Effectiveness of Trust Repair Strategies in the Crisis of Corporate Internet Public Opinion

In the Internet environment, the network public opinion of corporate quality defects will cause enterprises to fall into the crisis of consumer trust. Enterprises often choose to issue announcements to deal with public opinion. Can an announcement release effectively restore trust? Are there differences in the effectiveness of different strategies in the announcement? It has become an urgent problem to be explored in the management of corporate public opinion crisis. This study takes the three corporate defect events exposed at the March 15th Evening Party as cases, and crawls the online comments and corporate response announcements on the microblogs about defect events. First, the sentiment analysis is used to calculate the emotional strength of the defect events. Secondly, the content analysis method is used to classify the trust repair strategies of the enterprise announcements. Finally, by constructing the trust repair strategy effectiveness model, the effectiveness of the trust repair strategy adopted by enterprises in different evolution stages is analyzed. The results show that in different stages of public opinion evolution, the release of response announcements can effectively enhance the emotional intensity of consumers. Moreover, in the emergency period, the relationship repair strategy is more effective; in the sustained period, the informational repair strategy is more effective; in the regression period, the functional repair strategy is more effective. Through practical cases, the research explores the effectiveness of enterprise trust repair strategy under the network public opinion crisis, expands the research content of enterprise network public opinion field, and provides strategic advice for enterprise public opinion crisis management.

Introduction
New online social media is gradually becoming the mainstream platform for the public to comment and express emotions. These new social media have the characteristics of large number of users and fast information dissemination, and their social influence far exceeds that of traditional media [1]. In this context, when a company's product quality or service defect occurs, the consumer's communication and interaction on social media quickly disseminates the defect information, thereby forming corporate public opinion, which greatly destroys consumer trust and puts the company into a crisis of trust [2]. For example, in the "Xi'an Benz Customer Rights Defender Incident" in 2019, the company's delay in responding caused the incident's public opinion to ferment over and over again, causing serious damage to the corporate image. In the case of public opinion crisis, companies often use public announcements to respond to public opinion in an attempt to repair consumer trust and restore the losses caused by public opinion. The real question that needs to be addressed is: Can the company's announcement effectively repair trust? What response strategies do companies use in announcements to be more effective?
In order to control the "butterfly effect" of corporate public opinion and restore consumer trust, the issue of trust repair is receiving more and more scholars' attention. Some scholars are devoted to discussing various factors that affect trust repair, such as the spread of information, time delay, and the type of repair strategy, so that companies can manage the corresponding controllable factors to achieve the purpose of repairing consumer trust [3]. From the perspective of Internet public opinion, other scholars have grasped the changes in public emotional trends and concerns by digging the topics and emotions of Weibo texts to provide a basis for public opinion monitoring. They suggest strategies for companies to respond effectively at various stages of public opinion evolution, including apologizing, focusing on the transparency of information release [4], and summarizing experiences and communicating in a timely manner [5]. These studies have provided a wealth of preliminary results for the study of consumer trust repair issues.
However, most of the current studies are based on the research of the characteristics and influencing factors of trust repair, few scholars have discussed the effectiveness of trust repair strategies adopted by enterprises. Research on corporate strategy recommendations remains at the level of qualitative discussions. This article focuses on the three typical car defect events exposed by the "315 Evening Party", and takes the trust repair strategy adopted in the corporate announcement as the research object. Based on the consumer sentiment intensity of the public opinion of the defect event, the article builds a validity model of trust repair strategy, and analyzes the effectiveness of the trust repair strategy in different life cycles of the public opinion. The research conclusions expand the research content in the field of corporate public opinion and provide strategic suggestions for crisis management of corporate public opinion.

Corporate Internet Public Opinion
With the development of social media, the formation and dissemination of internet public opinion has brought new challenges to corporate management. On the one hand, corporate internet public opinion affects the production and operation of enterprises by affecting consumers' attitudes and behaviors; on the other hand, new media on the internet provides a platform and conditions for corporate public opinion monitoring, guidance and management [6]. The existing literature in the field of Internet public opinion mainly focuses on the theoretical model and application cases to study the corporate internet public opinion.
In the theoretical research of corporate crisis network public opinion, scholars mostly carry out research from the perspective of the characteristics of internet public opinion itself or the index system. Zhu Xi et al. screened fifteen indicators used to evaluate the public opinion of corporate defect events, established an index system based on popularity, danger and dispersion, analyzed the impact and relationship between indicators, and established an efficient internet public opinion analysis model. Qu Qixing et al. [8] put forward a trend analysis model of public opinion heat based on Markov chain, aiming at the large fluctuation range of public opinion heat of enterprise network, which will show some missing characteristics, and provide theoretical basis for enterprise to carry out internet public opinion management and internet crisis public relations activities. Xiao Liyan et al. [9] based on the concept of corporate internet public opinion and social influence, combined with traditional information dissemination influence evaluation methods, established an index system from the three dimensions of influence breadth, influence strength and influence speed to provide decision-making support for corporate managers to conduct corporate internet public opinion management.
In the case application of corporate internet public opinion, scholars discuss the impact of internet public opinion on enterprises around specific cases and put forward suggestions for public opinion management. Eleonora [10] proposed an intelligent public opinion monitoring system to automatically infer trends of opinion on the topic of vaccination. Zhang Dongkai et al. [11] took the WeiQian "Bone Soup Gate" incident as an example, and adopted methods such as vector autoregression (VAR) and impulse response analysis to divide the internet public opinion influence of corporate emergency crisis events on Weibo platform into opinion leader public opinion impact, media user public opinion influence, ordinary user public opinion influence, empirically analyzed the impact of group public opinion on corporate stock prices. Jiang Kan et al. [12] used the social internet analysis method to analyze the "Rusty door" incident of the JAC automobile, analyzed the internet structure characteristics of the corporate public opinion from three levels, and explored the relationship between the changes in the public opinion structure and the evolution of the public opinion. Based on the results of the study, suggestions for the management of corporate public opinion on incidents of defect events are put forward. Xu Yuan et al. [13] analyzed the influence of each role on the process of corporate public opinion from the perspectives of the government, netizens, and enterprises through the two public opinion cases of the Wei Zexi and Lei Yang events coupled in time series.
From the literature, most of the existing researches are devoted to the establishment of an enterprise public opinion index system and analysis model, or to discuss the characteristics of public opinion structure and the impact of public opinion on enterprises from specific cases, so as to provide suggestions for the management of public opinion. Few scholars analyze corporate crisis management from the perspective of the emotional evolution of public opinion on corporate defect events. This article attempts to explore the evolutionary characteristics of public sentiment through emotion analysis technology to specifically analyze the process of corporate public opinion management.

