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The effects of age on symbol comprehension in central rail hubs in Taiwan
Applied Ergonomics 43 (2012) 1016e1025
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The effects of age on symbol comprehension in central rail hubs in Taiwan Yung-Ching Liu*, Chin-Heng Ho Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Douliu, Yunlin 640, Taiwan
a r t i c l e i n f o
a b s t r a c t
Article history: Received 15 April 2011 Accepted 15 February 2012
The purpose of this study was to investigate the effects of age and symbol design features on passengers’ comprehension of symbols and the performance of these symbols with regard to route guidance. In the first experiment, 30 young participants and 30 elderly participants interpreted the meanings and rated the features of 39 symbols. Researchers collected data on each subject’s comprehension time, comprehension score, and feature ratings for each symbol. In the second experiment, this study used a series of photos to simulate scenarios in which passengers follow symbols to arrive at their destinations. The length of time each participant required to follow his/ her route and his/her errors were recorded. Older adults experienced greater difficulty in understanding particular symbols as compared to younger adults. Familiarity was the feature most highly correlated with comprehension of symbols and accuracy of semantic depiction was the best predictor of behavior in following routes. Ó 2012 Elsevier Ltd and The Ergonomics Society. All rights reserved.
Keywords: Age Comprehension of symbols Central rail hub
1. Introduction The Taiwan High Speed Rail (THSR) was established in 2007 to solve traffic problems in western Taiwan, and the Kaohsiung rapid transit system was completed in 2008. Following the completion of these two transportation systems, central rail hubs, which include a railway station, a THSR station, and a rapid transit station, have appeared in Taiwan. Many different people pass through these stations for different purposes. For example, local residents in the Kaohsiung rapid transit station may actually intend to take the train or high speed rail, and tourists in the THSR or railway station may intend to travel by rapid transit. Various systems of directional and informational symbols are used in these hubs to guide passengers, provide them with information, and help them identify directions according to their travel intentions. However, not only are these three systems of symbols in the central rail hubs different but many symbols have been modified and new symbols have been added. Because the symbols in these transportation hubs are not standardized and some residents or foreign tourists may not have seen these symbols before, comprehension of symbols in such hubs is important for prospective users of transportation systems. It was hypothesized that detailed investigation of the comprehension of directional and informational symbols in central railway hubs was an important need. When the tourists can’t understand the
* Corresponding author. Tel.: þ886 5 5342601; fax: þ886 5 5312073. E-mail addresses: [email protected], [email protected] (Y.-C. Liu).
symbols of the central rail hubs, they experience confusion and are at a loss of what to do next. They are likely to feel even more lost in central rail hubs, and can easily become more flustered. These conditions could disrupt order and be a serious issue from a safety perspective (Building Research Establishment for Office of the Deputy Prime Minister, 2006). The advantages of using symbols are: (a) symbols can quickly communicate instructions; (b) use of symbols avoids problems related to inadequate reading skills or linguistic unfamiliarity; and (c) passengers may remember symbols better than they remember text (Wogalter et al., 1997). Because symbols provide a languagefree method of communicating, they can potentially be understood by diverse groups which vary in life experience and reading ability. However, studies have proved that many symbols currently in use are difficult to understand (Collins and Lerner, 1982; Davies et al., 1998; Wolff and Wogalter, 1993; Lesch, 2003). Picha et al. (1995, 1997) conducted multiple evaluations of comprehension of different traffic symbols by more than 3000 drivers in Texas. They found that low comprehension levels existed for several symbols. Paninti (1989) compared several alternative symbols used to signify work zones, and found that symbols with good physical resemblance to what they were meant to signify were all well understood, even when the details of these symbols were different. In contrast, when the intended message was difficult to convey symbolically, all proposed alternative symbols were not clearly understood. Measurement of symbol design is based on recognition rate. McDougall et al. (1999) measured characteristics of symbols or
