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The Effect of Smiling Pictures on Perceptions of Personas


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We analyze the effect of a smile in personas pictures on persona perceptions, including credibility, likability, similarity, and willingness to use. We conduct an online experiment with 2,400 participants using a 16-item survey and multiple persona profile treatments of which half have a smiling photo and half do not. We find that persona profiles with a smiling photo result in an increase in perceived similarity with, likability of, and willingness to use the personas. In contrast, a smile does not increase the credibility of the personas. Our research has implications for the design of persona profiles and adds to previous findings of persona research that the picture choice influences individuals' persona perceptions in profound ways.
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The Eect of Smiling Pictures on Perceptions of Personas
Joni Salminen
Qatar Computing Research
Institute, HBKU; and Turku
School of Economics
Doha, Qatar
Soon-gyo Jung
Qatar Computing Research
Institute, Hamad Bin
Khalifa University
Doha, Qatar
João M. Santos
Instituto Universitário de
Lisboa (ISCTE-IUL)
Lisbon, Portugal
Bernard J. Jansen
Qatar Computing Research
Institute, Hamad Bin
Khalifa University
Doha, Qatar
We analyze the eect of a smile in personas pictures on persona
perceptions, including credibility, likability, similarity, and
willingness to use. We conduct an online experiment with 2,400
participants using a 16-item survey and multiple persona prole
treatments of which half have a smiling photo and half do not.
We nd that persona proles with a smiling photo result in an
increase in perceived similarity with, likability of, and
willingness to use the personas. In contrast, a smile does not
increase the credibility of the personas. Our research has
implications for the design of persona proles and adds to
previous ndings of persona research that the picture choice
inuences individuals’ persona perceptions in profound ways.
• CCS Social and professional topics User characteristics
Smile, Personas, Persona design, User study
ACM Reference format:
Joni Salminen, Soon-gyo Jung, João M. Santos and Bernard J. Jansen.
2019. e Eect of Smiling Pictures on Perceptions of Personas. In 27th
Conference on User Modeling, Adaptation and Personalization Adjunct
(UMAP’19 Adjunct), June 9–12, 2019, Larnaca, Cyprus, ACM, New York,
NY, USA, 5 pages. hps://
1 Introduction
Persona creators are known to have design power when craing
persona proles, resulting in varied sense-making and possible
biases by the users of personas [7, 12, 23]. Personas provide a
shared mental model of the users’ needs and wants and
summarize data about users in an empathetic format that is more
memorable than numbers [5, 15]. One of the most prominent
sections of the persona prole is the picture, typically a portrait
photo. e picture choice has been shown to inuence the end
users’ perception of the persona, directing the end users’
thinking concerning the persona [16, 21]. For example, Salminen
et al. [23] found that using an African-American picture evokes
racially associated sense-making. In addition, there is a variety of
prior work showing the impact and information aspects of
pictures in system interfaces [10].
However, there is lile to no research on how to choose the
types of images used in persona proles. Even though there are
many possible areas to investigate in this regard, in this research
we are interested in the use of smiling people in pictures.
Specically, in this regard, we could locate no existing research
about the impact of smile on the user perceptions of personas. In
this regard, it is worthwhile to pursue a beer understanding of
individuals’ perceptions toward personas and what kind of
design choices drive these perceptions if personas are to be used
by decision makers. Such research can yield actionable insights
to aid persona designers in developing beer persona proles.
In the following section, we review the related literature.
After this, we explain the experimental setting, including the
creation of the treatments and data collection. This is followed
by an analysis of the results. We conclude by presenting
practical advice for persona creators along with interesting
questions for future research.
2 Related Work
Personas are a vehicle of communication about users within a
team, across departments, and with external stakeholders [15].
Thus, personas provide a shared mental model of the end users
and summarize data about users in an empathetic format that is
more memorable than numbers [20]. Often, personas are
communicated in the form of a story, e.g., “Mary is a 35-year-old
woman who likes…” A persona can be understood as a story that
conveys user experiences that the decision makers would not
necessarily know otherwise. Consider the difference between
creating a software product for the nameless and faceless target
group of 24-35-year-old women, or for “Anna, a stressed single
mum wanting to better manage her time”.
Social scientists have shown that a smile has a profound
eect on perceptions by others. Overall, a smile has been found
to reinforce perceived similarity and identication [6, 8, 26].