Consumer Trust and Trust Repair
Among the definitions of trust by scholars so far, the most influential one is proposed by Moorman et al. [14] Trust is the trust of the client in the reliability and integrity of the trading partner. For enterprises, consumer trust is consumer trust in the reliability and integrity of the company's products, services, and the company's own brand. This trust is the foundation of consumer behavior. Due to the fragility of trust [15], once a company's products or services are defective, the dissemination of defect information will make consumers doubt and feel uneasy about the company, reducing the consumer's intentions for the company [16], thus plunging companies into a crisis of consumer trust. After the crisis of trust, it is particularly urgent for enterprises to repair trust.
At present, the research on trust repair issues mainly focuses on the two aspects of trust repair influencing factors and repair strategies. As for the influencing factors, Han Yapin et al. [3] established an SInlpR model for consumer trust repair under the product injury crisis from the perspective of information dissemination, and programmed the system with Matlab to simulate the impact of initial values, parameter changes, and time delay on consumer trust repair. Wang Feng et al. [17] built a comprehensive crisis response management model that can analyze the crisis evolution process and formulate effective crisis response plans based on the life cycle process of corporate crisis, aiming to repair the damaged reputation of the organization.
In addition to the factors affecting trust repair, research on repair strategies is also more common. Zhang Zhenglin et al. [18] used the scenario simulation method, using analysis of variance and multiple regression analysis, to conduct empirical research on consumers' initial trust repair methods and subsequent trust repair methods, providing enterprises with better trust repair methods. Feng Jiao et al. [19] applied the theory of trust source, aiming at the industrial multi brand crisis caused by the product injury crisis, from the perspective of the trusted party, based on the empirical analysis of consumer trust repair strategy, put forward the corresponding industry repair strategy. Zhao Yanni et al. [20] combined the two dimensions of the attribution theory (external attribution dimension and contextual attribution dimension) with the company's repair strategy from the perspective of consumer attribution to repair the consumer's ability trust and goodwill trust and explore the differences between different remediation strategies. Hegner et al. [21] tested the correlation between crisis response strategies and types of crisis, as well as consumer trust and willingness to buy by experiments.
From the existing research results of trust repair, the research content is rich and innovative. However, from the perspective of research data, most of them focus on psychological experiments or questionnaires. Few studies use actual corporate data to test the effect of trust repair. The actual application effect of the enterprise has not been verified. This article uses the actual internet public opinion of the enterprise as the data source and consumer emotion as the research index to measure the effectiveness of the enterprise's trust repair strategy, to test the application effect in the real situation.

Research Design
From the perspective of public opinion evolution, this study compares and analyzes the effect of multi-case trust repair strategy.
First, multiple defect events of different companies in the same industry are selected as the research cases. Data was collected on multiple Sina Weibo platforms for each defect event, and a corpus of microblogs was obtained through preprocessing. The public opinion evolution cycle was divided according to the number of microblogs. Collect the response announcements issued by the enterprise on the company's official website and official media platform after the defect incident. Second, the sentiment analysis method based on sentiment dictionary is used to analyze the sentiment evolution of public opinion data, and the content analysis method is used to code and classify the enterprise response announcement, so as to obtain the trust repair strategy type of the enterprise.
Finally, the effectiveness model of trust repair strategy is built to compare and analyze the effect of trust repair strategy.

Data Acquisition and Preprocessing
Use the advanced search function of Weibo to accurately retrieve all Weibo content of a certain keyword in a certain period of time, and use Python to write programs to crawl the retrieved Weibo data, including Weibo username, user ID, Weibo Content, release time, etc. The original data is mixed with heterogeneous data that affects the results. The data is preprocessed first, including data deduplication, deleting missing data, irrelevant data and abnormal data, and then uniformly converting it to a standardized data format for storage.

Life Cycle Division of Public Opinion
After many scholars deduced the crisis life cycle theory, a variety of division rules have emerged. For example, a three-phase model of "pre-crisis, crisis, and post-crisis" [22], a four-phase model of "symptom period, sudden period, duration, and fade period" [23], five-phase model of "signal detection, detection and prevention, loss control, Recovery, learning " [24], six-phase model of "avoidance, management preparation, confirmation, control, solution, utilization " [25] and so on. In the field of Internet public opinion, the widely used model is the four-stage model proposed by Fink [23] [26] [27]. He believes that crisis is like the life cycle of a person, and it is different from birth, growth, maturity, and death. Based on the characteristics of Internet public opinion, this article divides the life cycle of public events of defect events into four phases: Prodromal, Breakout, Chronic, and Resolution.

Sentiment Analysis Based on Sentiment Dictionary
Sentiment analysis methods are designed to determine whether a passage is positive, negative, or neutral, or to determine the opinion of the publisher [28]. A sentiment dictionary-based sentiment analysis is to identify sentiment feature words with subjective emotions in the built-in sentiment dictionary, and then calculate the sentiment intensity value of the text according to a specific formula. At present, Chinese sentiment analysis dictionaries include Hownet Chinese and English sentiment dictionary, NTUSD of Taiwan University, and Chinese sentiment vocabulary ontology library of Dalian University of Technology. This study selected Dalian University of Technology's Chinese Affective Vocabulary Ontology Library as the basic sentiment dictionary for this research. Emotions were divided into 7 categories and 21 sub-categories, and emotion intensity was divided into five levels of 1, 3, 5, 7, and 9. Based on the basic sentiment dictionary, the sentiment words in the automotive field and the weight of sentiment words are determined manually. Calculate the sentiment intensity value of each Weibo text through three aspects: emotional words, degree words and negative words. The calculation formula is as follows: is the sentimental intensity value of sentimental words in the microblog; { } , 1, 2 , 3, 4 , 5 j w j = is the weight of degree words in front of sentimental words, and the weight is 1, 3, 5, 7, 9; m is the number of negative words in front of sentimental words. The value s reflects the sentimental intensity of the consumer.
When the value is greater than zero, it indicates that the consumer sentiment belongs to a positive attitude. The higher the value, the more positive the sentiment. If the value is less than zero, it means that the sentiment of this microblog belongs to a negative attitude, and the smaller the value, the more negative the sentiment will be.

