Next Article in Journal
Factors of the Revisit Intention of Patients in the Primary Health Care System in Argentina
Next Article in Special Issue
Revisiting Past Experiences of LGBTQ+-Identifying Students: An Analysis Framed by the UN’s Sustainable Development Goals
Previous Article in Journal
Green Defense Industries in the European Union: The Case of the Battle Dress Uniform for Circular Economy
Previous Article in Special Issue
Sustainable Teaching Strategies to Teach Indigenous Students: Their Relations to Students’ Engaged Learning and Teachers’ Self-Concept
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Key Factors for Evaluating Visual Perception Responses to Social Media Video Communication

1
Department of Advertising and Strategic Marketing, College of Communication, Ming Chuan University, 250 Zhong Shan N. Rd., Sec. 5, Taipei City 111, Taiwan
2
Department of Industrial Education and Technology, National Changhua University of Education, No. 1, Jin-De Rd., Changhua 500, Taiwan
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(20), 13019; https://0-doi-org.brum.beds.ac.uk/10.3390/su142013019
Submission received: 11 September 2022 / Revised: 6 October 2022 / Accepted: 9 October 2022 / Published: 12 October 2022

Abstract

:
The purpose of this study was to investigate the key factors for creating a positive visual perception response evaluation for social media video communication. The aim of this study was to determine what factors of light sources impact visual perception to increase the interactions in social media video communication. First, the key factors of visual perception and response evaluation of visual effects in social media video communication were summarized and analyzed through an interview consultation panel of experts and scholars. Key factors were compiled into four dimensions (48 sub-dimensions), including (1) visual perception, with 12 sub-dimensions; (2) emotional perception, with 12 sub-dimensions; (3) preference perception, with 11 sub-dimensions; and (4) shape perception, with 13 sub-dimensions. Second, 12 experts and scholars were invited to form a panel to develop the Delphi technique questionnaire. After three Delphi technique questionnaires were conducted, the mean (M), mode (Mo), and standard deviation (SD) of each response were statistically analyzed, and the one-sample Kolmogorov–Smirnov test was used to analyze the appropriateness and consistency of the Delphi technique survey results. The results of this study indicate that 15 sub-dimensions met the criteria of appropriateness and consistency, which were used to establish 15 key factors for evaluating visual perception responses to social media visual communication. This study will provide a technical reference for the visual perception of digital messages in social media to improve the quality of visual perception of digital communication.

1. Introduction

With internet technologies and social media development, digital communication is made possible by messages and online interactions. People share news and content, discuss topics, and exchange emotions on social media; it has become an effective tool for information exchange and interpersonal interaction [1,2]. Social interactions among users enhance mutual understanding and can be considered positive communication behaviors that build social relationships [3]. In social media, user-to-user interactions allow people to influence each other’s perceptions, experiences, attitudes, preferences, and behaviors. Regarding digital social media, users exchange messages for a variety of reasons: to positively impact their knowledge and usage behaviors or consumption attitudes; to exchange ideas or product information; and to share experiences [4]. With frequent information exchange, users obtain relevant information, increase their consumer knowledge, and become familiar with each other. Interactions can gradually deepen users’ mutual understanding and trust, promote friendship and intimacy between users, and promote a sense of recognition and belonging [5]. Due to the heterogeneity among social media users, a few users may have information, knowledge, and experience that other users do not have, making social media an “information source”. This source becomes a platform for exchanging information, sharing related experiences, and discussing topics where users can obtain information regarding topics [6].
Color assessment by people is influenced by many factors. Above all, they are differences in the light source that can give different color impressions depending on the spectral range. Apart from that, the characteristics of the observed object are important as well as environmental conditions connected to the observation direction or light position [7]. However, visual color, luminance, RGB values of colors, or specific color combinations also affect preference perceptions and judgments. Different color attributes such as hue, saturation, and brightness affect users’ attention differently. Analyses of the data on visual attention and preference perceptions indicate that the level of luminance and saturation is more important than hue in attracting attention. Visual attention is the most attractive when colors with maximum saturation and luminance are used.
Key factors are those aspects that must be well-managed in order to achieve success. The results could guide managers in the implementation of effective critical success factors in an effort to mitigate management problems in competitive sectors [8,9]. These factors that are unique and critical in their businesses are worth exploring. The key factors identified by this study can provide reference for the visual perception of digital messages in social media to improve the quality of visual perception of digital communication and provide referential indicators and suggestions for future studies. Regarding the research methods, in addition to consulting relevant university educationalists and industry experts by conducting in-depth interviews, this study summarizes the valuable experiences and opinions adopted the Delphi technique to analyze the possible key factors.
In this study, the key factors for understanding the visual perception of lights and colors in social media video communication were explored in terms of the performance of response evaluation of social media visual perceptions. This study aimed to construct an index of key indicators of visual perception response evaluation in social media video communication. The research purpose of this study includes the following: (1) to explore and determine the key factors that influence visual perception response evaluation in social media video communication; (2) to construct the contents and dimensions of the key factors for the visual effect of lights and colors of social media video communication; and (3) to explore the reference indexes for visual perception response evaluation in social media video communication. The findings can be used by subsequent users and researchers to create a positive visual perception for video users while drawing on light application techniques to inspire positive feelings for social media users.