Wang et al. [26] observed that the intensity of smile aects
interpersonal perceptions, specically the perceptions of warmth
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and competence. Hinsz and Tomhave [8] found that smiling has
a contagious eect, resulting in subjects echoing the positive
feeling, while Deutsch [6] found gender dierences in
perceptions of smiling and non-smiling individuals, so that
females are expected to smile more oen. Oa et al. [17] found
that smiling young people were considered as more extroverted
and ambitious than middle-aged and elderly smiling individuals.
In a similar vein, various studies in human-computer
interaction have explored the interaction of smiles and
technology. For example, Turner and Hunt [25] investigated
social network users’ assessment of other users’ personality
traits based on prole pictures and found that smiling had a
signicant impact on the personality assessments.
Despite the considerable interest in a smile in the context of
person perception, we could nd no existing work in the persona
domain on the impact of smiling pictures in personas, even
though the basis of personas is to present the persona as a real
person. Since personas are human-like user representations, they
are judged like individuals by other individuals, thus making
perceptual studies important in the persona domain.
Given that the persona’s facial picture impacts the way
personas are perceived by end users [21–23], understanding the
eect of a smile or absence of a smile has a direct impact on the
design and implementation of personas, along with their
potential use and eectiveness within organizations.
3 Research estions
Building on the ndings from previous research, we are
interested in the eect of using smiling images in persona
proles on persona perceptions. If the perception of a persona
can be inuenced by the choice of a smiling image, this has
direct implications for persona creation.
Our research question is: How does a smiling persona picture
aect persona perceptions?
Based on our research question, our hypotheses are:
H01: A smiling photo in a persona profile increases
perceived credibility
H02: A smiling photo in a persona profile increases
perceived likability
H03: A smiling photo in a persona profile increases
perceived similarity.
H04: A smiling photo in a persona profile increases the
perceived willingness to use.
We evaluate the impact of a smile in the persona prole on
four constructs: likability, credibility, similarity, and willingness
to use. ese constructs and their measurement items have been
validated in earlier work [24], and are dened as follows.
Credibility – Persona information is clearly presented to
the respondent; the persona seems authentic. Credibility is a
key concern of personas [4, 13].
Likability – The persona is liked by the respondent.
Likability is conceptually similar to attractiveness but a
more comprehensive construct [18].
Similarity – The respondent feels like the persona is like
him or her. Similarity to the personas is akin to the
identification of a common bond [11].
Willingness to use – The respondent would make use of
this persona in his or her work. Willingness to use is a
crucial perception, as it influences the adoption process of a
persona in practice [19].
Table 1 displays the measurement items for each construct.
Table 1: Variables and Items from Survey, Adapted from
Salminen et al. [24]
Measurement Items
This persona seems like a real person.
The persona seems natural.
The persona seems to have a personality.
The picture of the persona looks
I find this persona likable.
I could be friends with this persona.
This persona is interesting.
This persona feels like someone I could
spend time with.
I like the same things as this persona.
This persona feels like me.
The persona and I have similar opinions.
I can relate to this persona.
Willingness to
I would like to know more about this
I could see myself making use of the
information about this persona in my work.
This persona would improve my ability to
make decisions about the customers it
I found this persona helpful for
understanding the people it describes
4 Methodology
In a between-subjects experimental design, we present crowd
workers with persona proles containing the person either
smiling or not smiling.
4.1 Smiling and Non-smiling Pictures for
Persona Profiles
We obtain 24 picture pairs (48 total photos) of a person smiling
and not smiling by using online stock photo banks and
supplementing by employing a professional photographer.
Figure 2 shows examples of the obtained photos pairs.
Aer collecting the photos, we created the pairs of persona
proles (see Figure 1) using the automatic persona generation
method by An et al. [2, 3]. e key aributes of the created
persona proles include age, gender, location, and topics of
interests [2].
Figure 1: Example Treatment (Mature Woman Smiling) and (Mature Woman Not Smiling)
Although there are dierent layouts for persona proles [14],
in this research, we adopt a typical layout and information
content as presented in [9], as this represents a common layout
for personas. Altogether, we created 48 dierent persona prole
pairs by varying age, gender, and race. Previous research on
smile and person perceptions has found that gender has a
mediating inuence on perceived aributes of males and females
[6]. Similarly, ethnicity of the persona has been found inuential
in previous persona research for end user perceptions [23]. us,
by variating these variables we can investigate if the observed
eects are consistent across genders, age groups, and ethnicities.