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icons according to features like familiarity, concreteness, simplicity, meaningfulness, and accuracy of semantic depiction. Ng and Chan (2007) also used these same features to investigate the guessability of traffic symbols. These features have become central concerns in research on symbols and icons (Ng and Chan, 2008, 2009; Chan and Ng, 2010). Familiarity indicates the frequency with which icons have been encountered. Rosson (2002) also found that familiarity improves comprehension, a conclusion similar to that of Ben-Bassat and Shinar (2006). Concreteness indicates the degree to which something is material and genuine. Icons or symbols are concrete if they depict real objects, materials, or people; otherwise, they are abstract. Symbols with concrete design are more easily understood than those with ambiguous designs (Wolff and Wogalter, 1993; Foster and Afzainia, 2005; Passini et al., 2008; Rousek and Hallbeck, 2011). Symbols are regarded as complex if they are intricate or depict a lot of detail, and simple if they contain only few elements or details. Dewar (1999) noted that complicated symbols are more difficult to comprehend compared to simpler symbols. Meaningfulness refers to the degree of significance viewers attribute to icons and is seen as an important characteristic of symbol design (Huang et al., 2002; Lin, 1992). Accuracy of semantic depiction indicates how closely, accurately, and comprehensively the design of the symbol represents what the symbol is meant to signify. Lesch (2008) suggested that positive symbol characteristics are easy to understand, and that using positive symbol characteristics is more effective in helping viewers to understand symbols as compared to symbol comprehension training. Shinar et al. (2003) found that ‘infrequent symbols are more likely to be miscomprehended and less likely to be correctly learned’ by drivers. It was hypothesized that users identify symbols more easily if the symbols are familiar to them. Another hypothesis of this study was that because concrete symbols provide a direct visual aid to help viewers comprehend the intended meaning of such depiction, users comprehend concrete symbols better than they do abstract symbols. It was expected that simple symbols to be easier to identify than complex symbols because complex symbols have the potential to confuse or complicate understanding (Bruyas et al., 1998). The meaningfulness of a symbol refers to the ability of a symbol to elicit attribution of meaning from users (Preece et al., 1994), so researchers expected higher comprehension scores for meaningful symbols. Higher ratings for accuracy of semantic depiction indicated that symbols given such ratings were clearly associated with the concepts they were meant to signify, and thus should lead to higher comprehension scores. Young and Wogalter (1990) indicated that users will better identify a symbol that precisely communicates the semantic meaning. Statistics show that 20% of the population in most developed countries is older than 60, and that one third of the earth’s population will be over 60 years old by 2050 (United Nations Population Division, 2009). In 2009, 2.45 million people (10.7% of the population) in Taiwan were over 65 years old (Statistical Yearbook of Interior, Taiwan, 2010), and the population of elderly adults will only continue to increase. According to population statistics forecast in the international database of the U.S. Census Bureau, 12.3% of the population in Taiwan will be older than 65 in 2015. The 65 þ age group represents the fastest growing age group in Taiwan (U.S. Census Bureau, 2010). Regarding the effect of aging, past studies have indicated that the use of symbols may pose special problems for the elderly (Collins and Lerner, 1982; Dewar et al., 1994; Easterby and Hakiel, 1981; Hancock et al., 1999; Jones, 1992; Lesch, 2003; Morrell et al., 1990; Shinar et al., 2003; Zwaga and Boersema, 1983; AlGadhi et al., 1994; Scialfa et al., 2008). Hancock et al. (1999) indicated that aging is generally associated with a decline in various
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perceptual and cognitive abilitiesdfor example, vision and working memory. However, Al-Madani and Al-Janahi (2002) found that the differences between the different age groups showed that drivers in the younger age group (16e24 years) comprehend significantly less well than those in the older groups (35e44 and over 44 years). Ng and Chan (2008) also indicated that the poor performance of older subjects was not evident. In related experimentation, Dewar et al. (1994) showed 85 color slides of standard US symbol symbols to 480 volunteer licensed drivers from the USA and Canada. Results showed that for 39% of the symbols examined, the understanding of older drivers was poorer than that of younger drivers. Regarding the remaining symbols, no difference in comprehension with regard to age was observed. Jones (1992) reported that a survey of older drivers in Illinois also showed that older drivers failed to understand some common traffic control signs. Shinar et al. (2003) evaluated levels of comprehension of highway traffic symbols used in different countries, and found that “older drivers tend to do less well at symbol comprehension than other drivers, including novice drivers and repeated violators”. Lesch (2003) examined the effectiveness of three different training conditions to improve comprehension and remembrance of warning symbols for younger (18e35 years old) and older (50e67 years old) participants. Results showed that although training improved participants’ accuracy and speed of response on a comprehension test, the performance of older participants was significantly poorer than that of younger participants, both before (37% vs. 52% of correct answers, respectively, in the older and younger participant groups) and after training (68% vs. 88% of correct answers on the immediate post-test). Older participants also found it more difficult to reject incorrect meanings (55% vs. 68% of correct answers). According to the abovementioned research, the effects of aging significantly influence comprehension of symbols, and the design of symbols in central rail hubs should take elderly adults into account. The aims of this study were as follows, and two experiments were used to assess the objectives. (1) To evaluate the comprehension of symbols in Taiwanese central rail hubs by two user groups of different ages; (2) To explore the correlation between symbol design features and symbol comprehension; (3) To examine the differences among participants’ symbol comprehension performance with regard to various categories of symbols
2. Methods 2.1. Participants Thirty older participants (20 males and 10 females) and thirty younger participants (18 males and 12 females) were recruited to participate in this study. The older participants ranged from 65 to 74 years old (average ¼ 67.6 years old), and the younger participants ranged from 23 to 30 years old (mean ¼ 26.5 years old). Because the users of these central rail hubs in real life include local residents and outside tourists and their demographic details reflect diversity, one-third of the participants recruited for this study were local residents and the others were from other places. All participants had never been to the central railway hub in Kaohsiung City, Taiwan and had to pass a health screening examination which tested vision (near sightedness, farsightedness, 18/20 vision or better), and color blindness (ability to pass the Ishihara card color blindness test) (Ishihara, 1993). Each participant was paid 10 US dollars for participating in this study.
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2.2. Equipment This study used a Nikon COOLPIX S3 (at 800 600 resolution) to take the photographs. The objects in the images were modified with Adobe Photoshop CS. A microcomputer (PC) was used to control the stimulus presentation and the images were shown on a 17 in. color monitor at a viewing distance of approximately 60 cm. Flash software was used to develop the stimuli in both experiments and record response time, and Microsoft PowerPoint was used to display the symbols while participants evaluated the individual features of each symbol. 2.3. Experiment 1: comprehension of symbols and evaluation of symbol features Thirty-nine directional symbols collected from the central rail hub in Kaohsiung City, Taiwan were used in this study. The symbols in central rail hubs collected in the study help passengers identify directions and provide them with information, and were divided into the symbols from the Taiwan High Speed Rail (THSR), Kaohsiung rapid transit, and railway stations. Five traffic signs used in Taiwan were utilized for practice trials prior to testing participants with the 39 symbols. Flash software was used to develop the stimuli for symbol comprehension and record the length of response time each participant required when identifying symbols. All the symbols were shown in 7 cm 7 cm squares with no boundaries and depicted at the centre of the computer screen using Flash software. Participants viewed the symbols at a distance of 60 cm (subtending 6.67 ) from the screen. All participants pressed the “Space” key to begin the trial, after which the symbols began to appear on the screen. Participants were required to view and conceptually grasp the symbol and then press the “Space” key again to stop the display of stimuli. Verbal icons in Chinese to describe the meaning of symbols were used, and the responses of participants were recorded by a recorder. The responses of participants to each symbol were classified into one of the following three categories measuring accuracy: Correct and complete (coded as þ2), partially correct (e.g. “car park No. 1”, instead of “car park”dcoded as þ1), or incorrect (e.g. “tickets”, instead of “convenience store”dcoded as 0). Two researchers used this guide in scoring the comprehension test, and the average comprehension score of each participant for each symbol was calculated. Additionally, participants filled out an evaluation sheet for symbol features. This evaluation sheet allowed participants to rate symbol features and was selected from the research of Ng and Chan (2007). Participants were instructed about the rating instructions and the meanings of the terms familiarity, concreteness, simplicity, meaningfulness, and accuracy of semantic depiction, and three additional symbols were provided to familiarize them with the rating symbol feature at the beginning of the symbol feature evaluation. Participants were asked to subjectively rate the familiarity (0 ¼ very unfamiliar, 100 ¼ very familiar), concreteness (0 ¼ clearly abstract, 100 ¼ clearly concrete), simplicity (0 ¼ very complex, 100 ¼ very simple), meaningfulness (0 ¼ completely meaningless, 100 ¼ completely meaningful), and accuracy of semantic depiction (0 ¼ very weakly related, 100 ¼ very strongly related) for each of the 39 symbols. These symbols were presented on the computer screen using Microsoft PowerPoint. 2.4. Experiment 2: scenarios of following directional signs This study also used a series of photos, taken from inside the central railway hub in Kaohsiung City, to build various scenarios. Flash software was used to simulate scenarios in which passengers followed guide signs to arrive at arranged destinations. Participants