2. Literature Review

2.1. Social Media Analysis

COVID-19 has ushered in a new phase and has produced a new way of interacting with each other. Social media and communication methods have shifted from relying on public and mass communication channels, such as newspapers, television, and radio, to social media platforms, such as Facebook, YouTube, Instagram, and Twitter [10]. Social media allows the public to communicate with individuals and organizations and become a source of information while being affordable and easy to share [11]. In addition, social media technologies and the nature of social networks provide new ways of communicating, allowing other users to share information and express themselves through a network of social connections [12].
Social media platforms have become a vital resource and source for gathering and collecting users’ experiences, views, and information because of their popularity [13]. The use of social media to search for relevant information has grown exponentially, making it easier for consumers and users to interact and communicate with other people and product marketers [14,15]. Therefore, many people use social media as a channel and method to share their opinions, experiences, ideas, and marketing. Since people were limited by social distancing during the pandemic [16,17,18], social media platforms became an essential tool for people to interact, share information, and communicate during this time. It continues to be an important communication tool for users to seek information about their consumption, interests, reading, and communication; participate in behavioral observations and marketing; and share opinions and videos.
Therefore, using social media for interactive communication effectively changes users’ attitudes and behaviors towards sustainable development [19]. Users’ interactions become essential to increase their marketing influence on social media, such as sharing, liking, commenting, and complaining, which may change users’ attitudes, behaviors, and perceptions [20]. People sharing relevant information through social media consider it their personal choice to participate. Their attitudes and beliefs determine how they will respond to and participate in collective behavior in the community [21]. Advertisements and messages that are more informative or creative are more likely to gain empathy regarding “likes” on social media. When an informative cognitive or affective approach is used in social media, a brand’s marketing and advertising messages are more likely to be shared if their messages are appealing or attention-grabbing [22]. Branded social media messages can motivate consumers to interact with the brand by emphasizing the emotional aspect of the message, and the positive emotional response triggered by the consumer during the process can positively impact purchase intentions [23]. Therefore, people should try to increase the visual effects of social media through the contextualization of the environment so that the users’ perceived emotions and visual experiences are positive. Specifically, the application of light technology can be used to create the best visual perception for the user: an ambient light source to increase positive feelings.

2.2. Visual Perception

Do the background, color, and visual effects of lighting used in social media affect the viewer’s preference and attract attention? The differences in light source can give different color impressions depending on spectral range.
Regarding visual information, the attention of social media users can be divided into two types: sustained attention and distracted attention. When social media users focus on a specific task or visual message for an extended period, such as viewing a relevant message, filling out a form or questionnaire, making a bank transfer, or completing a purchase online, sustained attention is used. At this time, the main focus of the visual message is on readability and user-friendly color effects to sustain the user’s attention and visual focus [24]. In exploring the visual effects of social media graphics and designs, the assessment of people’s color perceptions is influenced by many factors, particularly differences in light sources, which can produce varying impressions depending on the spectral range. The characteristics of the object being viewed, the direction of viewing, and the location of the angle of light projection are also critical. A particular visual effect, such as the arrangement of lines, patterns, or objects, can impact the user’s visual perception of color in different ways [25]. Viewing intense color for an extended period can lead to retinal fatigue, which requires rest or visual neutralization. The eyes’ aging process should also be considered, as the perceptual impairment of color increases with age [26]. There may also be differences in contrast sensitivity among users [27].
The perception of visual color impressions varies depending on the object’s size and background; colors on larger surfaces are brighter and more vivid than those viewed on smaller surfaces. This phenomenon can lead to the illusion of color perception. Colors in front of a bright background seem darker than those in front of a dark background. This is known as the contrast effect, which also affects visual preference perception and attention. In related literature, some studies have explored the preference perception and visual attention associated with visual color [28,29]. In the relationship between preference perception and visual attention, color is used as the information for making preference perception decisions and for exploring the number of visual features. Research has found that simple stimuli are more advantageous than natural scenes or attractive faces, both of which have many features.

2.3. Light Source Illumination and Visual Perception

The illumination of different light sources has a relevant effect on users’ emotions [30,31], and some aspects of light source illumination affect the perception of visual comfort, naturalness, dimness, and warmth [32]. The use of light source illumination in social media allows users to perceive messages related to images or people. Different illumination sources affect visual perceptions but also affect users’ perceptions of comfort and task performance satisfaction [33,34]. For example, inappropriate lighting conditions may lead to fatigue and affect comfort and related task performance [35,36]. Conversely, appropriate lighting conditions may give users better visual comfort and improve their task performance [37,38]. In some surveys on the effects of lighting on user preferences, the following factors were measured for evaluation by surveying user perceptions: emotional perception, visual perception, task performance, user responses including task performance, lighting preferences, visual perception, emotions, health, stress, fatigue, comfort, pleasure, satisfaction, pleasure, uniformity, dimness, warmth, spaciousness, complexity, and darkness [39,40,41,42].
The purpose of light source illumination is to assist the user’s visual response to changes in perceptual, cognitive, and emotional states [43,44]. Some studies that have examined the effects of color temperature and brightness of light source illumination on emotional states [45] have found that warm or cool environments affect negative or positive emotional responses of people engaged in cognitive tasks [46]. These negative to positive responses also influence the level of stimulation from high to low and different levels of stimulation and attention. In the investigation of light source illumination conditions, mood measures, and emotional states, the scales of tension/anxiety, depression, anger/hostility, vigor/activity, fatigue, and confusion/bewilderment were employed for measurement [47].
The subjective preference is divided into strong dislike, moderate dislike, slight dislike, neutral, slight like, moderate like, and strong like. The preference of skin color is evaluated, the atmosphere perception of the illuminated environment is assessed based on the perceived attributes, and the atmosphere perception was evaluated as uncomfortable/comfortable, cool/warm, negative/active, and tense/relaxed. Subjective evaluations of the atmosphere of the illuminated environment by light sources are recorded in terms of comfort, warmth, activity, and relaxation [48].

2.4. Key Success Factors

The key success factors were judged and collected according to the experience in real production and inspection. These factors were scientifically analyzed to determine specific key success factors and obstacles [49]. Four dimensions were induced. The questionnaire content contained 48 subitem key factors, and the key success factors for evaluating visual perception responses to social media visual communication were discussed.