Figure 2: Example Smiling and Not Smiling Pictures: (a)
Asian Mature Male, (b) Black Young Male, and (c) White
Young Female
Apart from changing the picture, all other information (e.g.,
topics of interest, most viewed content, quotes) was kept
unchanged in the persona proles. Apart from changing the
picture according to the experimental variables, all other
information (e.g., topics of interest, most viewed content, quotes)
was kept unchanged. Table 2 denes the information elements of
the persona prole.
4.2 Survey Creation
We utilize the constructs and items from the Persona Perception
Scale [24]. From this instrument, we chose the four constructs as
outlined above with their associated measurement items (Table
1). We created a questionnaire using the items outlined in [24] as
statements shown to respondents. For each statement, we
utilized a seven-point Likert scale with the options ranging from
(1) Strongly disagree to (7) Strongly agree.
Table 2: Sections in the Persona Profile, Adapted from
Jung et al. [9]
Name Persona’s name.
Picture Persona’s picture is one of the
Demographic information Persona’s age, gender, country
Industry Persona’s job
Education Level Persona’s education level
Marital Status Persona’s marital status
Interest Persona’s topics of interest.
Most Viewed Contents Persona’s content interacted with.
Quotes Persona’s quotes
Audience Size Size of the user segment
4.3 Data Collection
We recruited 50 respondents for each persona prole for 2,400
respondents in total. We used FigureEight, the crowdsourcing
platform. To control the answer quality, we undertook measures
outlined in [1]. First, we manually selected the participant
channels, specically FigureEight Elite, Clickworker, and
ClixSense in order to focus on high-quality networks. Second,
we set the participant quality level to Level 3 (Highest quality).
ird, we set a minimum time of 120 seconds for the experiment;
any answer taking less time than this would be disqualied.
Fourth, we ran the experiments in parallel to mitigate the
possibility of the same participants enrolling in many surveys.
e sampling was narrowed to four English-speaking countries:
United States (US), United Kingdom, Canada, and Australia. e
reward for lling in the survey was 0.30 US dollars.
e respondents were asked to reveal their thoughts about
the persona they were shown. We dened the persona as
follows: A persona is a ctitious person describing a bigger
customer segment. It can be understood as a typical or average
customer. We instructed the respondents to review the persona
information carefully. en, we asked the participants to answer
the statements about the persona. At any time while responding
to the survey, they could review the persona prole.
5 Results
We now return to our research question is (How does a smiling
persona picture aect the user’s perception of the persona?) and
the associated research hypotheses that a smiling photo in a
persona prole increases perceived credibility (H01), likability
(H02), similarity (H03), and willingness to use (H04).
A multivariate analysis of variance (AMNOVA was
conducted to determine the effects of smiling on perceived
credibility (H01), likability (H02), similarity (H03), and
willingness to use (H04) at the p<0.05 level of significance in
response to whether the person in the picture of the persona
profile was smiling or not smiling.
No significant differences were found regarding perceived
credibility (F(1, 2398) = 0.560, p = 0.454) on pictures with a
smiling persona (M = 4.763, SD = 1.168) and without a smiling
persona (M = 4.727, SD = 1.149).
Significant dierences were found regarding perceived
likability at the p < 0.001 level (F(1, 2398) = 7.091, p = 0.008), with
pictures with a smiling persona showing higher scores for likability
(M = 4.692, SD = 1.198) than pictures without a smiling persona
(M = 4.563, SD = 1.178).
Signicant differences were found regarding perceived
similarity at the p < 0.001 level (F(1, 2398) = 8.307, p = 0.004),
with pictures with a smiling persona showing higher scores for
similarity (M = 4.493, SD = 1.215) than pictures without a smiling
persona (M = 4.353, SD = 1.171).
Finally, signicant differences were also found regarding
willingness to use at the p < 0.05 level (F(1, 2398) = 5.179, p =
0.023), with pictures with a smiling persona exhibiting higher
scores for willingness to use (M = 4.620, SD = 1.125) than pictures
without a smiling persona (M = 4.518, SD = 1.067). Means and
standard deviations are presented in Table 3.