Classification of Trust Repair Strategies Based on Content Analysis
After the crisis of public opinion, in order to get rid of the crisis of public opinion and restore consumer trust, companies usually respond by issuing response announcements. The content of the announcement reflects the trust restoration strategy of the enterprise, and even some enterprises adopt a non-response strategy to allow public opinion to die on its own. Research in the field of trust repair divides repair strategies into various types. Based on the characteristics of trust repair strategies in the announcement, this research uses the classification system proposed by Xie and Peng [29] to divide trust repair strategies into: informational, relational, and functional.
The research uses content analysis to classify the response strategies of multiple companies. First, establish analysis categories. The first category is: informational repair strategy, relational repair strategy, and functional repair strategy. The second category of information repair strategy is to explain the reasons, clarify the facts, and issue announcements. The secondary categories of the relational repair strategy are: apology, commitment, and concern. The secondary categories of the functional repair strategy are compensation, refunds, and offers. Develop a preliminary coding framework and coding table to form a sample trust repair strategy type coding table (Table 1). Second, by analyzing the announcement sample repeatedly, define the analysis unit of the announcement. The coders are then trained to ensure the inherent consistency of the coders' coding. The encoders review each other's encoding results, summarize the content of inconsistent encoding opinions, discuss them, and repeat the above encoding work multiple times until all encoding work is completed. Finally, a clear category of trust repair strategy types is formed, and the frequency and percentage of each trust repair strategy type are calculated, based on which to analyze the effectiveness of the strategy.

Establishment of Effectiveness Model of Trust Repair Strategy
This study takes the repair strategy of public opinion crisis events of multiple enterprises as the research object, and sets the enterprise defect crisis events as . According to the informational repair strategy, relational repair strategy and functional repair strategy proposed by Xie and Peng [29], let's assume that the type of trust repair strategy adopted by the enterprise is This article considers that at different stages of the public opinion cycle, consumer sentiment intensity to the company has both the continuous impact of the previous stage of information and the role of trust repair strategies in this stage, which is a comprehensive response of consumers to the two stages of information. Therefore, sentiment intensity can be regarded as the combination of two parts of emotion intensity, the formula is as follows: It should be noted that, for the symptom period ( 1 k= ) stage, consumers have responded to the defect event, but the enterprise has not yet intervened in the defect event. Therefore, it can be considered that the sentimental intensity ( 1 i s ) of consumers during the symptom period is completely affected by the defect event.
As the sentiment intensity of each Weibo at different stages cannot be directly calculated by subtracting from formula (3), this article uses the sentiment intensity of each Weibo at this stage minus the average sentiment intensity of the previous stage, the formula is as follows: Through the test of the mean value ( it k s ) of each stage of the same enterprise, the effectiveness of the enterprise information repair strategy can be verified. By comparing the mean value ( it k s ) of each stage of different enterprises, we can verify the effect difference of different information repair strategies.

Data Collection and Preprocessing
In this study, Volkswagen DSG defect event, JAC Tongyue body rust event and Land Rover aurora gearbox fault event exposed by "3.15 Evening Party" were selected as the case studies. The study collects data on three defect events on the Sina Weibo platform. The collection time is set for the entire March in which each event was exposed by "3.15 Evening Party". The "3.15 Evening Party" is a show that exposes events that violate consumer rights. The data collection content includes: username, user ID, Weibo ID, Weibo content, publishing time, etc. Data is pre-processed by using Python language, including data deduplication, deletion of missing data, irrelevant data and abnormal data, and unified conversion to standardized data format storage. In the end, 10058 pieces of effective public opinion Weibo data were collected, including 7,380 pieces on Volkswagen DSG defect events, 1812 pieces on JAC Tongyue body rust events, and 1,866 pieces on Land Rover Aurora nine-speed transmission failure events.
This study collected the announcements published on the official websites and official media platforms of Volkswagen China, JAC, and Land Rover China, after the defect occurred. Among them, Volkswagen China issued four announcements on March 15,16,18, and 20, JAC issued two announcements on March 15 and 16, and Land Rover China issued two announcements on March 15 and 19. All 8 announcements are processed in clauses and stored in a unified format.

Life Cycle of Public Opinion
This study uses one day as a time unit to perform time series statistics on the daily number of Weibos for the three defect events during the collection time ( Figure 2). According to the four-stage model of the life cycle proposed by Fink, the life cycle of three defect events on Weibo public opinion data is divided (Table 2): during the Prodromal period of the three cases, the defect event has occurred, and Weibo users began to express their views on related events; In the Breakout period, after the events were exposed by the "3.15 Evening Party", the discussion volume of Weibo users experienced a sudden and large-scale increase; during the Chronic period, the incident had not been resolved well, and the discussion volume was in a gentle and stable state; receding In the Resolution period, the defect incident has basically ended, the discussion volume of the incident has gradually decreased, and the impact caused has gradually subsided.

Sentimental Intensity of Defect Events
Based on the sentiment dictionary of the automobile field constructed in this paper, the Weibo texts of the three defect events are sentimentally analyzed to obtain a time series diagram of the average daily sentimental intensity value ( Figure 2). Occurs on a date in March. This paper calculates the sentiment intensity ( i k s ) of three defect events, and its descriptive statistical analysis in each stage of the life cycle is shown in Table 3.

Classification of Trust Repair Policies
The classification coding result of corporate response announcements by content analysis method is shown in table 4. Table 4. Results of encoding trust repair policy types.

The Event
The Primary Categories Coding

Frequency
The Percentage Frequency The Percentage Frequency The Percentage According to the coding results in Table 4, we classify the trust repair strategies of different enterprises at each stage. In the DSG defect incident, Volkswagen issued a total of four announcements during the emergency period and duration. In the Breakout period, two Weibo announcements were issued on March 15th and 16th respectively. In the announcement content, the highest proportion of relationship repair strategies (50%) concerned with customers and commitment to take this incident seriously, we classify it as relational repair strategy (B). In the Chronic period, the announcement was made twice on March 18th and 20th respectively. In the announcement content, the proportion of responding to the cause of the failure and explaining the various concerns of consumers was the highest (65%), which was classified as an informational repair strategy (A). There is no information announcement during the Resolution period, which is classified as a non-response strategy (O).
During the JAC Tongyue body rust event, JAC Automobile issued two announcements during the Breakout and Chronic periods. During the Breakout period, JAC issued an announcement on Weibo immediately, expressing great importance and concern for the incident, apologizing to the owner, and promising to provide solutions for the owner as quickly as possible, and fully adopted the relationship repair strategy (B). During the Chronic period, JAC Automobile issued an announcement explaining the technical reasons for the rust of the car body and clarifying that the new product cars do not have similar problems such as rust. The information repair strategy has the highest proportion (50%), which is classified as an information repair strategy. (A). There is no information announcement during the Resolution period, which is classified as a non-response strategy (O).
In the event of a Land Rover Aurora gearbox failure, Land Rover China issued two announcements during the Chronic period and the Resolution period. Among them, no information announcement was issued during the Breakout period, which was classified as a non-response strategy (O). During the Breakout period, Land Rover China issued an announcement saying it attached great importance to the exposure, apologized to consumers and promised to solve the problem as soon as possible. At the same time, it had applied to the AQSIQ to recall the vehicles involved. The proportion of relational repair strategies was the highest (62.5%). It is classified as a relational repair strategy (B). During the Resolution period, Land Rover China issued a recall notice, apologized again and explained the cause of the failure. At the same time, different types of compensation and maintenance benefits were provided for the involved and non-involved owners. The functional repair strategy had the highest proportion (50%). Classified as a functional repair strategy (C).
Based on the above analysis, we obtained the types of trust repair strategies adopted by enterprises in different life cycle stages of public opinion in the three events, as shown in table 5. Table 5. Ttrust repair policy types.