2.5. Delphi Technique

Delphi technique is applied in various related research fields [50,51]. When the background knowledge of the Delphi technique is used, and there are more than ten experts, the minimum group error and highest credibility can be achieved [52]. A consensus can be effectively established when the standard number of expert scholars required to reach a consensus is 10 to 15 [53,54]. Therefore, a total of 12 experts and scholars were invited to form the Delphi technique panel in this study. Anonymity is required during the interview process to avoid interpersonal communication complexities that may interfere with objective responses. Anonymity is necessary to prevent the psychological factors of authoritarian submission or a bandwagon effect caused by group leadership positions or hierarchical relationships [55]. The anonymity allows the expert panel members to express their genuine opinions and positions, leading to various considerations at different levels, making the experts’ opinions and arguments more objective. This shows that joint decisions resulting from collective discussions are more comprehensive than conclusions reached by individual thinking [56]. The subsequently summarize and analyze these experiences, and the questionnaire items are developed for the next round accordingly.
Through the literature, this study is able to establish a follow-up Delphi method survey of experts and scholars from the section of video visual perception in social media. In the literature section, we start from the interaction and communication of social media and move on to the section of visual perception, organizing, and summarizing the research of related scholars, such as visual comfort, preference, color, brightness, fatigue, atmosphere, and other feelings. Based on the compilation and summarization of the literature, we processed a Delphi method questionnaire conducted by 12 invited moral and ethical experts and scholars. Before the survey, we introduced the purpose and methodology of this study in detail, as well as the literature, which was organized, to each expert and scholar. After confirming the understanding of the research topic, the initial questionnaire was developed based on the organized literature, then we invited the experts and scholars to comment on the initial questionnaire or revise the content, collating their opinions. Later, experts and scholars were invited to comment again. We arrived at the final questionnaire by obtaining consensus and consistency.
The study was built on the basis of the compiled literature, and we expatiated the theme of the study to the experts and scholars, obtaining the conclusions of the questionnaire by exchanging opinions several times with a group of experts and scholars that is proficient in the field of Delphi method.
The purpose of this study was to investigate the key factors for creating a positive visual perception response evaluation for social media video communication. The aim of this study was to determine what factors of light sources impact visual perception to increase the interactions in social media video communication. First, the key factors of visual perception and response evaluation of visual effects in social media video communication were summarized and analyzed through an interview consultation panel of experts and scholars.

3. Methodology

3.1. Expert Interviews

A total of 12 experts and scholars were invited to form the panel in this study. After the contents and opinions of the interviews were organized and summarized, the key factors considered necessary by the experts and scholars were selected, analyzed, converged, and classified according to the opinions of the experts and scholars [57]. This was used to develop the structure of the initial questionnaire assessment factors. The factors were measured with the five-point Likert scale, with 5 = very important, 4 = important, 3 = average, 2 = unimportant, and 1 = least important. The Likert scale results were used as the basis for the retention or deletion of questionnaire items.

3.2. Research Structure and Process

The key factors for the evaluation of visual perception responses to social media visual communication in terms of light source illumination quality were explored in this study. The experts and scholars were invited to conduct three Delphi technique questionnaires anonymously until a consensus was reached. As for the criteria for selecting the appropriateness and consistency of the study [58], the following measures were used: (1) criteria for appropriateness test: very high appropriateness—mean (M) ≥ 4.5, high appropriateness—mean between 4 and 4.5, and low appropriateness—M < 3.5. (2) Criteria for consistency test: high consistency—standard deviation (SD) ≤ 0.5, moderate consistency—SD ≤ 1, and low consistency—SD > 1. (3) The one-sample Kolmogorov–Smirnov test: to assess the consistency of opinions among the interviewed experts (reaching the significant level) and to remove the indicators of the questionnaire that did not reach the significant level of inconsistency. (4) Quartile deviation (Q): quartile deviation is a measure or indicator to describe the disparity of a group, which is the 1/2 distance from the first quartile to the third quartile. If the quartile deviation is larger, the opinions of the experts are more dispersed [59]. When the quartile deviation is lower than or equal to 0.50, the group members have reached high consistency and consensus, as proposed by Hollden and Wedman [60]. In general, when the criterion of the quartile deviation is lower, the required consistency is higher, and it is more difficult to achieve consistency.
Figure 1 shows the steps to prepare the questionnaire by the Delphi technique. It illustrates the steps taken in this study to develop the questionnaire using the Delphi technique.

3.3. Delphi Technique Research Methodology and Design

The Delphi technique group is composed of 12 experts and scholars, including (1) two participants from industry experts in the social media and communication field, (2) two participants from industry experts in the visual field, (3) two participants from industry experts in the lighting field, (4) two participants from university educationalists in the social media and communication field, and (5) two participants each from university educationalists in the visual field and lighting field for four participants in total. Based on the opinions and analyses of the experts and scholars, four dimensions were developed to evaluate the key factors in the visual perception of the light source illumination quality of social media viewers. Namely, these factors were: (1) visual perception, (2) emotional perception, (3) preference perception, and (4) shape perception. A Delphi questionnaire with 48 sub-dimensions was summarized. In dimension 1 of “visual perception”, there were 12 sub-dimensions, as shown in Table 1.
The structure of the assessment factors of the questionnaire of dimension 2, “emotional perception”, was summarized based on the opinions of the interview consultation panel of experts and scholars, and there were 12 sub-dimensions, as shown in Table 2.
The structure of the assessment factors of the questionnaire of dimension 3, “preference perception”, was summarized based on the opinions of the interview consultation panel of experts and scholars, and there were 11 sub-dimensions, as shown in Table 3.
The structure of the assessment factors of the questionnaire of dimension 3, “shape perception”, was summarized based on the opinions of the interview consultation panel of experts and scholars, and there were 13 sub-dimensions, as shown in Table 4.

4. Data Processing and Analysis

A Kendall’s W test of the questionnaire was used to evaluate the consistency of the 12 professionals [61]. Table 5 shows the statistics of Kendall’s coefficient of concordance. With a chi-square of 90.877, the coefficient of concordance of Kendall was 0.000 (p < 0.001), which suggests consistent expert opinions.
The aim of this study was to determine the key factors for creating a positive visual perception response evaluation for social media video communication. Data collected from the questionnaires were analyzed by means of the Statistical Package for the Social Sciences (SPSS) software [62]. For the data analysis, descriptive analysis was adopted for the mode (Mo), mean (M), standard deviation (SD), quartile deviation (Q), and Z value of the Kolmogorov–Smirnov (K-S) test and the Kruskal–Wallis analysis of variance by ranks with regard to Delphi analysis [63].
The K-S one-sample test is used to verify the hypothesis about the origin of the sample from a given probability distribution. In the case of SPSS, the authors used the probability distribution. The K-S test involves using a z-test on ordinal variables for single samples to determine whether the sample distribution diverges from the frequency distribution. The z-score is greater than 1.96, which implies significance and consistency. The Kruskal–Wallis one-way analysis of variance by ranks (chi-square) is used to prove the consistency of opinion of all the experts and the items that participants considered to be important.