We also conducted correlation analyses, as shown in Table 4.
Factor analyses for the scale validation have been conducted in
previous work [24].
A significant and positive association was found between
Liking and Similarity (r(2398) = 0.647, p < 0.001), between Liking
and Willingness to Use (r(2398) = 0.644, p < 0.001), and also
between Liking and Credibility (r(2398) = 0.647, p < 0.001).
Furthermore, significant and positive associations were also
found between Similarity and Willingness to Use (r(2398) =
0.599, p < 0.001) and between Similarity and Credibility (r(2398)
= 0.505, p < 0.001). Finally, Willingness to Use was found to have
a significant and positive association with Credibility (r(2398) =
0.612, p < 0.001).
Table 3: Mean and Standard Deviations for Tested
Constructs. Gender, Age, or Ethnicity Did Not Have
Significant Interaction Effects.
Constructs Average Rating (Smiling
& Non-smiling)
Standard Deviation
(Smiling & Non-smiling)
Credibility 4.763
Likability 4.692
Similarity 4.493
to use
(Bold denotes significant difference at p value presented in text.)
Table 4: Correlations Table for Liking, Similarity, and
Credibility Liking Similarity Willingness
to Use
Credibility 1 0.647 *** 0.505 *** 0.612 ***
Liking 1 0.647 *** 0.644 ***
Similarity 1 0.599 ***
to Use
Note: *** p < 0.001; ** p < 0.01; * p < 0.05
6 Discussion and Implications
A summary of hypotheses testing is presented in Table 5. From
these results, findings are that a smile in the persona picture
increases likability of and the similarity with the persona, as
perceived by the individual exposed to the persona. Moreover,
the willingness to use a persona is increased by showing smiling
pictures. is encourages persona creators to use pictures with
smiling people, as smile results in several positive perceptions
from individuals exposed to personas. is in line with ndings
of the smile perception literature [8, 26] suggesting personas are
seen as people-like to those viewing them.
e lack of support for H01 can be explained by the ndings
of Wang et al. [26] that suggest a tradeo between likability and
credibility. In their context, a smile increased the likability of a
marketer, however, reducing the perception of competence.
us, a smile can contribute to the impression of “fakeness”, at
least among some participants. is eect should be explored
further in subsequent studies by varying the intensity of smile in
the tested persona pictures. Future studies should also explore
the interaction eects between constructs investigated here, e.g.,
the relationship between positive perceptions (likability,
similarity) on willingness to use.
Concerning the limitations, our sample was restricted to four
English-speaking countries. It remains a question for future
research to validate the findings in other cultural contexts and
regions of the world, where perceptions toward others, in
general, may differ. Moreover, experimental conditions, such as
the quality of photos, lighting, and backgrounds could be tested
in future research, as these might have confounding effects.
Future research may also examine how images interact with
other informational elements of the persona profile (i.e., text,
numbers) to form the overall persona perception.
Table 5: Results for Hypothesis Testing – () Indicates
Confirmed, () Not Confirmed
H01: A smiling photo in
a persona profile
increases perceived
There can be a tradeoff
between positive affections
and credibility (i.e., sense of
H02: A smiling photo in
a persona profile
increases perceived
Individuals are more likely
to feel positive affections
toward a smiling persona.
H03: A smiling photo in
a persona profile
increases perceived
Individuals identify more
easily with smiling
H04: A smiling photo in
a persona profile
increases the perceived
willingness to use
Individuals are keener to
learn more about the
persona they like.
As practical advice for persona creators, smiles in persona
pictures seem to play a signicant role in perceptions by
individuals, so it is probably best to use pictures of people
smiling, as smiling pictures increase perceived likeability,
similarity, and willingness to use. Naturally, this would be
moderated by the case, as there may be contexts and persona use
cases (i.e., funerals, extremely professional profiles, etc.) where a
smile is not appropriate. In general, though, including a smile in
a persona profile picture can be recommended.
7 Conclusion
In general, a smile elicits a positive emotional response to
personas, especially with likeability, similarity, and willingness
to use. erefore, as a general guideline, it is recommended to
use photos of smiling people when designing persona proles.