Effectiveness Test of Enterprise Trust Repair Strategy
According to the effectiveness analysis model of the trust repair strategy constructed in this paper, the effect of the trust repair strategy of each enterprise at different stages is calculated by formula (4). In order to test whether the repair is significantly effective, a single sample T test is performed on i t k s . The results are shown in Table 6. The results of the one-sample T-test showed that, in the symptom period, without the enterprise response, the average sentimental intensity of consumers for the three defect events was -2.089, -8.846, and -3.993, all of which were significantly negative (p <0.001). It shows that all three companies were affected by the defect events during the warning period, which triggered a crisis of public opinion on the Internet.
The average effectiveness of the trust repair strategy during the Breakout period was 3.764, 9.290, and 0.509 respectively. Volkswagen and JAC issued response announcements, and the effectiveness of their trust repair strategy was significantly positive (p <0.001). The non-response repair effect was not significant (p = 0.078). This shows that the repair strategy adopted by the company in the Breakout period is effective, and keeping silent is not conducive to trust repair.
The mean values of the trust repair strategy in the Chronic was 7.670, 3.156 and 1.455, respectively, and the effect of the trust repair strategy was significantly positive (p<0.001). At this stage, all three companies issued response announcements and adopted repair strategies that were conducive to trust repair.
The average effectiveness of the trust repair strategy in the Resolution period was -9.940, -1.622, and 4.940 respectively. Neither Volkswagen nor JAC issued a response announcement, and the response effects of no response were significantly negative (p <0.001). Land Rover issued an announcement at this stage, and its trust repair strategy has a significant positive effect (p <0.001). This shows that in this period, enterprises still need to take measures to repair trust.

Comparison of Effectiveness of Enterprise Trust
Repair Strategies The one-sample T-test analyzes the effectiveness of the repair strategy adopted by the same company at different stages. To test the difference in the effectiveness of different trust repair strategies, it is necessary to compare the average effectiveness of trust repair strategies in different stages of different enterprises. We use Games-Howell test to compare two different repair strategies adopted at the same stage, and test whether there is a significant difference in the average effectiveness of the trust repair strategies adopted by the three companies at each stage. The analysis results are shown in Table 7.

Conclusion
This article uses three typical auto defect incidents exposed at the "3.15 Evening Party" as the research cases, crawling online comments and corporate response announcements about the defect incident on Weibo. First, apply sentiment analysis technology to sentiment analysis of corporate public opinion caused by defect events. Second, the content response method is used to code and classify the company's response strategy. Finally, a effectiveness model of trust repair strategy is constructed, statistical methods are used to test the effectiveness of the company's trust repair strategy and to compare the differences in the effectiveness of trust repair strategies at different evolutionary stages.
The research conclusions are as follows: First, after the formation of online public opinion, the coping strategies adopted by companies to issue response announcements are effective at all stages of the public opinion life cycle, which can significantly increase the consumer's sentiment intensity. This result also confirms the research conclusions of related psychological experiments [29]. The efforts of enterprises in information, relationships, and functions all help to rebuild the integrity of the enterprise and win the trust of consumers. Second, a comparative analysis of partial trust repair strategies shows that there are some differences in the effectiveness of repair strategies in different stages: 1) Relational repair strategies are more effective during Breakout periods. 2) In the Chronic, the informational repair strategy is more effective. 3) During the Resolution period, the functional restoration strategy is more effective.
The research conclusions try to answer the practical question of "Can an enterprise issue a response announcement to effectively restore trust? What response strategy does the enterprise use in the announcement to be more effective?" It has certain management implications. In the Breakout period, in order to alleviate the spread of negative consumer sentiment and the spread of negative public opinion, companies should adopt a relational repair strategy, express their concern and attention to the incident in a timely manner, and promise to actively solve the problem. In the Chronic, in order to eliminate the anxiety caused by the lack of consumer information, companies should adopt informational repair strategies, issue announcements to explain the facts and reasons, and actively interact with consumers. During the Resolution period, enterprises should adopt functional repair strategies, release solutions to incidents, restore consumer economic losses to restore consumer trust, and finally resolve the crisis of online public opinion.
From the perspective of the evolution of public sentiment and the actual data of the enterprise, this study explores the effectiveness of the trust repair strategy in the crisis public opinion of the enterprise. This research makes up for the shortcomings of the current research in the measurement of public opinion indicators and data sources. It also expands the theoretical content in the field of corporate Internet public opinion management. In addition, We put forward specific countermeasures for enterprises: adopting a relational strategy in the emergency period, an information strategy in the continuation period, and a functional strategy in the recession period, which have certain practical guiding significance for the management of corporate public opinion in the Internet environment. There are still some limitations in the study. Due to the limited number of comparable announcements, this article only compares some strategies adopted by enterprises, and fails to analyze all repair strategies systematically. Future research can collect more abundant enterprise announcement data., Carry out a comprehensive assessment of the effectiveness of the three types of "informational, relational, and functional" repair strategies.


Simple or complex? Consumer response to display signs1

Retail signage provides information from the marketer to facilitate product purchase. An increase in sign information creates greater sign complexity, which raises the question: for consumer product choices, what quantity of information is helpful versus overwhelming? We hypothesize that consumers would allocate more visual attention to complex signs and that sign complexity would be a predictor of likeliness to buy (LTB). Five experts rated 105 real garden center signs for complexity and five low, moderate, and highcomplexity signs were selected for the study. Signs were incorporated into Tobii X1 Light Eye Tracker software, where 85 non-student subjects rated sign attractiveness and LTB from a display containing that sign. Subjects allocated greater visual attention (higher fixation count and longer total fixation duration) to more complex signs, which were also rated as most attractive. Initial regression results showed sign attractiveness and fixation count were positive predictors of LTB, while complexity and total fixation duration were inversely related to LTB. Mediation analysis showed that fixation duration fully mediates fixation count impact on purchase intention. Results suggest that informationrich messaging in high complexity signs, while seen as attractive, may give consumers too much information and higher cognitive load, which makes decision-making more difficult.