4.1. One-Sample Kolmogorov–Smirnov Test

The results of the Delphi survey were analyzed for their appropriateness and consistency. The one-sample Kolmogorov–Smirnov test statistical analysis was used for the Delphi survey, and the modifications were based on the opinions of the experts and scholars.
In the statistical results of the first Delphi questionnaire, 38 sub-dimensions met these three elements: (1) mode (Mo) was above 4; (2) mean (M) ≥ 3.5 in criteria for appropriateness test; (3) standard deviation (SD) ≤ 1 in criteria of moderate consistency test; and (4) Q ≤ 0.5; thus, the group members reached high consistency and consensus. Therefore, these sub-dimensions were kept to prepare for the second Delphi study questionnaire.
In the statistical results of the second Delphi questionnaire, a total of 22 sub-dimensions met the two elements of: (1) high criteria for appropriateness test with mean (M) ≥ 4 and (2) criteria for moderate consistency test with standard deviation (SD) ≤ 0.68. As a result, they were kept for the development of the third Delphi study questionnaire.
In the statistical results of the third Delphi questionnaire, a total of 15 sub-dimensions met the four elements of: (1) criteria for high appropriateness test with a mean (M) ≥ 4.2; (2) criteria of high consistency test with a standard deviation (SD) ≤ 0.5; (3) the one-sample Kolmogorov–Smirnov test reaching significance in the statistical analysis results; and (4) Q ≤ 0.5; thus, the group members reached high consistency and consensus.
This indicated that the revised third Delphi questionnaire had more reliability than the second one, as shown in Table 6.

4.2. Kruskal–Wallis (K-W) Independent Samples Test

When the cognitive assessments and perceptions of the sub-dimensions among different groups of experts and scholars fail to reach a significant level (p > 0.05), it can be inferred that there is agreement among the other groups of experts and scholars on the sub-dimensions. After three questionnaires and the one-sample K-S (Kolmogorov–Smirnov) test, followed by the Kruskal–Wallis (K-W) four-group independent sample test, the statistical results did not reach a significant level (p > 0.05) for the sub-dimensions. Thus, we can infer that there is a consensus among the members of the four expert groups on the importance of the sub-dimensions, as shown in Table 7.

5. Discussion

In traditional media, any new information is filtered through various editors before it is disseminated to the public. In contrast, social media users can share information and content without verification [64,65]. These social media networks have become media platforms for social interactions, consumer marketing, communication, expression, content sharing, interest, and message dissemination.
Regarding the effect of light source illumination on visual perception and cognitive ability, studies are testing the effect of different light source illumination environments on visual preferences and evaluations, perceptual judgments, and feelings concerning environmental atmosphere to investigate the impact of their visual perception and cognitive ability and subjective preferences.
The participating expert panel, without being influenced by their external environment, can fully express their ideas, techniques, and positions with their professional knowledge and practical experience. Three Delphi structured questionnaire interviews were conducted in this study. As found from the statistical results of the third Delphi questionnaire, there were 15 sub-dimensions of the four dimensions of the key factors for evaluating the response of social media viewers to the quality of light source illumination in visual perceptions meeting the following four criteria: (1) criteria of high appropriateness test with mean (M) ≥ 4.2; (2) criteria of high consistency test with standard deviation (SD) ≤ 0.5; (3) the consistency of opinions between experts and scholars, which was achieved in the one-sample Kolmogorov–Smirnov test (KS single-sample test); and (4) progressive significance, which reached the significant level.

6. Conclusions and Recommendations

6.1. Conclusions

The visual effect of social media users can be increased via the quality of the light source illumination so that the experience of the perception and visual perception of the video user is positively influenced. This study examined the key factors of the quality of light source illumination in evaluating the visual perception responses of social media viewers through the Delphi technique. The scholars and experts have analyzed and summarized the key factors for evaluating the visual perception responses to social media visual communication. There were four dimensions, namely: (1) visual perception, (2) emotional perception, (3) preference perception, and (4) shape perception. The results of the statistical analysis indicated a total of 15 sub-dimensions meeting the elements of appropriateness and consistency. The 15 sub-dimensions of the key factors for the evaluation of the response to the visual effect of social media video color were: comfort, relaxation, a sense of stability, brightness, clarity, degree of awakening, good atmosphere, liking, satisfaction, wanting continuity, attractiveness, a good sense of contour (shape), positive visual characteristics, a good sense of color, and a good sense of three-dimensionality.
This study can be used as a reference for planning vocational training courses in the visual field, lighting field, and social media and communication field. In addition, the results of this study can be used as a way of training performance, but the application of proof of vocational training methods or concepts need to be adjusted; therefore, the results of this study can be used as a cautious planning training course and progress to improve training effectiveness.