were the same as those in experiment 1 and were asked to imagine that they were inside the central rail hub in Kaohsiung at the beginning. Participants were provided with a verbal description of the experiment procedure and invited to ask questions about the experiment before signing a consent form. Three practice trials that followed an introduction of the experiment were provided to the participants to practice and become familiar with Experiment 2. In each trail Participants were told to do something at the station (i.e. buy a ticket) or use specific public facilities (i.e. car park, or toilet), and then participants pressed the “Space” key to begin the trial and searched for and followed directional signs in the photos. Participants controlled the direction of their routes using the up, down, left, and right keys of the keyboard. If participants selected the correct route from directional signs in a photo, then a photo with related directional signs appeared on the screen; if the wrong route was selected from the first photo, the photo that appeared would be one without any symbols, thereby indicating to the participant that he/she was lost, and he/she would then need to return to the first photo and make another selection for the route to take. When participants had responded with five continuously correct directional choices, the trial ended. Thirty-nine directional route scenarios with guidance symbols were developed using Flash software. The length of time each participant spent to follow routes and the number of errors (wrong direction) was recorded in each trial. The number of errors for a particular trial meant the number of times participants selected the wrong route. 2.5. Procedure Within the first 15 min of the trial, participants completed a consent form and undertook a health screening examination consisting of a formal vision test and color blindness test. Before commencement of the formal experiment, five traffic signs used in Taiwan were used for practice trials. Each participant completed 39 symbol comprehension trials in random order. Next, participants were given brief instructions for evaluating and rating symbol features. Participants subjectively rated each symbol according to familiarity, concreteness, simplicity, meaningfulness, and accuracy of semantic depiction. One week from Experiment 1, each participant was asked to participate in Experiment 2, which involved various scenarios of following directional signs or symbols. After being briefed about this experiment and participating in five practice trials, participants completed 39 trial scenarios in random order. 2.6. Data collection and analysis Researchers collected data on each subject’s comprehension time, comprehension score, and feature ratings for each symbol in experiment 1; and the length of time each participant spent to follow his/her route and the number of errors were recorded in experiment 2. Data collected for this present study were analyzed by means of four types of analyses. First, reliability analysis was used to examine the internal consistency in measuring comprehension rates of the symbols’ features. The second kind of analysis investigated some of the differences in symbol comprehension among the 39 symbols. Cluster analysis was used to classify the categories of symbol comprehension in this study, and Ward’s method was used to conduct clustering. In order to examine the effects of age, the third kind of analysis involved a series of T-tests to assess variations in symbol comprehension and other elements of performance as a function of age. The purpose of the fourth type of analysis was to identify the correlation, among both elderly and younger participants, between symbol comprehension and related
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Fig. 1. Process of testing symbol comprehension: (a) please press SPACE bar to start the experiment; (b) Viewing and comprehension of symbol; and (c) Verbal answer indicating perceived meaning of symbol.
elements of performance and symbol features. Canonical correlation analysis was utilized to examine the relationship between symbol features and symbol comprehension performance. 3. Results
required to follow routes and number of errors). The 39 symbols included 26 well-designed symbols,10 poorly-designed symbols, and 3 easy to misunderstand symbols. Table 1 illustrates the performance of participants in the three symbol categories. Table 2 presents respective comprehension rates for each of the 39 symbols, and Fig. 3 shows the symbol features in each symbol category.