INTRODUCTION
The retail environment can overwhelm consumers with visual cues such as merchandise, display fixtures, and signage. Signage is an important marketing communication tool which may influence and persuade consumers at the point of purchase (Kellaris and Machleit 2016). For unpackaged or minimally packaged goods (e.g. plants, apparel, produce), signs can facilitate the buying decision by providing product information that may not be readily discernable by simply viewing the product. Yet, what is the best level of signage information to motivate a purchase, without overwhelming consumers? Scant work has investigated retail sign complexity and its influence on purchase intention. In a study of print advertisements, Pieters et al. (2010) classified traditional measures of visual complexity as feature complexity; their research indicates that visual complexity, composed of both feature and design complexity, greatly differed in their impact on visual attention and attitude toward the ad. While increased feature complexity had mixed results on favorable evaluations, increased design complexity had a more consistent correlation with longer gazes and more favorable attitudes toward the visual display (Pieters et al.). Using those findings as the motivation for the present study, our goal was to investigate the role of sign complexity on likeliness to buy. We speculate that complexity will play a role in visual attention, sign attractiveness, and purchase intention.

LITERATURE REVIEW Visual complexity
Perceived complexity, a subjective property of signage, is an evaluative label that consumers might attribute to a sign and may impact how consumers cognitively process the information presented (Kellaris and Machleit 2016).
Processing fluency refers to the "subjective evaluation of how easily a stimulus is processed," and this concept describes how people cognitively monitor the mental effort required for processing a stimulus (Orth and Crouch 2014, 526;Schwarz 2004). Visual complexity theory helps to explain the mechanism behind processing fluency and explains how "visual input interacts with the perceiver to generate behavior and experience" (Donderi 2006, 84). Berlyne's (1974) aesthetic theory proposes an inverted U relationship of visual complexity with consumer response; the inverted U-curve depicts mid-range visual complexity as the most appealing and capturing the greatest consumer attention (Berlyne 1974;Tuch et al. 2009). In other words, moderately complex images may be more appealing and command more visual attention than simpler or more highly complex images. Table 1 summarizes the current empirical literature on complexity with regards to design, purchasing intent, functionality, and cognitive processing and the inclusion of images. While many of the studies included images, not all did. Three assessed purchase intention and one measured purchase consideration. One study included a construct similar to attractiveness: aesthetic appeal. None of the literature in this review investigated the complexity of signs focusing instead on webpages, print advertisements, product images, product descriptions, brand logos, and shopping environments. The degree of complexity varied depending on the stimuli being observed. Complexity had an impact on purchase intent in these four studies: Anderson and Jolson (1980);Geissler et al. (2006); Puškarević et al. (2016);and Putrevu et al. (2004).

Simple Designs
Advertising managers have traditionally shown a preference for simplicity in advertisements, as less complex stimuli are generally easier to process, resulting in higher fluency (Anderson and Jolson 1980;Shuptrine and McVicker 1981;Janiszewski and Meyvis 2001;Reber, Schwarz, and Winkielman 2004;Reber, Wurtz, and Zimmermann 2004). Several studies have analyzed consumer responses to design complexity, for example Orth and Crouch (2014) demonstrate that lower complexity enhances the perceived attractiveness of products and packages and Eytam et al. (2017) find that the majority of subjects (~75%) rated simple designs as easiest to use but lower in functionality. Therefore, a bifurcation emerges: simplicity in design is to remove as many unnecessary elements as possible, yet the reduced functionality is not always appreciated by consumers (Berlyne 1974;Thompson et al. 2005). Simple designs may be easier to process and be most attractive but may not necessarily provide sufficient information, nor evoke the greatest likeliness to buy.

Medium and High Complexity Designs
Increasing the design complexity of a visual message is correlated with increased curiosity and sustained visual attention (Pieters et al. 2010). Moderate complexity has been associated with maximum appeal for message designs (Berlyne 1974;Geissler et al. 2006). Although medium complexity is generally preferred, lower levels of perceived complexity have been associated with increased performance in search tasks, comprehension, and recall (Tuch et al. 2009).
Consumer characteristics influence the perception of complexity and complex designs can create both positive and negative associations. Knowledge can be a significant moderator for visual, technical, and lexical dimensions of complexity (Putrevu et al. 2004). Also, information-rich messages seem to be more effective for highly involved consumers, suggesting that those consumers were less likely to suffer from information overload and, hence, were persuaded by the information contained in more complex messages (Putrevu et al.). Dellaert and Stremersch (2005) and Eytam et al. (2017) further confirm that consumers are more willing to accept the complexity of a customized product if they perceive a higher product utility or functionality. Yet, high complexity may cause featurefatigue because feature-laden products may frustrate users and reduce satisfaction (Goodman and Irmak 2013;Thompson et al. 2005). Negative feelings towards greater complexity may reduce purchase probabilities.
Characteristics associated with higher complexity such as irregular shapes, greater detail (such as visually rich photographs), and increased variety of objects increase the likelihood of higher order processing (Donderi 2006). However, higher complexity is not always seen as a positive. Orth and Wirtz (2014) sought to establish visual complexity effects on approach / avoidance behavior through processing fluency and perceived attractiveness of an environment. In their work, two stores were evaluated on their perceived complexity. The main result is that complexity has a significant negative effect on perceived attractiveness and on perceptual load. However, perceptual load (amount of distractor information) fully mediates the relationship between complexity and shopping experience. This confirms that complexity operates through perceptual load to negatively affect the shopping experience for consumers. In this case, more visually complex environments are detrimental to the shopping experience because of the increased load they place on customers (Orth et al. 2016).

Design Complexity and Attention
To process the visual information, visual attention must first be allocated to a stimulus, or sign (Kellaris and Machleit 2016;Tang 2020). This form of attention is related to processing ease and fluency (speed); for example, greater attention to complex words leads to shorter processing time (Rayner 2009). Complexity can influence visual attention negatively, as crowded shelf displays can disrupt shoppers' visual attention by slowing down the cognitive processing required to locate an item (Pieters et al. 2010;Clement et al. 2013). This cognitive load, where individuals monitor and try to gain control over their thoughts and mental effort when processing input, can deplete a person's attention capacity (Lavie 2000;Schwartz et al. 2013). When visual complexity increases so does mental processing effort. Gilbert et al. (1988) show that individuals who try to actively control their visual attention and ignore nuisance stimuli (e.g. crowded displays) perform worse on a subsequent task than subjects who view the same meaningless stimuli but do not actively try to ignore them. Therefore, complex display designs may impede consumer purchase choice because the effort to cognitively process the display distracts from the goal of the display -to motivate purchase.

Eye Movement
Eye movement is an indicator of visual attention, decision-making processes, and choice (Ares et al. 2014;Behe et al. 2017;Hepworth et al. 2010; between visual complexity and FC (see Chassy et al. 2015;Wang et al. 2014). Interestingly, Wang et al. found that FD was similar for websites, despite varying degrees of complexity; however, for complex tasks on moderately complex websites, FD is greater. This could be attributed to load theory of attention, which explains how a person can filter unnecessary stimuli under conditions of high perceptual load (Wang et al. 2014). Van der Laan et al. (2015) investigate the effect of FD on choice and they found that the preferred choice was fixated upon longer. Thus, we hypothesize: H1a: Signs classified as highly complex (vs. moderate or low) will have the highest FD; H1b: Highly complex signs will have the greatest FC.

Attractiveness
Attractiveness, "the quality of being pleasing or appealing to the senses," is a subjective property, and several dimensions of attractiveness (aesthetics, arousal, functionality, and fluency) have been investigated in the context of product evaluation (Kellaris and Machleit 2016; "Attractiveness" 2020). Of these dimensions, aesthetics is a predictor of preference across all levels of visual complexity regardless of user type (Eytam et al. 2017). Thus, if a consumer finds an item to be attractive, then regardless of the level of packaging or signage complexity, the consumer will prefer that particular item. These aesthetic appraisals happen within a few seconds and a key driver of attractiveness is how fluently viewers are able to process the stimulus (Lindgaard et al. 2006;Mollerup 2015;Tractinsky et al. 2006;Reber, Wurtz, and Zimmermann 2004).
Fluency is the subjective experience of ease with which a person processes a stimulus and an important source of information (Reber, Wurtz, and Zimmermann 2004). Sometimes consumers misattribute the fluency to the stimulus and associate more fluent stimuli with greater attractiveness (Schwarz). In Puškarević et al. (2016), a study similar to Wedel and Pieters (2008), consumer attitude towards advertisements with different typeface figurations were evaluated on a five-point Likert scale using three variables: likeable, favorable, and interesting. Findings show that al. 2018; Milosavljevic et al. 2012;Mundel et al. 2018;Werthmann et al. 2013). This information is collected in an objective, non-invasive manner by observing gaze behavior and computing metrics, such as fixation duration and fixation count (Vu et al. 2016). Fixation duration is the length of visual stop on an object, while fixation count is the number of stops in a visual area of interest (AOI). For complex visual stimuli such as displays, eye fixations are necessary in identifying and cognitively processing objects (Chandon et al. 2009).
Humans are blind during physical eye movement (saccade), except for smooth pursuit saccade, an example of which would be watching a car drive past. "Visits" are a visual metric that include both saccades and fixations in a specific AOI. Given the inability to see during eye-movement, fixations, not visits, are the more common metric. Additionally, fixation count (FC) and total fixation duration (FD) are highly correlated. FC is the number of times a person views a specific area and FD is the sum of fixation times in a specific area of interest. If a subject had 10 fixations (FC=10) in a specific AOI, each lasting 0.1 second, the FD is 1 second. Although the measures are related, they do highlight different aspects of visual attention, as where FC indicates the number of "looks" an area attracts FD is an aggregate measure of time in that area.
In consumer behavior contexts, eye-movements are thought to be controlled by top-down and bottom-up processes in choice tasks (Orquin and Loose 2013). Top-down processes refer to characteristics about the consumer, such as individual traits. Bottom-up factors, on the other hand, refer to characteristics about the stimulus, such as signs or products. Top-down and bottom-up factors contribute to attention and, thus, both affect meaning derived from the stimuli (Huddleston et al. 2015). Top-down information assessment is considered as goal-driven attention and bottom-up is commonly defined as stimulus-driven attention and is controlled by marketers who regulate the visual stimuli on displays (Corbetta and Shulman 2002).
The evidence linking FC to complexity is mixed. Huddleston et al. (2015) found a negative relationship between the FC and LTB in a retail center context while other studies have confirmed a positive relationship consumers pay attention to and find advertisements most attractive when the short verbal cues are depicted through rhetorical figuration, indicative of the observation that simple advertisements are most attractive (Puškarević et al. 2016). In this study, we use signage as an advertising format and hypothesize that: H2: Consumers will rate moderately complex signs as more attractive compared to simple or high-complexity signs; H3a: Sign complexity is a predictor of Likeliness to Buy (LTB) a product from a display containing that sign; H3b: More attractive signs will evoke a higher LTB; H3c: Greater visual attention to the number of elements in the display (FC) will evoke a greater LTB; H3d: More visual attention through total time processing (FD) will indicate a greater LTB.

MATERIALS AND METHODS Stimuli
To develop the study stimuli, a selection of 105 images were taken from displays in U.S. garden retailer centers. This selection was designed to ensure a broad array of sign complexity, based on attributes described in Pieters et al. (2010), was present. A team of five raters gauged sign complexity based upon the following criteria: quantity of objects (many = complex), irregularity of objects (irregular = complex), dissimilarity of objects (dissimilar = complex), detail of objects (detail = complex), asymmetry of object (asymmetric = complex), and irregularity of object arrangement (irregular = complex). The raters were trained independently, and each evaluated all of the images; their ratings were averaged and subject to mean separation by SPSS (data available upon request). Of the 105 images, 5 low, medium, and high complexity signs were then selected for the study, totaling 15.
Low complexity signs have mean scores at least one standard deviation (SD) above the minimum (1.00) and a SD of raters less than two-thirds the overall  (1.12). Moderate complexity signs are scored at one SD above the mean (0.560) and a SD of the raters less than 2/3 of the overall SD (1.12). High complexity signs have 1 SD under the maximum and a SD of the raters less than 2/3 of the overall SD (1.12). To further reduce the number of images, those with the lowest standard deviations and with means closest to the overall minimum, mean, and maximum are chosen for the low, moderate, and high complexity categories, respectively. Figure 2 (below) shows the stimuli in each complexity level.
Images were then randomized and incorporated into the Tobii X1 Light Eye Tracker software and pre-tested with several subjects prior to study implementation to beta-test for subject fatigue and experiment timing. The eye-tracking camera was mounted on the study's computer monitor and the images were located centrally on the screen.