6.2. Recommendations

Based on the scales of the 4 dimensions and 15 sub-dimensions obtained from this study, efforts will continuously be made to conduct analyses and statistics on samples to explore the presentation effect of the responses of social media viewers’ visual perception of lights and colors to different light source illumination angles. Further investigations will be conducted on video users’ and viewers’ visual perceptual satisfaction.
The purpose of this study was to investigate the key factors for creating a positive visual perception response evaluation for social media video communication. The key factors of visual perception and response evaluation of visual effects in social media video communication were summarized and analyzed through an interview consultation panel of experts and scholars. The key factors scale of evaluation on visual perception and visual effect constructed by this study was used on the follow-up research. During the establishing of the key factors of the scale in this study, it was not necessary to control the subsequent experimental environment, lighting, as well as the use of mobile phones and tablet computers. After the completion, the subsequent experimental environment and mobile phone shooting and lighting projection were based on the following:
(1)
The experimental environment and the lighting angle of social media videos will be explored in subsequent studies to test the response evaluation of the visual perception of social media video communication to determine key visual qualities consistent with the expectations of video users.
(2)
Video samples will be collected in a studio with lighting from different angles. Since cell phones are often used for communication and interaction on social media, the sample videos will not be taken with a standard camera but with a cell phone to obtain samples consistent with the characteristics of typical social media videos. Based on the above examples, a follow-up survey and analysis of the questionnaire were conducted to further this research.
The created questionnaire in this study may gain scientific value that can apply to a real sample of social media users in the future. Based on the scales of the 4 dimensions and 15 sub-dimensions obtained from this study, efforts will continuously be made to conduct analyses and statistics on samples to explore the presentation effect of the responses of social media viewers’ visual perception of lights and colors to different light source illumination angles. Further investigations will be conducted on video users’ and viewers’ visual perceptual satisfaction:
(1)
It will investigate whether the visual perception satisfaction of the video user is the same as that of the viewer.
(2)
It will investigate whether the visual perception satisfaction of the video user is the same for different light source angles.
(3)
It will investigate whether the visual perception satisfaction of the video viewer is the same for different light source angles.
In addition, the experimental environment and the lighting angle of social media videos will be explored in subsequent studies to test the response evaluation of the visual perception of social media video communication to determine key visual qualities consistent with the expectations of video users. Video samples will be collected in a studio with lighting from different angles. Since cell phones are often used for communication and interaction on social media, the sample videos will not be taken with a standard camera but with a cell phone to obtain samples consistent with the characteristics of typical social media videos. Based on the above examples, a follow-up survey and analysis of the questionnaire were conducted to further this research. The structure and hypotheses of the study and research are as follows. (1) H1: The visual perceptions of social media video users are different for different lighting angles. (2) H2: The shape perceptions of social media video users are different for different lighting angles. (3) H3: Social media video users’ visual and emotional perceptions are related. (4) H4: Social media video users’ visual and preference perceptions are related. (5) H5: Social media video users’ preferences and emotional perceptions are related. (6) H6: Social media video users’ shape perceptions and emotional perceptions are related.