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... Thus, researchers can focus on a subset of them. As an example, this was done in a study by Salminen et al. [123] that selected four constructs from the PPS to investigate how smiling in persona pictures affects user perceptions. ...
... How does increased persona transparency affect persona perceptions? (in review)How does using a smiling vs. non-smiling picture in the persona profile affect persona perceptions?[123,124] How does the use of toxic quotes shape persona perceptions? ...
Although used in many domains, the evaluation of personas is difficult due to the lack of validated measurement instruments. To tackle this challenge, we propose the Persona Perception Scale (PPS), a survey instrument for evaluating how individuals perceive personas. We develop the scale by reviewing relevant literature from social psychology, persona studies, and Human-Computer Interaction to find relevant constructs and items for measuring persona perceptions. Following initial pilot testing, we conduct an exploratory validation of the scale with 412 respondents and find that the constructs and items of the scale perform satisfactorily for deployment. The research has implications for both academic researchers and persona developers. Using the PPS, researchers and designers can evaluate how different persona designs affect individual perceptions of personas, for example persona users’ (e.g., designers, marketers, software developers) perceived credibility of the persona and their willingness to use it. Because persona perceptions are associated with persona acceptance and adoption, using a perceptual measurement instrument can improve the chances of persona adoption and use in real organizations.
... In this way, gender is highlighted as a demographic point of interest in both users and user perceptions of gendered personas. In [61], for instance, the authors conducted a survey measuring user perceptions of pseudopersonas, specifically in response to pairs of identical profiles where the profile features a smiling picture versus a non-smiling picture. They found gender to be an influential attribute of generated personas, wherein variation in the gender of participants resulted in perceptual variation of the gendered personas. ...
Information access research (and development) sometimes makes use of gender, whether to report on the demographics of participants in a user study, as inputs to personalized results or recommendations, or to make systems gender-fair, amongst other purposes. This work makes a variety of assumptions about gender, however, that are not necessarily aligned with current understandings of what gender is, how it should be encoded, and how a gender variable should be ethically used. In this work, we present a systematic review of papers on information retrieval and recommender systems that mention gender in order to document how gender is currently being used in this field. We find that most papers mentioning gender do not use an explicit gender variable, but most of those that do either focus on contextualizing results of model performance, personalizing a system based on assumptions of user gender, or auditing a model's behavior for fairness or other privacy-related issues. Moreover, most of the papers we review rely on a binary notion of gender, even if they acknowledge that gender cannot be split into two categories. We connect these findings with scholarship on gender theory and recent work on gender in human-computer interaction and natural language processing. We conclude by making recommendations for ethical and well-grounded use of gender in building and researching information access systems.
... This interpretation is generally in line with previous HCI research regarding the foundational impact of photos in persona profiles (Hill et al. 2017;Salminen et al. 2018d;Salminen et al. 2019b). Possibly, stock photos can appear, at times, less realistic than photos of 'real people' because they are 'too shiny, too perfect' (or 'too smiling' [Salminen et al. 2019e]). Thus, if the generator's outputs are closer to real people than stock photos in their appearance, it is possible that these pictures are deemed more realistic than stock photos. ...