3.1. Categories of symbols In experiment 1, the Standard Cronbach alpha of 0.929 reflected a high internal consistency in the feature ratings for each symbol. Experimental results showed significant differences among symbol comprehension, with some symbols being fully understood by most respondents, and others being either misunderstand or not fully understood by 80% or more of the respondents in a fairly uniform manner in both groups. However, the error patterns were not the same for all symbols. In a very few cases, poorer comprehension performance was due to partially correct answers; however, the most common errors were due to unknown or misunderstood meanings. For example, the symbol “Kaohsiung Rapid Transit” (
) might be misunderstood as Kaohsiung Rail Train Station.
Cluster analysis methods were used to classify the categories of symbol comprehension in this study, and Ward’s method of cluster analysis was used to conduct clustering. The screen plot in Figs.1 and 2 was examined to determine the number of categories in this study. The difference coefficient tended to level off after the first three clusters; thus, this study set three cluster criterions. The three categories of symbols examined were well-designed symbols (optimal performance), poorly-designed symbols (generating longest response time and poorest comprehension scores), and symbols easy to misunderstand (average response time but low comprehension score, as well as significantly poorer scores on time
3.2. Differences between age groups with regard to symbol comprehension performance and evaluation of features According to the data scale, ManneWhitney U test which is a nonparametric statistical measure was used to evaluate age effects on comprehension scores, number of errors, and symbol features in three symbol categories. Highly significant differences were found in symbol comprehension and related elements of performance between the two groups of participants. The results of a classification of symbols into symbol comprehension categories based on participant performance were different for each age group. Results indicated that the average reaction time for symbol comprehension, average comprehension score, average time required to follow routes, and average number of errors (all p-value ＜0.05) were significantly different between the two groups, and that the performance of older participants was poorer than that of younger participants in three symbol comprehension categories. Regardless of any symbol comprehension category, the responses of young participants were faster and more accurate (Well-designed symbols: avg. response time ¼ 4.530 s, avg. comprehension score ¼ 1.817; poorly-designed symbols: avg. response time ¼ 7.343 s, avg. comprehension score ¼ 0.437; easy to misunderstand symbols: avg. response time ¼ 6.167 s, avg. comprehension score ¼ 0.48) regarding symbol comprehension, and younger participants were also able to search for and determine the correct directional symbols more rapidly and smoothly (Well-designed symbols: avg. time required to follow routes ¼ 8.063 , avg. number of errors ¼ 0.096; poorly-designed symbols: avg. time required to follow routes ¼ 10.181 s, avg. number of errors ¼ 0.278; easy to misunderstand symbols: avg. Table 1 Participant performance in the three symbol categories. Symbol categories Avg. Avg. Avg. time required Avg. number response comprehension to follow routes (s) of errors time (s) score
Fig. 2. Screen plot of cluster analysis. Screen plot was examined to determine the number of categories. The difference coefficient tended to level off after the first three clusters.