Author Contributions

The authors contributed meaningfully to this study. C.-J.T., research topic; C.-J.T. and W.-J.S., data acquisition and analysis; W.-J.S., methodology support; C.-J.T. and W.-J.S., original draft preparation; C.-J.T. and W.-J.S., writing review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ding, S.; Lin, J.; Zhang, Z. The influences of consumer-to-consumer interaction on dissatisfactory consumers’ repetitive purchases in network communities. Sustainability 2021, 13, 869. [Google Scholar] [CrossRef]
  2. Chang, C.M.; Hsu, M.H.; Lee, Y.J. Factors influencing knowledge sharing behavior in virtual communities: A longitudinal investigation. Inf. Syst. Manag. 2015, 32, 331–340. [Google Scholar] [CrossRef]
  3. Jung, J.H.; Yoo, J.J. Customer-to-customer interactions on customer citizenship behavior. Serv. Bus. 2017, 11, 117–139. [Google Scholar] [CrossRef]
  4. Imankhan, N.; Eekani, S.; Fakharyan, M. Examining the effect of customer-to-customer interactions on satisfaction, loyalty, and word-of-mouth behaviors in the hospitality industry: The mediating role of personal interaction quality and service atmospherics. J. Travel Tour. Mark. 2014, 31, 610–626. [Google Scholar]
  5. Wei, W.; Lu, Y.; Miao, L.; Cai, A.L.; Wang, C. Customer-to-customer interactions (CCIs) at conferences: An identity approach. Tour. Manag. 2017, 59, 154–170. [Google Scholar] [CrossRef] [Green Version]
  6. Johnson, Z.; Massiah, C.; Allan, J. Community identification increases consumer-to-consumer helping, but not always. J. Consum. Mark. 2013, 30, 121–129. [Google Scholar] [CrossRef]
  7. Sharma, A. Understanding Color Management; Delmar Cengage Learning: Clifton Park, NY, USA, 2003. [Google Scholar]
  8. Marais, M.; Plessis, E.; Saayman, M. A review on critical success factors in tourism. J. Hosp. Tour. Manag. 2017, 31, 1–12. [Google Scholar] [CrossRef]
  9. Chen, D.C.; Chen, D.F.; Huang, S.M.; Shyr, W.J. The investigation of key factors in polypropylene extrusion molding production quality. Appl. Sci. 2022, 12, 5122. [Google Scholar] [CrossRef]
  10. Piltch-Loeb, R.; Savoia, E.; Goldberg, B.; Hughes, B.; Verhey, T.; Kayyem, J.; Miller-Idriss, C.; Testa, M. Examining the effect of information channel on COVID-19 vaccine acceptance. PLoS ONE 2021, 16, e0251095. [Google Scholar] [CrossRef]
  11. Shao, G. Understanding the appeal of user-generated media: A uses and gratification perspective. Internet Res. 2009, 19, 7–25. [Google Scholar] [CrossRef]
  12. Phua, J.; Jin, S.V.; Kim, J. Uses and gratifications of social networking sites for bridging and bonding social capital: A comparison of Facebook, Twitter, Instagram, and Snapchat. Comput. Hum. Behav. 2017, 72, 115–122. [Google Scholar] [CrossRef]
  13. Binkheder, S.; Aldekhyyel, R.N.; AlMogbel, A.; Al-Twairesh, N.; Alhumaid, N.; Aldekhyyel, S.N.; Jamal, A.A. Public perceptions around mHealth applications during COVID-19 pandemic: A network and sentiment analysis of Tweets in Saudi Arabia. Int. J. Environ. Res. Public Health 2021, 18, 13388. [Google Scholar] [CrossRef] [PubMed]
  14. Son, J.; Nam, C.; Diddi, S. Emotion or information: What makes consumers communicate about sustainable apparel products on social media? Sustainability 2022, 14, 2849. [Google Scholar] [CrossRef]
  15. Sogari, G.; Tommaso Pucci, B.A.; Zanni, L. Millennial generation and environmental sustainability: The role of social media in the consumer purchasing behavior for wine. Sustainability 2017, 9, 1911. [Google Scholar] [CrossRef] [Green Version]
  16. MacKay, M.; Colangeli, T.; Gillis, D.; McWhirter, J.; Papadopoulos, A. Examining social media crisis communication during early COVID-19 from public health and news media for quality, content, and corresponding public sentiment. Int. J. Environ. Res. Public Health 2021, 18, 7986. [Google Scholar] [CrossRef]
  17. Alamoodi, A.H.; Zaidan, B.B.; Zaidan, A.A.; Albahri, O.S.; Mohammed, K.I.; Malik, R.Q.; Almahdi, E.M.; Chyad, M.A.; Tareq, Z.; Albahri, A.S.; et al. Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review. Expert Syst. Appl. 2021, 167, 114155. [Google Scholar] [CrossRef] [PubMed]
  18. Abd-Alrazaq, A.; Alhuwail, D.; Househ, M.; Hamdi, M.; Shah, Z. Top concerns of Tweeters during the COVID-19 pandemic: Infoveillance study. J. Med. Internet Res. 2020, 22, e19016. [Google Scholar] [CrossRef] [Green Version]
  19. Turunen, L.L.M.; Minna, H. Communicating actionable sustainability information to consumers: The Shades of green instrument for fashion. J. Clean. Prod. 2021, 15, 126605. [Google Scholar] [CrossRef]
  20. Ways Customers Interact and Engage with Your Brand on Social. Available online: https://sproutsocial.com/insights/social-media-interaction/ (accessed on 29 May 2022).
  21. Chen, R.; Sakamoto, Y. Feelings and perspective matter: Sharing of crisis information in social media. In Proceedings of the 47th International Conference on System Sciences, Hawaii, HI, USA, 6–9 January 2014; pp. 1958–1967. [Google Scholar]
  22. Villarroel Ordenes, F.; Grewal, D.; Ludwig, S.; Ruyter, K.D.; Mahr, D.; Wetzels, M. Cutting through content clutter: How speech and image acts drive consumer sharing of social media brand messages. J. Consum. Res. 2019, 45, 988–1012. [Google Scholar] [CrossRef]
  23. Chan, W.Y.; To, C.K.; Chu, W.C. Materialistic consumers who seek unique products: How does their need for status and their affective response facilitate the repurchase intention of luxury goods? J. Retail. Consum. Serv. 2015, 27, 1–10. [Google Scholar] [CrossRef]
  24. Lewandowska, A.; Olejnik-Krugly, A. Do background colors have an impact on preferences and catch the attention of users? Appl. Sci. 2021, 12, 225. [Google Scholar] [CrossRef]
  25. Gleitman, H.; Gross, J.; Reisberg, D. Psychology, 8th ed.; W. W. Norton & Company: New York, NY, USA, 2011; pp. 165–171. [Google Scholar]
  26. Ramek, M. Studies on color vision, color blindness and computer-generated images. Curr. Approaches Sci. Technol. Res. 2021, 4, 27–35. [Google Scholar]
  27. Owsley, C. Aging and vision. Vis. Res. 2011, 51, 1610–1622. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Jost, T.; Ouerhani, N.; Von Wartburg, R.; Müri, R.; Hügli, H. Assessing the contribution of color in visual attention. Comput. Vis. Image Underst. 2005, 100, 107–123. [Google Scholar] [CrossRef] [Green Version]