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We conduct two studies to evaluate the suitability of artificially generated facial pictures for use in a customer-facing system using data-driven personas. STUDY 1 investigates the quality of a sample of 1,000 artificially generated facial pictures. Obtaining 6,812 crowd judgments, we find that 90% of the images are rated medium quality or better. STUDY 2 examines the application of artificially generated facial pictures in data-driven personas using an experimental setting where the high-quality pictures are implemented in persona profiles. Based on 496 participants using 4 persona treatments (2 × 2 research design), findings of Bayesian analysis show that using the artificial pictures in persona profiles did not decrease the scores for Authenticity, Clarity, Empathy, and Willingness to Use of the data-driven personas. ARTICLE HISTORY
Purpose The “what is beautiful is good” (WIBIG) effect implies that observers tend to perceive physically attractive people in a positive light. The authors investigate how the WIBIG effect applies to user personas, measuring designers' perceptions and task performance when employing user personas for the design of information technology (IT) solutions. Design/methodology/approach In a user experiment, the authors tested six different personas with 235 participants that were asked to develop remote work solutions based on their interaction with a fictitious user persona. Findings The findings showed that a user persona's perceived attractiveness was positively correlated with other perceptions of the persona. The personas' completeness, credibility, empathy, likability and usefulness increased with attractiveness. More attractive personas were also perceived as more agreeable, emotionally stable, extraverted and open, and the participants spent more time engaging with personas they perceived attractive. A linguistic analysis indicated that the IT solutions created for more attractive user personas demonstrated a higher degree of affect, but for the most part, task outputs did not vary by the personas' perceived attractiveness. Research limitations/implications The WIBIG effect applies when designing IT solutions with user personas, but its effect on task outputs appears limited. The perceived attractiveness of a user persona can impact how designers interact with and engage with the persona, which can influence the quality or the type of the IT solutions created based on the persona. Also, the findings point to the need to incorporate hedonic qualities into the persona creation process. For example, there may be contexts where it is helpful that the personas be attractive; there may be contexts where the attractiveness of the personas is unimportant or even a distraction. Practical implications The findings point to the need to incorporate hedonic qualities into the persona creation process. For example, there may be contexts where it is helpful that the personas be attractive; there may be contexts where the attractiveness of the personas is unimportant or even a distraction. Originality/value Because personas are created to closely resemble real people, the authors might expect the WIBIG effect to apply. The WIBIG effect might lead decision makers to favor more attractive personas when designing IT solutions. However, despite its potential relevance for decision making with personas, as far as the authors know, no prior study has investigated whether the WIBIG effect extends to the context of personas. Overall, it is important to understand how human factors apply to IT system design with personas, so that the personas can be created to minimize potentially detrimental effects as much as possible.
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There has been little research into whether a persona's picture should portray a happy or unhappy individual. We report a user experiment with 235 participants, testing the effects of happy and unhappy image styles on user perceptions, engagement, and personality traits attributed to personas using a mixed-methods analysis. Results indicate that the participant's perceptions of the persona's realism and pain point severity increase with the use of unhappy pictures. In contrast, personas with happy pictures are perceived as more extroverted, agreeable, open, conscientious, and emotionally stable. The participants’ proposed design ideas for the personas scored more lexical empathy scores for happy personas. There were also significant perception changes along with the gender and ethnic lines regarding both empathy and perceptions of pain points. Implications are the facial expression in the persona profile can affect the perceptions of those employing the personas. Therefore, persona designers should align facial expressions with the task for which the personas will be employed. Generally, unhappy images emphasize realism and pain point severity, and happy images invoke positive perceptions.
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During exceptional times when researchers do not have physical access to users of technology, the importance of remote user studies increases. We provide recommendations based on lessons learned from conducting online user studies utilizing four online research platforms (Appen, MTurk, Prolific, and Upwork). Our recommendations aim to help those inexperienced with online user studies. They are also beneficial for those interested in increasing their proficiency, employing this increasingly important research methodology for studying people’s interactions with technology and information.
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Increased access to data and computational techniques enable innovations in the space of automated customer analytics, for example, automatic persona generation. Automatic persona generation is the process of creating data-driven representations from user or customer statistics. Even though automatic persona generation is technically possible and provides advantages compared to manual persona creation regarding the speed and freshness of the personas, it is not clear (a) what information to include in the persona profiles and (b) how to display that information. To query into these aspects relating information design of personas, we conducted a user study with 38 participants. In the findings, we report several challenges relating to the design of automatically generated persona profiles, including usability issues, perceptual issues, and issues relating to information content. Our research has implications for the information design of data-driven personas.
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We propose a novel approach for isolating customer segments using online customer data for products that are distributed via online social media platforms. We use non-negative matrix factorization to first identify behavioral customer segments and then to identify demographic customer segments. We employ a methodology for linking the two segments to present integrated and holistic customer segments, also known as personas. Behavioral segments are generated from customer interactions with online content. Demographic segments are generated using the gender, age, and location of these customers. In addition to evaluating our approach, we demonstrate its practicality via a system leveraging these customer segments to automatically generate personas, which are fictional but accurate representations of each integrated behavioral and demographic segment. Results show that this approach can accurately identify both behavioral and demographical customer segments using actual online customer data from which we can generate personas representing real groups of people.