Procedure
To test our hypotheses, an experiment with a protocol and instrument approved by the University Committee on Research Involving Human Subjects was conducted using the selected signs (IRB# x18-1348e Category: Exempt 2). Collected data were analyzed with SAS System for Windows (Version 9.4). After visually evaluating each image, subjects rated their likeliness to buy (LTB) a product from a display and sign attractiveness. Attractiveness, consisting of a three-item scale (likeable, beautiful, and attractive) are measured using 5-point Likert scales. Likeliness to buy (LTB) is measured using a 11-point Juster scale (0=no probability of purchase, 10=certain probability of purchase). At the end of the experiment subjects complete a separate computer-based questionnaire (Qualtrics Online Survey Software) containing sociodemographic information.
Data were collected in the fall of 2018 at two large American universities and subjects, largely non-student, were recruited through email invitations sent to departmental panels. Upon arrival at the testing location, subjects were greeted, provided with an informed consent form, and paid a $10 incentive; they then conducted the eye-tracking portion of the study, followed by a purchase and demographic questionnaire.

Eye-movement tracking process
Once seated at the Tobii eye-tracking device, the eyetracker was calibrated to the subjects (Behe et al. 2013).

Figure 2 / Signs within experiment displayed by complexity category
The study began with instruction and practice slides. Each of the 15 stimuli were preceded by a 2 second bull's eye to reposition the subject's gaze to one of the 4 corners of the screen to avoid central gaze bias. In addition to the LTB and attractiveness ratings, two visual measures of attention, fixation count (FC) and fixation duration (FD) with FD calibrated to a hundredth of a second, were extracted.

Sample Characteristics
The study sample is 73% female (SD = 0.44) and has a mean age of 34 years (SD = 11.92). There is an average of 2 adults per household (mean = 2.05, SD = 0.79) and 75% of the households have at least 1 child (mean = 0.75, SD = 0.02). The percentage of subjects who completed a 4-year college degree or greater is 80.2% of the sample (mean = 5.18; SD = 1.36). Household income averages $62,559 (SD = 49.21). Ninety-five percent of the population is plant purchasers.

Analyses
Sign complexity is the independent variable and the dependent variables are visual attention measures (fixation count and fixation duration), sign attractiveness, and likeliness to buy (LTB). Sign attractiveness is measured using a three-item construct: "How attractive is this sign?" (0=very unattractive, 5=very attractive) "How beautiful is this sign?" (0=not at all beautiful, 5=very beautiful) and "How likeable is this sign?" (0=very unlikeable, 5=very likeable), whereas LTB is measured using an 11-point Juster scale (0=no probability of purchase, 10=certain probability of purchase) (Juster 1966). The Principal Component Analysis of the three items used to measure attractiveness accounts for 85% of the variance (Cronbach Coefficient Alpha Standardized = 0.9281). The three items have loadings over 0.600 (Attractive = 0.9519, Beautiful = 0.9420, Likeable = 0.9111) and are used as a single construct to measure attractiveness.
A series of one-way ANOVA analyses, by complexity, was conducted to test H1a, H1b, and H2. Differences between sign complexity levels are tested with Tukey's HSD. To test all parts of H3, a regression assessed impact of sign complexity, sign attractiveness, and visual measures (FC and FD) has on LTB.

Hypothesis 1a: Signs classified as highly complex (v. moderate or low) will have the highest FD.
Signs with high and moderate complexity have a similar FD (6.1 high vs. 5.8 moderate ) which was greater than signs classified as low complexity (4.7 low ) (see Table 2). Thus, Hypothesis 1a is partially supported.

Hypothesis 1b: Highly complex signs will have the greatest FC.
In partial support of Hypothesis 1b, signs categorized as highly complex have a greater FC as compared to signs categorized as low complexity (29.7 high vs. 24.1 low ), but FC in high complexity signs is similar to signs classified as moderately complex (28.9 moderate ) (see Table 2).

Hypothesis 2:
Consumers will rate moderately complex signs as more attractive compared to simple or high-complexity signs. Using participants' composite rating of sign attractiveness for each of the 15 signs, results show that signs classified as highly complex have the greatest attractiveness (mean=0.678, SE= 0.02) compared to low (mean=0.134, SE=0.03) or moderate complexity signs (mean=-0.659, SE=0.04). This is confirmed with a Tukey's HSD (High v. Low =1.337,p=0.0001;Moderate v. Low=0.790,p=0.0001;High v. Moderate=0.554,p=0.0001). Thus, Hypothesis 2 is not supported; contrary to this hypothesis, highly complex signs are rated as most attractive while low complexity signs are rated as least attractive.

Hypothesis 3: Sign complexity, sign attractiveness, FD, and FC are predictors of LTB.
To test Hypotheses 3a-3d, we examined both simple pairwise correlations (Table 3) and conducted a regression (Table 4). Sign Complexity (β=-0.1936), Attractiveness (β=1.8868), FC (β=0.0093), and FD (β=-0.0404) are all predictors of LTB, however, sign complexity and FD are inversely correlated to likeliness to buy. Therefore, regression results support Hypothesis 3b and 3c. Approximately two-thirds of the observed variation in LTB are explained by this model, with attractiveness having the greatest explanatory power (R 2 = 0.6842).

Table 4 / Complexity and Attractiveness on Purchase Intent
Because FD and sign complexity were negatively related to purchase intention, a mediation effect of FD is suspected and following Baron and Kenny's (1986) recommendation for testing mediation, a series of regressions was conducted. First, we found that FC is positively related to FD (F=13342.08, p=0.0001). Next, regressing FC on purchase intention uncovered a positive result (F=20.4098, p=0.0001), as did regressing FD on purchase intention (F=17.5186, p=0.0001). Simply stated, both FC and FD independently increased purchase intention, however, regressing FC and FD on purchase intention showed that FD was no longer a significant predictor of purchase intention (FC: F=2.8856, p=0.0895; FD: F=0.0240, p=0.8770), indicating that FD mediates the effect of FC on purchase intention.
To summarize, subjects spent more time (higher FD) viewing high and moderately complex signs and across more areas (higher FC) as compared to lower complexity signs. Highly complex signs were found most attractive, with moderately and then low complexity signs following. A negative relationship found greater fluency, a subjective measure based on the individuals' experience with the stimulus, in the combination of text and images present in highly complex signs. Since all the participants have previously purchased plants, they may have found greater fluency in highly complex signs because of their familiarity with these types of signs or products or expected to see this type of sign in a garden store or retail center. A relationship between familiarity and perceived attractiveness has been found in previous work (Peskin and Newell 2004). Future studies should investigate the relationships between fluency, familiarity, and attractiveness, and in turn, the ability of these constructs to predict purchase intention.
Contrary to Hypotheses 3a-3d, complexity is inversely related to LTB (purchase intention), so, while the respondents found more complex signs to be more attractive, this did not result in higher purchase intention. A seeming disconnect, this aligns with Iyengar and Lepper's (2000) study, which finds that consumers are more attracted to a larger product display but are more likely to purchase from a limited product display.
In forming purchase intention some information may be necessary, but too much information may lead to excessive higher order processing which in turn decreases purchase intention (Donderi 2006).
Hypotheses 1a and 1b, which explore visual attention and sign complexity, is supported; high complexity signs garnered more visual attention (i.e. higher fixation count and total fixation duration). This finding is consistent with Pieters et al. (2010), who confirm that advertising complexity increased visual attention, and with Morrison and Dainoff (1972), who show that greater sign complexity resulted in longer looking time.
A positive relationship exists between likeliness to buy and fixation count, thereby indicating that participants allocate more "looks" to the point of purchase signs. This allocation of visual attention supports Behe et al. (2014), in that consumers seek out and find desired or useful information faster when it is important to them, almost as if they unconsciously sort through the information present on the sign to "cherry-pick" what would help them make a purchase decision. This "cherry picking" would be supported in the observed between sign complexity and FD on purchase intention exists, meaning that lower complexity and a shorter glance increased purchase intention. However, there was a positive relationship between attractiveness and FC on purchase intention, indicating that a more aesthetically pleasing sign drew more looks increasing purchase intention. The series of regression analyses shows that FD mediates the relationships between FC and purchase intention, meaning that shorter viewing time decreased the number of possible areas at which to look or pieces of information that could be acquired.

DISCUSSION
The objective of this study is to explore the relationship between sign complexity, visual attention, perceived attractiveness of signage, and likeliness to buy. Pieters et al. 's (2010) complexity criteria are used by trained judges to characterize low, medium, and high complexity signs. Previous studies report that moderate complexity is most appealing to consumers, whereas our findings show the highest complexity level is rated as most attractive (Berlyne 1974;Pieters et al.). This could be the result of study participants finding greater functionality in the high-complexity signs (i.e. more salient information) and low functionality with low complexity signs and because plants are living products that are expected to grow and change, consumers may require more information before investing in a perishable product to reduce a perceived risk (Etyam 2017;Behe and Fry 2019). In this purchase context, and contrary to Thompson et al. (2005), reducing sign functionality is not desired. Also, consumers with an existing information base may need more detailed information provided by complex signage as a basis for comparison against that existing knowledge (Putrevu et al. 2004). This is confirmed by previous work that describes how consumers are more willing to accept the complexity of a customized product if that product allows them to achieve a higher product utility or functionality (Dellaert and Stremersch 2005).
The study participants found greater visual appeal in the higher complexity signs, potentially the result of finding highly complex signs more attractive because they have greater aesthetic appeal (see Janiszewski and Meyvis 2001;Reber et al. 2004a;2004b). While we did not measure fluency, it is possible that consumers tion in the high complexity signs actually reduced the likelihood of purchase intention, but it is unknown what information is superfluous. For example, highlighting experience attributes, such as a tomato's flavor or basil's aroma, may be helpful in a purchase decision to some customers-others though may seek out credence attributes and would rather know if the plant was grown organically or from a local producer. Teasing out the underpinnings of this intricate relationship should be a focus of future research.
From a managerial standpoint, the inverse relationship between likeliness to buy and complexity suggest that low complexity signs are appropriate for impulse purchases where the seller is trying to encourage a quick decision (Hausman 2000). In light of emergent textbased (SMS) marketing, there may be a mechanism to send a potential customer the customized information that reduces the perceived information clutter unintentionally communicated by highly complex signs. Future research should focus on the sign element combinations that lead to perceived attractiveness, which, as determined in this study, leads to purchase intention. For example, will using text to highlight the benefits (vs. features) of a plant or any other product resonate with consumers (Hall and Knuth 2019a;2019b;2019c)? How should images be integrated into the point of purchase signage? Will information-rich messaging create cognitive overload for consumers? What is the correct balance between information and images to enhance both perceived attractiveness and purchase intention? Another fruitful avenue for study is to measure both the perceived fluency and cognitive load that high and low complexity signs present and then analyze these constructs in light of purchase intention.
One potential limitation to this study is the lack of product images on low complexity signs. Since the complexity criteria for this study originated in Pieters and Wedel (2010), where the number of images were one of the evaluation criteria, the number of images for these signs were minimized. Choices of signage to reflect the different complexity levels in the main study were based on the mean / standard deviation of the judges' responses. As a result, some signs were primarily text and without images of the plant or produce higher FC. Further, the inverse relationship between fixation duration and likeliness to buy refutes previous studies (Atalay 2012;Glaholt and Reingold 2009;Krajbich 2010).
Finally, the study also found a mediation effect of FD on FC predicting purchase intention. Intuitively, a person would have fewer "looks" if they were not looking as long. Maughn et al. (2007) reported that the study participants liked a bus advertisement more if they looked at it longer. Yet, the evidence in the present study shows that the time spent viewing a sign (FD) limits how many individual pieces of information (FC) can be gleaned from it.

CONCLUSIONS
The impetus for this study is the application of Pieters et al. 's (2010) visual complexity criteria to a retail setting (garden centers) using point of purchase signage. We found that moderate / high complexity signs captured more visual attention (FD) and motivated consumers to view more areas in the signs (FC), which was similar to Chassy et al. (2015). Based on previous research, we predicted that consumers would prefer moderately complex signs over simple/highly complex signs, but this was not the case. Highly complex signs are perceived as more attractive, but greater attractiveness did not enhance purchase intention. Perceived attractiveness and complexity are positively related, i.e. more elements created a richer image and a more pleasing view, however, simply finding a complex sign as attractive did not stimulate purchase intention. More information, while creating a more attractive image, could have cluttered the path to a purchase decision; not all of the information on a complex sign was useful to all of the study subjects.
The challenge for a retailer is to identify the information that is most useful to a broad customer base and strike a balance between information and quality. For example, price as a search attribute is quite often an important, if not essential, input to the purchase decision and needs to be included in a display sign. In this study, respondents were not asked to identify what information was useful, but future work should seek to capture usefulness of information and to whom it matters most. A perceived overabundance of informa-and may have altered the sign's complexity rating. Perhaps also, other experts would have assessed the signs differently. Future studies should investigate a broader array of products and signs to determine how perceptions vary by product type or also include a complexity rating by participants.
In addition to the research questions previously mentioned, future research could investigate in what circumstances low or high complexity signs might be more effective in eliciting purchase intention (e.g. impulse purchases). For premium price points or for featured products a more complex sign could be more effective in motivating consumers to learn more about the product and dive into the details. Typically, in-store signs present other dimensions such as brand credibility and readability, which may influence perceived attractiveness, complexity and therefore, purchase intention. Top down factors (e.g. expertise, time, pressure) might also influence these constructs and will be considered as a future research area.
The relationship of Complexity, Attention, and Purchase Intent