  29. Kawasaki, M.; Yamaguchi, Y. Effects of subjective preference of colors on attention-related occipital theta oscillations. NeuroImage. 2012, 59, 808–814. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Partonen, T.; Lönnqvist, J. Bright light improves vitality and alleviates distress in healthy people. J. Affect. Disord. 2000, 57, 55–61. [Google Scholar] [CrossRef]
  31. Avery, D.; Kizer, D.; Bolte, M.; Hellekson, C. Bright light therapy of subsyndromal seasonal affective disorder in the workplace: Morning vs. afternoon exposure. Acta Psychiatr. Scand. 2001, 103, 267–274. [Google Scholar] [CrossRef]
  32. Ma, J.H.; Lee, J.K.; Cha, S.H. Effects of lighting CCT and illuminance on visual perception and task performance in immersive virtual environments. Build. Environ. 2022, 209, 108678. [Google Scholar] [CrossRef]
  33. Sun, C.; Lian, Z.; Lan, L. Work performance in relation to lighting environment in office buildings. Indoor Built Environ. 2019, 28, 1064–1082. [Google Scholar] [CrossRef]
  34. Zhang, R.; Campanella, C.; Aristizabal, S.; Jamrozik, A.; Zhao, J.; Porter, P.; Ly, S.; Bauer, B.A. Impacts of dynamic LED lighting on the well-being and experience of office occupants. Int. J. Environ. Res. Public Health 2020, 17, 7217. [Google Scholar] [CrossRef]
  35. Benedetto, S.; Carbone, A.; Drai-Zerbib, V.; Pedrotti, M.; Baccino, T. Effects of luminance and illuminance on visual fatigue and arousal during digital reading. Comput. Hum. Behav. 2014, 41, 112–119. [Google Scholar] [CrossRef]
  36. De Kort, Y.; Smolders, K. Effects of dynamic lighting on office workers: First results of a field study with monthly alternating settings. Light. Res. Technol. 2010, 42, 345–360. [Google Scholar] [CrossRef]
  37. Boyce, P.R.; Veitch, J.A.; Newsham, G.R.; Jones, C.; Heerwagen, J.; Myer, M.; Hunter, C. Lighting quality and office work: Two field simulation experiments. Light. Res. Technol. 2006, 38, 191–223. [Google Scholar] [CrossRef] [Green Version]
  38. Hong, T.; Chou, S.; Bong, T. Building simulation: An overview of developments and information sources. Build. Environ. 2000, 35, 347–361. [Google Scholar] [CrossRef]
  39. Chraibi, S.; Crommentuijn, L.; van Loenen, E.; Rosemann, A. Influence of wall luminance and uniformity on preferred task illuminance. Build. Environ. 2017, 117, 24–35. [Google Scholar] [CrossRef]
  40. Moscoso, C.; Matusiak, B.; Svensson, U.P.; Orleanski, K. Analysis of stereoscopic images as a new method for daylighting studies. ACM Trans. Appl. Percept. 2015, 11, 1–13. [Google Scholar] [CrossRef] [Green Version]
  41. Murdoch, M.J.; Stokkermans, M.G.; Lambooij, M. Towards perceptual accuracy in 3D visualizations of illuminated indoor environments. J. Solid State Light. 2015, 2, 12. [Google Scholar] [CrossRef] [Green Version]
  42. Chokwitthaya, C.; Saeidi, S.; Zhu, Y.; Kooima, R. The impact of lighting simulation discrepancies on human visual perception and energy behavior simulations in immersive virtual environment. In Proceedings of the ASCE International Workshop on Computing in Civil Engineering, Seattle, WA, USA, 25–27 June 2017; pp. 390–398. [Google Scholar] [CrossRef]
  43. Hygge, S.; Knez, I. Effects of noise, heat and indoor lighting on cognitive performance and self-reported affect. J. Environ. Psychol. 2001, 21, 291–299. [Google Scholar] [CrossRef]
  44. Knez, I. Effects of indoor lighting on mood and cognition. J. Environ. Psychol. 1995, 15, 39–51. [Google Scholar] [CrossRef]
  45. Hawes, B.K.; Brunyé, T.T.; Mahoney, C.R.; Sullivan, J.M.; Aall, C.D. Effects of four workplace lighting technologies on perception, cognition and affective state. Int. J. Ind. Ergon. 2012, 42, 122–128. [Google Scholar] [CrossRef]
  46. Knez, I.; Kers, C. Effects of indoor lighting, gender, and age on mood and cognitive performance. Environ. Behav. 2000, 32, 817–831. [Google Scholar] [CrossRef]
  47. McNair, D.; Lorr, M.; Droppleman, L. Manual for the Profile of Mood States; Educational and Industrial Testing Services: San Diego, CA, USA, 1971. [Google Scholar]
  48. Liu, Q.; Huang, Z.; Li, Z.; Pointer, M.R.; Zhang, G.; Liu, Z.; Gong, H.; Hou, Z. A field study of the impact of indoor lighting on visual perception and cognitive performance in classroom. Appl. Sci. 2020, 10, 7436. [Google Scholar] [CrossRef]
  49. Abeykoon, C.; McMillan, A.; Nguyen, B.K. Energy efficiency in extrusion-related polymer processing: A review of state of the art and potential efficiency improvements. Renew. Sustain. Energy Rev. 2021, 147, 111219. [Google Scholar] [CrossRef]
  50. Rowe, G.; Wright, G. The Delphi technique as a forecasting tool: Issues and analysis. Int. J. Forecast. 1999, 15, 353–375. [Google Scholar] [CrossRef]
  51. Delbecq, A.L.; Van de Ven, A.H.; Gustafson, D.H. Group Techniques for Program Planning: A Guide to Nominal Group and Delphi Processes; Scott Foresman: Baltimore, MD, USA, 1975. [Google Scholar]
  52. Woudenberg, F. An evaluation of Delphi. Technol. Forecast. Soc. Chang. 1991, 40, 131–150. [Google Scholar] [CrossRef]
  53. Jayaratne, K.S.; Collins, D.P.; McCollum, S.B. Early-career challenges of youth development extension educators and effective strategies. Sustainability 2021, 13, 9017. [Google Scholar] [CrossRef]
  54. Hsu, C.C.; Sandford, B.A. The Delphi technique: Making sense of consensus. Pract. Assess. Res. Eval. 2007, 12, 10. [Google Scholar]
  55. Nworie, J. Using the Delphi technique in educational technology research. Tech. Trends. 2011, 55, 24–30. [Google Scholar] [CrossRef]
  56. Murry, J.W., Jr.; Hammons, J.O. Delphi: A versatile methodology for conducting qualitative research. Rev. High. Educ. 1995, 18, 423–436. [Google Scholar] [CrossRef]
  57. Shyr, W.J.; Shih, F.Y.; Liau, H.M.; Liu, P.W. Constructing and validating competence indicators for professional technicians in fire safety in Taiwan. Sustainability 2021, 13, 7058. [Google Scholar] [CrossRef]
  58. Antonio, A.A.; Benitez, M.; Castro, J.L. Consistency measures for feature selection. J. Intell. Inf. Syst. 2008, 30, 273–292. [Google Scholar]
  59. Faherty, V. Continuing social work education: Results of a Delphi survey. J. Educ. Soc. Work. 1979, 15, 12–19. [Google Scholar] [CrossRef]
  60. Holden, M.C.; Wedman, J.F. Future issues of computer-mediated communication: The results of a Delphi study. Educ. Technol. Res. Dev. 1993, 41, 5–24. [Google Scholar] [CrossRef]
  61. Chen, D.C.; Chen, D.F.; Huang, S.M.; Huang, M.J.; Shyr, W.J.; Chiou, C.F. Critical success factors to improve the business performance of tea drink chains. Sustainability 2021, 13, 8953. [Google Scholar] [CrossRef]
  62. Shavelson, R.J. Statistical Reasoning for the Behavioral Sciences; Allyn & Bacon: Needham Heights, MA, USA, 1996. [Google Scholar]