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In this research, we investigate if and how more photos than a single headshot can heighten the level of information provided by persona profiles. We conduct eye-tracking experiments and qualitative interviews with variations in the photos: a single headshot, a headshot and images of the persona in different contexts, and a headshot with pictures of different people representing key persona attributes. The results show that more contextual photos significantly improve the information end users derive from a persona profile; however, showing images of different people creates confusion and lowers the informativeness. Moreover, we discover that choice of pictures results in various interpretations of the persona that are biased by the end users' experiences and preconceptions. The results imply that persona creators should consider the design power of photos when creating persona profiles.
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Personas are widely used in software development, system design, and HCI studies. Yet, their evaluation is difficult, and there are no recognized and validated measurement scales to date. To improve this condition, this research develops a persona perception scale based on reviewing relevant literature. We validate the scale through a pilot study with 19 participants, each evaluating three personas (57 evaluations in total). This is the first reported effort to systematically develop and validate an instrument for persona perception measurement. We find the constructs and items of the scale perform well, with factor loadings ranging between 0.60 and 0.95. Reliability, measured as Cronbach's Alpha, is also satisfactory, encouraging us to pursue the use of the scale with a larger sample in future work.
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We present Automatic Persona Generation (APG), a methodology and system for quantitative persona generation using large amounts of online social media data. The system is operational, beta deployed with several client organizations in multiple industry verticals and ranging from small-to-medium sized enterprises to large multi-national corporations. Using a robust web framework and stable back-end database, APG is currently processing tens of millions of user interactions with thousands of online digital products on multiple social media platforms, such as Facebook and YouTube. APG identifies both distinct and impactful user segments and then creates persona descriptions by automatically adding pertinent features, such as names, photos, and personal attributes. We present the overall methodological approach, architecture development, and main system features. APG has a potential value for organizations distributing content via online platforms and is unique in its approach to persona generation. APG can be found online at
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One of the reasons for using personas is to align user understandings across project teams and sites. As part of a larger persona study, at Al Jazeera English (AJE), we conducted 16 qualitative interviews with media producers, the end users of persona descriptions. We asked the participants about their understanding of a typical AJE media consumer, and the variety of answers shows that the understandings are not aligned and are built on a mix of own experiences, own self, assumptions, and data given by the company. The answers are sometimes aligned with the data-driven personas and sometimes not. The end users are divided in two groups: news producers who have little interest in having data-based insights of news consumers and producers for social media platforms who have more interest in this information.
We investigate whether additional photos beyond a single headshot makes a persona profile more informative without confusing the end user. We conduct an eye-tracking experiment and qualitative interviews with digital content creators after varying the persona in photos via a single headshot, a headshot and photo of the persona in different contexts, and a headshot with photos of different people with key persona attributes the gender and age. Findings show that contextual photos provide significantly more persona information to end users; however, showing photos of multiple people engenders confusion and lowers informativeness. Also, as anticipated, viewing additional photos requires more cognitive focus, which is measured by eye-tracking metrics; these metrics are correlated with levels of informativeness and confusion. Furthermore, various interpretations of the persona based on the choice of photos are biased by the end users’ experiences and preconceptions. Concerning persona design, findings indicate that persona creators need to consider the intended persona use objectives when selecting photos and when producing persona profiles. Using contextual photos can improve informativeness, but this demands more cognitive focus from end users. Thus, adding contextual photos increases the perceived informativeness of the persona profile without being obfuscating, but multiple photos of different people do evoke confusion about the targeted persona.
Instagram and other photo-based social networking sites have emerged as a popular medium. Previous studies mainly focused on social media texts, but the current study deals with the relationships between the characteristics of Instagram users and the features of their photos. The Big Five personality traits and gender were employed as the variables for user characteristics. Content category, the number of faces, the emotions on the faces, and the pixel derived features were employed as the variables for photo characteristics. An online survey of 179 university students was conducted to measure user characteristics, and 25,394 photos in total were downloaded and analyzed from the respondents’ Instagram accounts. Results suggested that content category is associated with extraversion and gender of users. The number of faces is associated with extraversion, agreeableness, and openness of users. Extraversion, agreeableness, and openness of users were partly associated with emotions expressed on the faces in their photos. Correlations were observed among some pixel features and extraversion, agreeableness, conscientiousness, and gender of users. It was also observed that the Big Five personality traits, except for gender, can be predicted by above variables. Implications and limitations are discussed and suggestions for future research are suggested.