Well-designed 6.222 Poorly-designed 10.671 Easy to 7.933 misunderstand
1.708 0.306 0.323
9.688 12.487 24.493
0.257 1.171 3.640
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Table 2 Respective comprehension rates for each of the 39 symbols. Well-designed symbols
Familiarity
Concreteness
Simplicity
Meaningfulness
Accuracy of semantic depiction
Public telephone
95.08
87.25
92.17
97.58
92.92
Elevator
89
83.15
87.25
92.235
93.185
Toilet
98.92
97.085
96.585
97.75
98.08
Refreshments
88.58
72.75
80.75
86.085
74.335
Service counter
47.415
45.085
82.085
75.75
71.5
Bus stop
68.815
84.415
83.5
88.67
89.33
Car parking
95.5
94.915
95.415
95.92
96.25
High speed rail station
77
82.665
83.665
85.25
85.335
Nursery room
83.4
89.635
83.585
90.58
90.23
Rapid transit station
53.5
65.5
69.165
66.335
66.17
Elevator
87.22
88.985
88.75
95.665
92.08
Rail Train Station
74.17
82.415
83.415
86.58
89.165
Health Care Room
63.335
61.085
70.335
66.415
63.085
Toilet
94.915
93.25
93.5
93.33
95.415
Police
51.085
60.585
76.33
70.75
69.335
Elevator
91.4
91.33
92.33
93.65
92.98
First car park
85.92
88.75
93.5
91.58
94
High speed rail station
84.335
81
83.65
85.665
83.5
Taxi pickup area
92.47
87.25
94.965
94.715
94.165
Toilet
97.92
94.835
94.585
96.25
96
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Table 2 (continued ) Well-designed symbols
Familiarity
Concreteness
Simplicity
Meaningfulness
Accuracy of semantic depiction
Breastfeeding room
84.315
91.465
91.33
93.415
92.715
Service counter
52.83
56.915
81.915
84.17
78.25
Motorcycle parking
94.15
96.15
95.15
95.985
97
Escalator
83.355
87.37
83.56
90.155
87.915
Disabled facility
84.845
88.725
94.94
92.04
89.57
Motorcycle parking
91.835
88.9
87
94.98
94.75
Poorly-designed symbols
Familiarity
Concreteness
Simplicity
Meaningfulness
Accuracy of semantic depiction
Shuttle services
29.665
38.67
63
45.835
33.5
Rest room
36.33
42.5
57.75
60.165
57.5
Ticket vending machine
33.335
45.585
56.42
54.17
53.665
Kiss and ride
42.835
64.08
66.415
72.415
69.25
Tickets
23.585
38.585
58.835
46.165
47.92
Car rental
34.665
35.165
62.5
42.25
46.335
Ticket barrier
32.5
53.75
69.9
63.415
64.58
MPS liaison section
30.915
50.75
75.5
72.83
68.585
Travel services
46.75
61.17
64.915
61.17
61.08
Ticket vending machine
42.5
55.565
68.635
70.4
67.235
Easy to misunderstand symbols
Familiarity
Concreteness
Simplicity
Meaningfulness
Accuracy of semantic depiction
Kaohsiung rapid transit
30
22.165
65.165
43.835
37.42
Rail train station
47.665
50.08
58.22
61.5
53.83
Platform
35.5
28.25
52.835
30.915
23.915
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Familiarity 100 50
Semanic distance
Concreteness
0 Meaningfulness
Well-designed
Complexity
Bad-designed
Misunderstood
Fig. 3. Radar diagram of the three symbol categories.
time required to follow routes ¼ 18.397 s, avg. number of errors ¼ 1.39) compared to older participants (Well-designed symbols: avg. response time ¼ 7.914 s, avg. comprehension score ¼ 1.599, avg. time required to follow route ¼ 11.312 s, and avg. number of errors ¼ 0.418; poorly-designed symbols: avg. response time ¼ 13.998 s, avg. comprehension score ¼ 0.176, avg. time required to follow route ¼ 14.793 s, and avg. number of errors ¼ 2.063; easy to misunderstand symbols: avg. response time ¼ 9.7 s, avg. comprehension score ¼ 0.167, avg. time required to follow route ¼ 30.59 s, and avg. number of errors ¼ 5.89). However, the ratio of older to younger participants implied the performances of older participants decreased more greatly than those of younger ones in the “poorly-designed” symbol category, and comprehension scores of the older participants declined more greatly than those of the younger participants in the “easy to misunderstand” symbol category, as shown in Table 3. The results also show the differences in comprehension performance between the two groups significantly influenced time required to follow routes in the “easy to misunderstand” symbol category. Regarding symbol features, this study used ManneWhitney U test to examine the age effect, and the different symbol categories also showed differences in evaluation of symbol features between the two participant groups (Table 4). In the “well-designed” symbol category, the differences in each features evaluation between the two groups were not highly significant, and no significant difference appeared in familiarity and accuracy of semantic depiction. However, older participants reported less familiarity than young participants in the categories of “poorly-designed” (U ¼ 283.00, pvalue ¼ 0.013) and “easy to misunderstand” symbols (U ¼ 202.50, p-value ¼ 0.000). Older participants reported less accuracy of semantic depiction than young participants in the category of “poorly-designed” symbols as well (U ¼ 256.50, p-value ¼ 0.004). 3.3. Correlation between symbol features and symbol comprehension performance Canonical correlation analysis was utilized to examine the relationship between symbol features and symbol comprehension performance. Results of the analysis are shown in Table 5.