  63. Wen, J.R.; Shih, W.L. Exploring the information literacy competence standards for elementary and high school teachers. Comput. Educ. 2008, 50, 787–806. [Google Scholar] [CrossRef]
  64. Rizal, A.R.A.; Nordin, S.M.; Ahmad, W.F.W.; Khiri, M.J.A.; Hussin, S.H. How does social media influence people to get vaccinated? The elaboration likelihood model of a person’s attitude and intention to get COVID-19 vaccines. Int. J. Environ. Res. Public Health 2022, 19, 2378. [Google Scholar] [CrossRef] [PubMed]
  65. Kircaburun, K.; Alhabash, S.; Tosuntaş, Ş.B.; Griffiths, M.D. Uses and gratifications of problematic social media use among university students: A simultaneous examination of the big five of personality traits, social media platforms, and social media use motives. Int. J. Ment. Health Addict. 2020, 18, 525–547. [Google Scholar] [CrossRef]
Figure 1. Steps to prepare the questionnaire by the Delphi technique.
Figure 1. Steps to prepare the questionnaire by the Delphi technique.
Sustainability 14 13019 g001
Table 1. Visual perception (dimension 1): Social media video light and color visual effects.
Table 1. Visual perception (dimension 1): Social media video light and color visual effects.
1-1 Do you feel comfortable?
1-2 Do you feel relaxed?
1-3 Do you feel a sense of stability?
1-4 Do you feel mild?
1-5 Do you feel warm?
1-6 Do you feel cool?
1-7 Do you feel bright?
1-8 Do you feel clear?
1-9 Do you feel dazzling?
1-10 Do you feel fatigued?
1-11 Do you feel awakening?
1-12 Do you feel natural?
Table 2. Emotional perception (dimension 2): Social media video light and color visual effects.
Table 2. Emotional perception (dimension 2): Social media video light and color visual effects.
2-1 Do you feel fun?
2-2 Do you feel affinity?
2-3 Do you feel a good atmosphere?
2-4 Do you feel pleasant?
2-5 Do you feel romantic?
2-6 Do you feel monotonous?
2-7 Do you feel changeability?
2-8 Do you feel alive?
2-9 Do you feel positive?
2-10 Do you feel happy?
2-11 Do you feel a sense of presence?
2-12 Do you feel a sense of reality?
Table 3. Preference perception (dimension 3): social media video light and color visual effects.
Table 3. Preference perception (dimension 3): social media video light and color visual effects.
3-1 Do you feel that you like it?
3-2 Do you feel satisfied?
3-3 Do you feel that you want continuity?
3-4 Do you feel an important preference?
3-5 Do you feel it is interesting?
3-6 Do you feel it is optimized?
3-7 Do you feel it is attractive?
3-8 Do you feel it is good to use?
3-9 Do you feel it is stable?
3-10 Do you feel it is desirable?
3-11 Do you feel it is appropriate?
Table 4. Shape perception (dimension 4): Social media video light and color visual effects.
Table 4. Shape perception (dimension 4): Social media video light and color visual effects.
4-1 Do you feel a good sense of shape contour (modeling)?
4-2 Do you feel good visual characteristics?
4-3 Do you feel good texture and material characteristics (texture)?
4-4 Do you feel the emphasis on functional characteristics?
4-5 Do you feel the emphasis on structural relationships?
4-6 Do you feel the recognition of the correct rate (composition)?
4-7 Do you feel the recognition of graphic details (construction)?
4-8 Do you feel good recognition response time?
4-9 Do you feel good color sense?
4-10 Do you feel a good sense of three-dimensionality?
4-11 Do you feel a good sense of proportion?
4-12 Do you feel a good sense of transparency?
4-13 Do you feel a good sense of light and shadow?
Table 5. The Kendall’s coefficient of concordance.
Table 5. The Kendall’s coefficient of concordance.
Number (N)12
Kendall’s W test0.361
Chi-square90.877
Degree of freedom21
Asymptotic significance0.000
Table 6. Statistical analysis of the third Delphi questionnaire.
Table 6. Statistical analysis of the third Delphi questionnaire.
No.ItemMoMSDQK-S
z-Test
Choice
1. Visual perception
1-1Do you feel comfortable?5 4.83 0.389 02.887 *** Keep
1-2Do you feel relaxed?54.670.4920.52.309 ***Keep
1-3Do you feel a sense of stability?54.670.4920.52.309 ***Keep
1-5Do you feel warm?440.42601.443 *Delete
1-7Do you feel bright?54.670.4920.52.309 ***Keep
1-8Do you feel clear? 54.670.4920.52.309 ***Keep
1-11Do you feel awakening?54.670.4920.52.309 ***Keep
2. Emotional perception
2-1Do you feel fun?440.60301.155Delete
2-2Do you feel affinity?440.60301.155Delete
2-3Do you feel a good atmosphere?54.750.4520.3752.598 ***Keep
2-5Do you feel romantic?440.42601.443 *Delete
3. Preference perception
3-1Do you feel that you like it?54.750.4520.3752.598 ***Keep
3-2Do you feel satisfied?54.670.4920.52.309 ***Keep
3-3Do you feel that you want continuity?54.670.4920.52.309 ***Keep
3-5Do you feel it is interesting?440.42601.443 *Delete
3-7Do you feel it attractive?54.670.4920.52.309 ***Keep
4. Shaping perception
4-1Do you feel a good sense of shape contour (modeling)?5 4.83 0.389 02.887 *** Keep
4-2Do you feel good visual characteristics?54.750.4520.3752.598 ***Keep
4-6Do you feel the recognition of the correct rate (composition)?440.60301.155Delete
4-7Do you feel the recognition of graphic details (construction)?440.60301.155Delete
4-9Do you feel good color sense?5 4.83 0.389 02.887 *** Keep
4-10Do you feel a good sense of three-dimensionality?54.670.4920.52.309 ***Keep
* p < 0.05, *** p < 0.001.
Table 7. Kruskal–Wallis (K-W) independent sample test.
Table 7. Kruskal–Wallis (K-W) independent sample test.
Code of sub-dimension1-11-21-31-71-81-112-33-13-23-33-74-14-24-94-10
Chi-square2.2005.5002.7500.0000.0002.7504.4811.2220.0002.7500.0002.2004.4812.2002.750
df333333333333333
Progressive significance0.5320.1390.4321.0001.0000.4320.2140.7481.0000.4321.0000.5320.2140.5320.432
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Tsai, C.-J.; Shyr, W.-J. Key Factors for Evaluating Visual Perception Responses to Social Media Video Communication. Sustainability 2022, 14, 13019. https://0-doi-org.brum.beds.ac.uk/10.3390/su142013019

AMA Style

Tsai C-J, Shyr W-J. Key Factors for Evaluating Visual Perception Responses to Social Media Video Communication. Sustainability. 2022; 14(20):13019. https://0-doi-org.brum.beds.ac.uk/10.3390/su142013019

Chicago/Turabian Style

Tsai, Chi-Jui, and Wen-Jye Shyr. 2022. "Key Factors for Evaluating Visual Perception Responses to Social Media Video Communication" Sustainability 14, no. 20: 13019. https://0-doi-org.brum.beds.ac.uk/10.3390/su142013019

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop