolutionary game model. Then, we analyze the stability of the model, and define reasonable codes of conduct for each player. Furthermore, we provide numerical examples for illustration and to verify the mathematical model. Finally, we suggest several countermeasures that could be used to improve the development of privacy protection in the mHealth system. The main contributions of this paper are as follows:The rest of this paper is organized as follows: in Privacy has become one of the most important research issues in the information age . PrivacyOrganizational research is often characterized or related to organizational culture , privacyDespite being quite comprehensive, the organizational and technological methods of privacy protection have disadvantages and limitations. Effective privacy protection in mHealth systems requires a substantial investment by system providers, hospitals, governments and patients, all of whom also want to maximize their benefits. However, traditional methods ignore the economic factors, such as credit loss, investment costs and expected profits, all of which are important for independent entities attempting to make the optimal strategic decision. Investment decisions in this context lead researchers to use game theoretic approaches to allocate limited resources, model benefit interactions, and take into account the underlying incentive mechanisms .Game theoretic approaches have been proposed by many researchers to improve network security and privacy. The application of game theory in network security and privacy can be classified into several categories: applications for analysis of network attack-defense , applicaWith increasing interdependence, each firm free rides by investing less, and suffers lower profit, while the attacker enjoys higher profit. Therefore, information sharing and cooperation among firms can increase the level of information security; this is consistent to the finding of . In 41]41], the Considering attacker behavior and leakage costs, the relationship between security investment and information sharing are further discussed. Their findings showed that firms should devote significant attention to their relationship with other firms when strategically choosing security investment . By usinAs shown in To distinguish this study from the models in existing works, we propose a parametric evolutionary game model. Our model consists of system providers, hospitals and governments, and can be used to analyze the optimal strategies of privacy investment in a mHealth system. Our evolutionary game theory (EGT) model which contains three types of entities, can potentially find several realistic stable strategies after recursive interactions. This study is designed to fill the gap in existing literature by exploring strategies of privacy investment. Our study also examines the impact of profit growth coefficients, investment costs, reputation profits and fines on the strategic choice of the participantsAs well known, there is a large population in China, and the healthcare resources are in short supply . As a ty(1)Insufficient budget is viewed as the main challenge for sustaining privacy investment because of the long return period and high investment costs.(2)Because of information asymmetry and market dynamics, system providers and hospitals are not sure that privacy investment can provide competitive advantages. Therefore, they adjust their strategic choice frequently for profits maximization.According to analysis above, an effective mechanism for mHealth privacy protection requires moderate investments of different entities, which may include technological research, devices purchase, organizational restructure, staff training, etc. Therefore, mHealth system providers, hospitals and governments have very important positions in mHealth privacy protection, while the factors that influence investors’ strategic choice should be deeply investigated. With these initial impressions, we went to Jiangsu Province Hospital of TCM, who has well-established mHealth systems in Nanjing, China and interviewed the hospital dean in charge of financial budgets, the manager in charge of mHealth service, several doctors and patients. In addition, we also went to iFLYTEK, a highly regarded mHealth system provider and interviewed the manager in charge of financial budgets and market, several system engineers and the relevant personnel of mHealth system. Through investigation, we found there were two outstanding problems of privacy investment:Concerning the first problem, the basic cost-benefit analysis is essential for both system providers and hospitals ,53. EconHowever, concerning the second problem, system providers, hospitals and governments are bounded rationality because of incomplete information and dynamic investment process. It is difficult for investors to achieve an optimal strategy in a single game process by using Bayesian game, Stackelberg game and differential game theoretic approaches. Instead, they always adjust and improve their strategic choice for profits maximization. This characteristic is consistent with the evolution of nature, which motivate us to use evolutionary game theory.(1)Equilibrium solution refinement. The evolutionary game approaches provide a refined solution that ensures stability of a strategy adopted by a population, where no small subgroup of deviants could successfully invade the whole population. Such strategy is known as evolutionary stable strategy (ESS) .(2)Bounded rationality. In traditional game theory, the game players are assumed as rational and the players believe that the other side is also rational throughout the game. However, this assumption is often unrealistic. This situation is avoided in evolutionary game, where players adopt dynamic strategies that lead them to sustain in the population without caring about instant profits maximization .(3)Game dynamics. Since players in evolutionary game interact with each other for multiple rounds by adopting different strategies, the state of their interaction varies over time according to the replication games. Thus, the evolutionary game provides a natural way to introduce dynamics, where success strategies are imitated by others and propagate over interaction rounds .Considering the decision problems of mHealth privacy investment, this paper applies evolutionary game theory to model such situations. We investigate the optimal strategies of privacy investment in mHealth context not only based on cost-benefit analysis, but also from evolutionary perspective. The motivation of using evolutionary game theory can be concluded as follows:(1)Offenders. Offenders maliciously use their computer/security skills to steal, exploit, and sell patient’s data. They always attempt to find and exploit weaknesses and vulnerabilities in security systems for illegal profit.(2)Defenders. Defenders in a mHealth system include system providers and hospitals. System providers are technology providers  that can provide support to secure mHealth devices, databases and software for hospitals. Whether or not they will invest in privacy protection is dependent on the profits from and costs of those investments. Hospitals collect patient data from various sources, and perform other operations such as deleting, editing, and sharing data. The security level of patients’ data is positively related to the level of privacy management and the regulation of hospitals, which are also required to make sufficient investment.(3)Patients. Patient’s data that is collected by mHealth devices will be incorporated into an electronic record that is stored in hospital databases. The electronic record is sought by and may potentially be shared with medical experts, caregivers, academic researchers and public health organizations [izations . To prev(4)Regulators. Regulators in a mHealth system refer to third-party organizations, such as governments. Under the current profit condition of privacy investment, governments should motivate system providers and hospitals to invest in privacy protection via punishment and compensation mechanisms.The privacy concerns of a mHealth system involve several types of entities, including offenders, defenders, patients and regulators. Their roles in privacy protection of mHealth system are described as follows:The relationship of the above entities is shown in S), hospitals (denoted by H) and governments With the rapid development of information and communication technology (ICT), personal health information in a digital format can be conveniently copied, transmitted, and integrated. Under this scenario, malicious offenders can easily use their professional skills to steal, exploit, and sell patient’s data for illegal benefits. According to our interview in Jiangsu Province Hospital of TCM and other hospitals, we find that patients are willing to pay more for the improved privacy protection of mHealth service for the reason of increased security/privacy incidents and the improvement of privacy awareness.(2)Under current technical conditions, the investment cost is so high that the three types of game entities cannot make the optimal strategic decision at the initial stage. They have bounder rationality, and cannot make a choice that maximizes their own profits. Each player has imitating abilities and can adjust his or her own strategy according to experience.(3)SP and HP, respectively. In this scenario, if governments choose to “regulate”, system providers and hospitals will receive a fine F. Therefore, the payoff functions of game players can be defined as:If system providers and hospitals simultaneously choose to “not invest”, patients will not pay more for a low level of privacy protection. The profits of system providers and hospitals remains fixed, which can be written, as (4)Hξ. In this scenario, if governments choose to “regulate”, hospitals will receive the fine F and system providers will get the subsidy. Therefore, the payoff functions of entities can be defined as:If only system providers choose to “invest”, this will encourage technology research and software upgrades. Patient’s privacy can be protected at a higher level and patients are therefore willing to pay more. However, hospitals might free ride off the investment made by system providers and share in the extra benefits (such as having a positive reputation and earning patient trust). Thus, the hospitals will earn a larger profits (5)Sξ by free riding. In this scenario, if governments choose to “regulate”, then system providers will receive the fine F and hospitals will receive the subsidy. Therefore, the payoff functions of entities can be defined as:If only hospitals choose to “invest”, staff behavior can be regulated, and privacy awareness can be improved. Patients are also willing to pay more for effective privacy protection. Similarly, system providers also might share in the extra profits (6)R0 (R0 > 0) if they choose to “regulate”. However, if governments choose to “not regulate”, it will result in low efficiency of privacy protection and free riding might be present. Therefore, they will not obtain reputation profits.As mentioned above, patient’s privacy can be protected at a higher level even if only one side of the system providers and hospitals choose to “invest”. In this scenario, governments can obtain reputation profits (7)F < R0).In practice, not all the behavior of privacy investment or free riding can be assessed precisely, for the sake of limited budgets and technological support. Therefore, we assume that the fine for system providers and hospitals is not enough, and smaller than reputation profits (2(8)R1 (R1 > R0). Therefore, the payoff functions of entities can be defined as:If both the system providers and hospitals choose to “invest”, patient’s privacy can be protected more effectively, and patients will be willing to pay more. In this scenario, whether or not governments choose to “regulate”, they will receive higher reputation profits Considering the reality of mHealth privacy investment in China, we posit the following assumptions to facilitate the model formulation and solution:These assumptions are either common to most game theoretical approaches or closer to scenarios in reality. Based on the above assumptions, the payoff matrix for game players is shown in x(0 ≤ x ≤ 1), and the population choosing “not invest” is 1−x. Similarly, the population of hospitals choosing “invest” is y(0 ≤ y ≤ 1), and the population choosing “not invest” is 1−y. The population of governments choosing “regulate” is z(0 ≤ z ≤ 1), and the population choosing “not regulate” is 1−z.In the initial stage of the three types of players’ game, suppose that the population of system providers choosing “invest” is μ1,1 represents the expected payoff of system providers that choose to “invest”, μ1,2 represents the expected payoff of system providers that choose “not invest”, and μ1 represents the average expected payoff. Then:According to the assumption in μ2,1 represents the expected payoff of hospitals that choose to “invest”, μ2,2 represents the expected payoff of hospitals that choose to “not invest”, and μ2 represents the average expected payoff. Then:Supposing that μ3,1 represents the expected payoff of governments that choose to “regulate”, μ3,2 represents the expected payoff of governments that choose to “not regulate”, and μ3 represents the average expected payoff. Then:Supposing that x for system providers is:According to the Malthusian dynamic equation , the repy for hospitals is:The replication dynamic equation of the population z for governments is:The replication dynamic equation of the population P1, P2, P3, P4, P5, P6, P7, P8, P9—that correspond to equilibria of the dynamic system. P9 is a mixed equilibrium point that satisfies the condition:From the replication dynamic equations above, we have nine equilibrium points—The stability of equilibrium points can be analyzed by using a Jacobian matrix . The Jaca11 < 0, a22 < 0 and a33 < 0 [P9 is not satisfied under the above condition because a11 = 0, a22 = 0 and a33 = 0. Here, P8 is not a satisfied condition because GC > 0. Other equilibrium points will be ESSs whereas the values of related parameters are satisfied under different conditions.We can examine the stability of equilibrium points by following the conditions  a33 < 0 . Then, wAccording to GC > R0 + L, governments will choose to “not regulate”, whether or not system providers and hospitals choose to “invest”. The ESSs are elaborated by the following propositions:If the regulation cost is high enough to satisfy the condition of Proposition 1.When Proof. If In this scenario, the regulation cost is higher than the credit loss. Therefore, governments will choose to “not regulate”. Meanwhile, the profit growth coefficients are small, so system providers and hospitals have little or no incentive to invest in privacy protection . Therefore, the ESS profile is to . □Proposition 2.When Proof. If In this scenario, hospitals have a stronger incentive to invest in privacy protection. Then, if the profit growth coefficients of the system providers remain fixed, we can prove thatSystem providers prefer to choose to “not invest” because the profit from choosing “invest” is lower than the profit from free riding. Therefore, the ESS profile is to . □Proposition 3.When Proof. Similarly, we find that In this scenario, system providers will invest in privacy protection. We can also prove thatHospitals prefer to “not invest” because the profit from choosing to “invest” is lower than the profit from free riding. The ESS is to . □Proposition 4.When Proof. Similarly, we know that governments will choose to “not regulate”. Then, if The profits to system providers and hospitals that choose to “invest” are higher than the investment costs. However, the profits are lower than the profits from free riding. These entities may therefore free ride others, and the ESS profile can be to  or to  which depending on the initial stage. □Proposition 5.When Proof. If In this scenario, the profits for system providers and hospitals that choose to “invest” are higher than both the investment costs and the profits from free riding. Both of these entities will therefore choose to “invest”, even without the imposition of regulations. Therefore, the ESS profile is to . □F + L < GC < R0 + L , then governments will choose to “regulate” when system providers and/or hospitals choose to “invest”. The entities will receive a fine if they do not choose to “invest”. Here, ESSs are elaborated by the following propositions:If the regulation cost is satisfied by the condition of 2Proposition 6.When Proof. Because Therefore, hospitals will invest in privacy protection when they can obtain the subsidy In this scenario, the profit for system providers choosing to “invest” is lower than the profit from free riding even if the system providers may receive the fine Proposition 7.When Proof. If Therefore, system providers will choose to “invest” when they can obtain the subsidy The profit for hospitals choosing to “invest” is lower than the profit from free riding even if they receive the fine Proposition 8.When Proof. If The profits for system providers and hospitals that choose to “invest” are higher than the investment costs, but the profits are lower than the profits from free riding. These entities may therefore free ride others, even when they might be faced with the fine GC < 2R + L, then governments will choose to “regulate” even if they will not gain the desired reputation profits. The ESS is illustrated as follows.If the regulation cost is low and therefore satisfied the condition of Proposition 9.When Proof. Because In this scenario, the profit growth coefficients are small. As such, system providers and hospitals will choose to “not invest” , even if they can obtain the subsidy. Therefore, the ESS profile is to . □Our game equilibria provide a detailed exposition of the game model and its properties. In this section, we derive numerical results from the game analysis, and use MATLAB to simulate and support the game-theoretic analysis. The variables used to calculate the ESSs were According to the analysis in We set the values of the included parameters to GC < 2R + L. Therefore, the replication dynamic equation of population z for governments can be defined as z = 0. Then, we set the replication dynamic equation of population x,y for system providers and hospitals to be from 5% to 95%.Governments will choose to “not regulate” because Therefore, the numerical simulation of different ESSs can be analyzed under the different values of F + L < GC < R0 + L , governments will choose to “regulate” if any of the system providers and/or hospitals choose to “invest”. Therefore, the replication dynamic equation of population z = 1. Similarly, we set the replication dynamic equation of population When 2To facilitate our simulation, we set the values of included parameters to Therefore, the numerical simulation of different ESSs can be analyzed under different values of GC < 2F + L, governments will always prefer to “regulate”. Therefore, the replication dynamic equation of population z for governments can be defined as z = 1. The replication dynamic equation of population When We set the values of the included parameters to We set The simulation result is shown in Next, we conduct a sensitivity analysis to explain the impact of First, we perform a sensitivity analysis of . The values of x,y as follows:Then, we set the initial population α0, α1, β0, β1, When governments choose to “regulate”, the entity that chooses to “not invest” will receive the fine Then, we set the initial population F on ESS. The values of F vary within a fixed range that can be defined as follows: If none of the system providers and hospitals chooses to “invest”, both of them will receive the fine Then, we set the initial population F) is, the fewer steps there are to ESS. Hence, under these circumstances system providers and hospitals will tend to choose to “not invest” due to the small profit growth coefficients, even when they know will receive the fine. That is, if the profit growth coefficients are satisfied MS > NS while the probability of choosing  is higher if MS < NS. MS can be defined as follows:In order to discuss the impacts of different parameters on ESS, we take Proposition 4 as an example. Based on the equilibrium analysis, the ESS of system providers and hospitals can be either  or  when free riding is present. As shown in SP, HP, Sξ, Hξ, SC, HC, α0, α1, β0, β1) influencing the ESS. We take investment costs  as examples, and other scenarios are similar.There are 10 variables  of HC are, the higher probability of converging to .Similarly, we define the values of The impacts of other variables on ESS are shown in To provide useful insights for the multiple participants in the privacy protection of mHealth system: (i) the system providers; (ii) the hospitals; (iii) the governments, we worked with Nanjing Drum Tower Hospital and Jiangsu Province Hospital of TCM, two famous hospitals with well-established mHealth systems in Nanjing, China. While building this evolutionary game theoretic model, we also interacted with Department of Health of Jiangsu Province, China, to help us understand the regulations involved in healthcare. Based on the model analysis and simulation results, we draw the conclusion that profit growth coefficients, investment costs, benefits from free riding and governmental regulation all play important roles in the investment choice of privacy protection. In particular, we find that if the profit growth coefficients are prohibitively small, system providers and hospitals will not invest in privacy protection, even if adequate government subsidies are available. Hence, we propose three different strategies for policy makers that can help boost participation in privacy investment.(1) Increasing the minimum profit growth coefficients and reducing the investment costsSupport innovation of privacy protection technology. Any technological innovations related to privacy protection that can increase payoffs and reduce costs should be encouraged and motivated through National Science and Technology Plans or industrial development funds . GovernmEnhance privacy awareness. Proper privacy education programs should be strengthened, in order to remove current forms of narrow-minded consciousness relating to privacy protection. Additionally, privacy protection lectures should be held, where experts in this domain would be invited to systematically explain to the citizens how to develop an appropriate attitude towards privacy protection.Provide two types of a mHealth service. Hospitals can provide two types of a mHealth service. The first type is a basic service, which patients could obtain it at a low price. The second is a value-added service, which would offer improved levels of patient safety and privacy. However, this service would be provided at a higher price. With improved of privacy awareness, patients would be willing to pay more for better privacy protection. Through this two-type method, system providers and hospitals could obtain the right balance between benefits and costs of privacy investment.According to Proposition 1, system providers and hospitals will choose to “not invest” due to the relatively small benefits to be obtained from privacy investment, even if they may potentially receive a fine from governments. According to Proposition 9, to  is the optimal state of privacy protection in a mHealth system. Ideally, system providers and hospitals will invest in privacy protection without the regulation of governments. Based on the above propositions, we find that the probability of privacy investment is positively related to the size of the profit growth coefficients. If the profit growth coefficients increase to a critical level, system providers and hospitals will obtain the expected benefits from investment in privacy protection, enabling patient privacy to be protected at a higher and more secure level. Therefore, increasing the minimum profit growth coefficients and reducing investment costs would help system providers and hospitals obtain larger benefits if and when they choose to “invest” in privacy protection. The policy makers can create these conditions by implementing the following measures:(2) Strengthening privacy advertisements and improving privacy regulationsAccording to Proposition 6, Proposition 7 and Proposition 8, when the profit growth coefficients and the investment costs of system providers and hospitals remain fixed, the probability of choosing to “invest” will be greater if governments choose to “regulate”. Based on the model analysis, the probability of governmental regulation is negatively related to regulation costs, and positively related to reputation profits. Positive public service advertisements unconsciously affect people’s behavior and thoughts, and shape their values. As such, advertising is an effective way to enhance reputation profits and reduce regulation costs. In addition, the absence of trust might be a severe issue and a major challenge to effective privacy protection in the healthcare domain. With serious doubts coming from the patients, a powerful legal system should be built, in order to improve patient trust levels and to reshape the credit mechanism rather than adopting industrial self-discipline. Therefore, we need to improve the relevant laws and regulations in China, in order to make clear the privacy authority of individuals and the responsibility of governments. In this way, governmental regulations can be properly implemented, and citizens can immediately preserve and defend their legal rights.(3) Intensifying punishment and offering incentivesAccording to Proposition 1 to Proposition 5, if governments choose to “not regulate”, the cost of privacy investment is so high under current technical conditions that system providers and hospitals react negatively. Additionally, one important reason for the strategic choice to “not invest” (and free riding instead) is that the entities do not have to pay very much for their misdemeanors. Based on the model analysis, giving larger subsidies and fines to system providers and hospitals will increase the probability of privacy investment. On the one hand, governments should reward and support those entities that persist in implementing privacy protection. Governments should guide system providers and hospitals towards transforming their attitude toward investment to one that supports the enhancement of security and privacy awareness. On the other hand, because of the importance of rewarding and punishing mechanisms used under the current technical conditions, the power of multiple social organizations should be used to supplement the regulation of governments. This could include such steps as relaxing approval conditions, in order to give legality and authority to system providers and hospitals, and supporting a variety of privacy protection activities (organized by the associations) through financial subsidies and social donations.The strategic choice of governmental regulation is mainly correlated with the size and degree of reputation profits and credit losses, as well as the cost of regulation. These factors profoundly affect the investment choice of system providers and hospitals.The strategic choices of privacy investment made by system providers and hospitals are not only correlated with the profits from investment, but those choices also affected by the extra benefits from free riding and the fine (or subsidy) from governments.If the profit growth coefficients are prohibitively small, then system providers and hospitals will choose to “not invest” due to the small benefits they will receive. This is true even if they receive a fine/subsidy from governments. When the profit growth coefficients increase to a critical level, the probability of choosing “invest” will be correspondingly larger if governments choose to “regulate”.As regulation costs increase, the strategic choice of governments will change from to “regulate” to “not regulate”. Similarly, as the profit growth coefficients increase, the strategy profile of system providers and hospitals will change as follows: , , , .If the extra benefits from free riding are large enough, the probability of system providers and hospitals choosing to “not invest” by will increase. On the other hand, if the fine/subsidy from governments increases, the probability of choosing to “not invest” and to free ride instead will decrease.If the profit growth coefficients are larger enough, system providers and hospitals will be willing to invest in privacy protection even if without governmental regulation. This result will not accrue additional benefits to system providers and hospitals, but will also reduce the cost to governments. Therefore, in reality the optimal stage of privacy protection is to .Taking mHealth system as the context, we propose an evolutionary game-theoretic model to assess the decision making of privacy investment among system providers, hospitals and governments. We examine the conditions under which the chosen strategy is an evolutionary stable strategy, and investigate to quantify the appropriate investment of privacy investment and regulation. Then, we design a numerical simulation to explore and verify the theoretical results for managerial implication. Our model is generalizable to other similar healthcare systems and settings around the world, but not in those which are free or heavily state subsidized. We obtained the following results with potential implications via a theoretical analysis and simulation:In this study, we use mHealth as the context for illustration, however, the development can be adapted with some effort to other emerging domains. Our study has several limitations that can be addressed in follow-up study. First, one could instead use an evolutionary game model for strategy choice based on a nonlinear demand function. It would be very interesting to compare those results with ours, but it would be very complicated to analyze due to its complexity. Second, a scenario involving an increased demand for patients’ privacy protection might be considered as this will affect the evolutionary path of the strategies. Third, not all the behavior of privacy investment or free riding can be evaluated precisely, for the sake of limited budgets and technological supports. How to enhance the accuracy of regulation on privacy investment should be addressed. Finally, an interesting issue to address in future work is how other factors  affect the evolution of the choice of strategy for privacy protection.


Despite advances in behavioral science, there is no widely shared understanding of the “mechanisms of action” (MoAs) through which individual behavior change techniques (BCTs) have their effects. Cumulative progress in the development, evaluation, and synthesis of behavioral interventions could be improved by identifying the MoAs through which BCTs are believed to bring about change.This study aimed to identify the links between BCTs and MoAs described by authors of a corpus of published literature.Hypothesized links between BCTs and MoAs were extracted by two coders from 277 behavior change intervention articles. Binomial tests were conducted to provide an indication of the relative frequency of each link.SD = 13.80). The most frequently linked MoAs were “Beliefs about Capabilities” and “Intention.” Binomial test results identified up to five MoAs linked to each of the BCTs  and up to eight BCTs for each of the MoAs .Of 77 BCTs coded, 70 were linked to at least one MoA. Of 26 MoAs, all but one were linked to at least one BCT. We identified 2,636 BCT–MoA links in total (mean number of links per article = 9.56, The BCT–MoA links described by intervention authors and identified in this extensive review present intervention developers and reviewers with a first level of systematically collated evidence. These findings provide a resource for the development of theory-based interventions, and for theoretical understanding of intervention evaluations. The extent to which these links are empirically supported requires systematic investigation. This study produced a large dataset of hypothesised links between behaviour change techniques and the processes through which they change behaviour, as described in a set of published research reports. These findings can be used in the design of behaviour change interventions, and in furthering our understanding of how interventions have their effects. Behavior plays a key role in maintaining health, and in the prevention, management, and treatment of disease and disability. Activities such as smoking, alcohol misuse, physical inactivity, and certain dietary behaviors contribute to the global disease burden and often lead to premature death , 2. TherDespite rapid growth in behavioral intervention research, the effects of behavioral interventions continue to be typically small, variable, and not maintained long-term , 4. Cumuincreasing beliefs about one’s capabilities. On the other hand, one might hypothesize that the BCT “Restructuring the Social Environment” . BCTs are designed to enable behavior change, and can do this by augmenting factors that facilitate behavior change, or by mitigating factors that inhibit behavior change. As an example, one might hypothesize that the BCT “Graded Tasks”  might cehavior” ) might cBCTs are usually selected on the basis of the theoretical constructs they are proposed to target; for example, prompting experience of mastery through behavioral practice is often used to increase self-efficacy, based on Bandura’s theory of self-efficacy . HoweverTo enhance the design of more effective interventions, we need to develop a clearer understanding of the processes through which individual BCTs have their effects  . We concUnderstanding the links between BCTs and MoAs is important not just for intervention development , but also for evaluation . Intervention evaluations, replications, and syntheses have benefitted, in recent years, from the development of guidance for reporting interventions, such as the Template for Intervention Description and Replication (TIDieR) , and BCTSuch frameworks and taxonomies are helpful methods for knowledge accumulation and evidence synthesis; however, despite these advances, we currently lack a thorough understanding of the links between BCTs and specific MoAs. To advance understanding of these links, one approach is to review the published intervention literature and identify the links that have been identified by their authors. Previous research has suggested that empirical evidence about the links between individual BCTs and their MoAs may be limited . HoweverThis article reports on the first study from a larger program of research , examiniHow frequently is each possible BCT–MoA link described?Which BCTs are frequently described as targeting a specific MoA?Which MoAs are frequently described as influenced by a specific BCT?Do any specific BCT–MoA links occur more frequently than might be expected given the average frequency of BCT–MoA links?As part of this program of research , the folWe identified published articles reporting behavior change interventions  in which authors described links between BCT(s) and MoA(s) . To maximize efficiency, given time and resource constraints, our search strategy prioritized articles in which (i) BCTs had been identified using a taxonomy , either in the article itself (by intervention authors), or retrospectively by systematic reviewers, and/or (ii) MoAs had been identified using a theoretical framework. We identified articles through electronic searches, requests to experts, and by reviewing the reference lists of systematic reviews.To identify articles in which BCTs were likely to have been explicitly identified (to maximize efficiency of data extraction), we conducted a forward-search  of five published BCT taxonomies , 22–25. http://www.ucl.ac.uk/behaviour-change-techniques/people/iab), spanning 10 countries, and to researchers in the field via scientific and professional societies, including the US Society for Behavioral Medicine (SBM), European Health Psychology Society (EHPS), UK Society for Behavioural Medicine, and Division of Health Psychology of the British Psychological Society.We sent a request for relevant articles to the 42 members of the project’s International Advisory Board  explicitly described a BCT (not necessarily labeled as such by the authors) as linked to one or more MoA(s) . For example, an article would be included if the authors described an intervention that asked participants to set goals related to the target behavior, and indicated that goal-setting would change behavior through increasing self-regulation. Articles were excluded if they were not peer-reviewed , if no behavioral outcome was reported, and/or if descriptions were not sufficiently detailed to be able to identify at least one link. For example, an article would be excluded if the authors described the intervention in detail, including BCTs, but did not explicitly state Two researchers initially reviewed the full texts of all identified articles for eligibility, with screening guidelines iteratively updated and all discrepancies resolved through discussion. Once acceptable inter-rater reliability was achieved (Kappa = 0.9), articles were screened independently  defined as a process through which behavior change could occur and (ii) clearly linked to a BCT. Discrepancies were resolved through discussion, and consulting with senior experts  where needed. Guidelines for BCT–MoA link coding were revised when judged necessary to improve clarity (see some inference needed and 2 = no inference needed), whether or not the links included groups of BCTs or MoAs (1 = one BCT linked to one MoA and 2 = more than one BCT linked to one MoA or more than one MoA linked to one BCT), and whether the link was tested empirically . The three tables were connected using an identifying code to ensure all data were available for each article.Extracted data were tabulated as follows. General information about the study  was entered into a “source” table; all identified BCTs were recorded in a “BCT” table; BCT–MoA link data were extracted into a “link” table. In the link table, each BCT–MoA link was assigned a unique row, to which the following information was added by two coders: BCT identity number (from BCTTv1 taxonomy), MoA label and definition (as described by the intervention authors), explicitness of the link , we conducted descriptive analyses (in MS Excel) to examine the frequency of links between BCTs and MoAs .R statistical software [0), we computed an expected value to serve as an estimate of the frequency that might be observed if BCTs were randomly linked to MoAs. The expected value was computed as the probability that a particular BCT was coded  multiplied by the probability a particular MoA was coded .In addition, to examine the relative frequency of each BCT–MoA link , a series of one-tailed exact binomial tests was conducted (using software ) on the p values represent an indication of the likelihood of a link, allowing us to examine how frequently a specific BCT (X) was linked to a specific MoA (Y), by comparing this frequency with how often BCT X was used in any intervention, and how often MoA Y was targeted in any intervention. Because of the method used to compute an expected value, the resulting comparisons serve to identify links that are high in frequency relative to other links examined in this set of studies. Thus, a particular link may emerge as relatively frequent despite being identified a small number of times, if the BCT and/or MoA was rarely identified across the interventions. For example, if BCT X was linked with MoA Y only twice, but it was the only time MoA Y was linked to any BCT, the likelihood of this link may be greater than would be expected by chance . Conversely, if BCT X was linked to MoA Y eight times, but BCT X and MoA Y were both frequently linked to a range of other MoAs/BCTs, the likelihood of this link may not be greater than would be expected by chance .The resulting p < .05 as an arbitrary minimum criterion for a BCT–MoA link, although clearly more or less stringent criteria can be applied to the resulting data. We are not making statistical inferences about links that meet, or do not meet, this criterion; rather, we are presenting the data in this way as an indication of the relative frequency of each link. We used a one-tailed test as the aim was to identify agreed-upon links rather than their absence.We used R [p values) and are colored or shaded to reflect the relative strength or “heat” of that value .The full results of the analyses  are represented in “heat maps” of the findings. Heat maps allow individual data values to be represented as colors within a matrix to aid in interpreting the findings, and were generated through R . The celThe heat map clusters rows  and columns  by similarity, such that BCTs linked to similar MoAs are clustered together, and MoAs linked to similar numbers of BCTs are clustered together. MoAs that could not be categorized into one of our 26  were not included in these binomial tests. Thus, although we have selected one criterion for what constitutes a “link,” all of the data are available such that others can select alternative criteria as needed.https://osf.io/7qjvn/.Of 974 intervention articles retrieved, 697 (72%) were excluded based on full-text screening. The most common reasons for exclusion were that intervention articles specified constructs as MoAs, but there were no clear links to individual BCTs, or that intervention articles linked BCTs to theoretical constructs, but did not identify these constructs as MoAs or hypothesized mediators. Of the remaining articles, 277 described at least one link, with years of publication ranging from 1982 to 2016 and 49% published in or after 2010. More than 10 behaviors were targeted by the interventions, including physical activity (40%), dietary behaviors (18%), alcohol reduction (10%), and smoking (6%). A majority (78%) were articles reporting outcome evaluations (rather than development papers or protocols). Approximately 14% of the articles did not mention any theoretical basis for the intervention. Thirteen percent of the articles mentioned theory, but without specifying how theory was applied to intervention development or evaluation. The analyses and discussions that follow are based on the 277 included articles. A full summary of study characteristics can be found on Open Science Framework (OSF) at M = 9.56, SD = 13.80), of which 88% included a group of BCTs linked to one MoA, or a group of MoAs linked to one BCT; 12% included a single BCT and a single MoA. Seventy-seven BCTs (of the 93 in BCTTv1) were coded across the 277 articles, 70 of which were linked to at least one MoA. The BCTs that were most frequently linked to an MoA were “Instruction on How to Perform the Behavior” (182 times) and “Problem Solving” (177 times). The most frequently linked MoA was “Beliefs about Capabilities” (734 times), followed by “Intention” (318 times). One of the MoAs from our pre-existing set of 26 was not identified: “Norms” . A full list of the 2,636 BCT–MoA links is available at https://osf.io/7qjvn/.A total of 2,636 BCT–MoA links were extracted from the 277 articles, of which 33% required some inference to code, and 0.9% had been empirically tested within the included study. There were approximately 10 links per study  BCTs and 24 of 26 (92%) MoAs.Binomial tests were conducted to examine the relative frequency of BCT–MoA links. There were 87 links that met the criterion of M = 3.63, range: 1–8), and up to five MoAs were identified for each of the BCTs . For example, the MoA “Social Learning/Imitation” was linked to one BCT: “Demonstration of Behavior,” whereas the MoA “Attitude Towards the Behavior” was linked to eight BCTs: “Information about Health Consequences,” “Salience of Consequences,” “Information about Social and Environmental Consequences,” “Information about Emotional Consequences,” “Pros and Cons,” “Material Incentive (Behavior),” “Framing/Reframing,” and “Incompatible Beliefs.” Similarly, for BCTs, “Information about Health Consequences” was linked to the MoAs: “Knowledge,” “Beliefs about Consequences,” “Intention,” “Attitude Towards the Behavior,” and “Perceived Susceptibility/Vulnerability.”Up to eight BCTs were identified for each of the MoAs ” was coded 14 times, but the relative frequency with which these were linked to an MoA did not meet the p < .05 threshold.Of the 25 MoAs (from our set of 26) that were linked to a BCT at least once, only “Optimism”—derived from the Theoretical Domains Framework —was not p values closer to zero) is shown in https://theoryandtechniquetool.humanbehaviourchange.org/; see Discussion section for more details).A heat map visually representing the frequency of BCT–MoA links . In other cases, there are BCTs with links to multiple MoAs, but with one seemingly “dominant” MoA; for example, while the BCT “Problem Solving” was frequently linked to three MoAs, the link to “Beliefs about Capabilities”  was substantially more frequent than the next highest two: “Environmental Context and Resources”  and “Skills” .p < .001), “Beliefs about Consequences” (p < .001), “Attitude Towards the Behavior” (p < .001), “Perceived Susceptibility/Vulnerability” (p < .001), and “Intention” (p = .004).It is also clear, based on the data in p < .001), and the MoA “Social Learning/Imitation” was only linked to “Demonstration of the Behavior” (p = .044). In other cases, multiple  BCTs were linked to a given MoA; for example, the MoA “Emotion” was linked to “Anticipated Regret” (p = .002), and “Information about Emotional Consequences” (p = .024).p < .05 criterion. Identifying these links provides an initial resource of theoretical and practical value indicating which links are believed to be present  and which links appear to be absent .Findings from this study represent the first dataset summarizing hypothesized links between BCTs and MoAs that were frequently described by authors of published interventions. We identified 2,636 BCT–MoA links between 70 BCTs and 25 MoAs. Of those, 87 links met the In some cases, there is one clear BCT for a given MoA and one clear MoA for a given BCT. In other cases, there are BCTs linked to more than one MoA and MoAs linked to more than one BCT. There are a number of possible explanations for this—for example, for some constructs, relevant BCTs have been explicitly described in the theoretical literature. Intervention strategies to target self-efficacy , for instance, have been explicitly identified in Bandura’s theory of self-efficacy ; the defp < .05 level: “Optimism”  and “Norms” . One possible explanation for this is that there may be a lack of clarity or agreement in the behavioral science community regarding the BCTs that can be used to target these MoAs. This is particularly problematic given that the MoA “Norms” occurs frequently in behavioral theories . An altp < .05 criterion.This research has also highlighted BCT–MoA links that have been identified but are infrequently used. For example, the BCT “Problem Solving” was linked to the MoA “Behavioral Regulation” 13 times; this did not meet the p < .05 criterion.The heat map, and For instance, a researcher interested in increasing perceived vulnerability/susceptibility  may conwww.scienceofbehaviourchange.org), helps to provide the grounding on which researchers and practitioners can build innovative interventions .Our findings can also be used to develop a framework for designing and conducting empirical tests of the BCT–MoA links, to guide the development of an evidence base that can resolve ambivalence about links, and to explore the potential of BCTs and MoAs that appear to be currently underused. Thus, the BCT–MoA links database can be used both to identify links that have been frequently described in the literature, for which empirical tests are needed, as well as to identify links that appear to be understudied. More broadly, advancing the science of behavior change at a theoretical and methodological level, through this and similar initiatives . Alternatively, it could point to a lack of specificity in the selection of BCTs and the targeting of MoAs, and/or to a lack of detail in intervention reporting.A number of limitations of the current work should be noted. The studies in this review were purposively selected to maximize the likelihood of identifying BCT–MoA links. In our call for articles, we contacted international societies with broad reach in North America (SBM) and Europe (EHPS); however, there are international societies covering other parts of the world that may have elicited further international research articles. Our dataset of articles may not be representative of the wider behavioral intervention literature; for example, the intervention literature may be restricted in the theories represented, and/or in how they represent the theories. We would note, however, that we did not set out to conduct a systematic review or to identify a representative sample of intervention articles; our aim was to identify a corpus of literature in which BCT–MoA links were most likely to be identifiable.www.scienceofbehaviourchange.org; see also [It should also be noted that the links extracted from the 277 articles were based on authors’ descriptions, and very few had been tested empirically within the articles. This suggests a clear research agenda: for researchers in behavior change to systematically test the links that have been frequently described. To this end, an initiative is underway in the United States to advance efforts to identify, measure, and manipulate MoAs through the experimental medicine approach .The results of this study provide no information about the links that did not appear in the included study articles. The absence of reported links may reflect several possibilities: authors’ beliefs that such links do not exist, not considering these links when designing their studies, finding them too difficult to operationalize, using theoretical constructs and ideas implicitly, defaulting to common-sense assumptions about how behavior change happens, or not including this detail when reporting.www.humanbehaviourchange.org).Finally, there are other characteristics of the studies, and BCT–MoA links, that we did not extract and that may be of interest and relevance . By publishing our dataset online through OSF and through our interactive online tool (see below), we welcome further data extraction and/or additional analyses by researchers who are interested in examining these characteristics. The findings from this study are informing a subsequent study that is bringing together other characteristics such as BCT delivery, behavioral target, intervention setting, and target population (see www.humanbehaviourchange.org). This program of work aims to advance our methods for intervention design, evaluation, and synthesis, creating an up-to-date knowledge base that can be tailored to specific populations, settings, and target behaviors.This is one of three related studies examining the links between BCTs and (i) MoAs and (ii) behavioral theories . Findinghttps://osf.io/7qjvn/) to ensure they are maximally transparent and useful to the scientific community. Publishing the data on OSF will enable research groups to identify new research questions and share data. By publishing our full matrix of links with all relevant data, we encourage researchers to examine the full matrix of links when drawing on the findings.The data sets resulting from this study are available via the website OSF (www.bct-taxonomy.com/interventions). This online resource is searchable by author, year, BCT, and target behavior, and also includes a facility for researchers to add articles that have identified BCTs using BCTTv1. Second, the “heat map” matrix is available as an interactive tool for researchers and intervention developers (https://theoryandtechniquetool.humanbehaviourchange.org/). For each link, users are able to access current study data, upload other data and information about relevant research activities, and contribute suggestions for collaborative research efforts to populate the matrix with empirical evidence (www.humanbehaviourchange.org). The more that programs of research in this area can be co-ordinated, the more efficiently evidence about BCT–MoA links will accumulate.The findings from this study have contributed to two online resources for the research community. First, the BCT coding completed as part of this work  is available as part of an existing resource that collates interventions specified by BCTs (see kay078_suppl_Supplementary_File_1Click here for additional data file.kay078_suppl_Supplementary_File_2Click here for additional data file.kay078_suppl_Supplementary_File_3Click here for additional data file.