Table 5 illustrates the canonical loadings for the two significant canonical functions associated with both data sets. Variables with an absolute canonical loading value exceeding 0.5 indicate high correlation between the variable and the canonical variable (c). The canonical variate c1 explained 90.9% of the variance of all symbol features, and the canonical variate c2 explained 5.6% of the variance of all symbol features. Regarding symbol comprehension and route-following performance, the canonical variate h1 explained 61.1% of the variance of all performances, and the canonical variate h2 explained 26.0% of the variance of all performances. The two redundancy coefficients of participant performance were 0.581 and 0.110, respectively. This indicates that the canonical variates c1, c2 composed of symbol features explained almost 70% of the variance of all performances. Regarding evaluation of symbol features, the five features of familiarity, concreteness, simplicity, meaningfulness, and accuracy of semantic depiction in the first canonical function were highly positively correlated with canonical variate c1, and the canonical loadings were 0.992, 0.952, 0.950, 0.942, and 0.930, respectively. In the second canonical function, accuracy of semantic depiction was negatively correlated with canonical variate c2, and the canonical loading was 0.561. Regarding symbol comprehension performance, in the first canonical function, average response time, average comprehension score, average time required to follow routes, and average number of errors were either highly positively or negatively correlated with the canonical variate h1, and the canonical loadings were 0.764, 0.987, 0.599, and 0.726, respectively. In the second canonical function, only time required to follow routes and number of errors were highly positively correlated with canonical variate h2, the canonical loadings were 0.800 and 0.592, respectively. The results of canonical correlation analysis indicated a higher evaluation of symbol features, thus implying better participant performance, especially in terms of symbol comprehension scores. Familiarity had the highest correlation with symbol comprehension performance. This result implies designing symbols that users are familiar with, which would help users in comprehending these symbols promptly. Additionally, lower scores in terms of accuracy of semantic depiction resulted in increased time required to follow routes and a higher number of errors. This result implies that users would likely be lost in central rail hubs due to lower accuracy of semantic depiction for symbols in genuine circumstances. The designers should try to design symbols that users are familiar with and, in particular, have a higher accuracy of semantic depiction.
4. Discussion and conclusions The cluster analysis method was applied to classify symbols into symbol comprehension categories. Results indicated that 33.33% of directional symbols in central railway hubs were difficult to comprehend or easy to misunderstand for both older and younger adults. Average response time for poorly-designed symbols was longer than that for easy to misunderstand symbols. However, easy to misunderstand symbols resulted in increased time required to
Table 3 Symbol comprehension performance in three categories between two age groups. Well-designed symbols
Avg. Avg. Avg. Avg. a
response time (s) comprehension score time required to follow route(s) number of errors
Poorly-designed symbols
Easy to misunderstand symbols
Older
Younger
Ratioa
Older
Younger
Ratio
Older
Younger
Ratio
7.914 1.599 11.312 0.418
4.530 1.817 8.063 0.096
1.747 0.880 1.403 4.352
13.998 0.176 14.793 2.063
7.343 0.437 10.181 0.278
1.906 0.402 1.453 7.421
9.7 0.167 30.59 5.89
6.167 0.48 18.397 1.39
1.573 0.347 1.663 4.237
Ratio ¼ performance of older participant group/performance of younger participant group.
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Table 4 Evaluation of symbol features in three symbol categories between two age groups by ManneWhitney test.
Well designed
Poorly-designed
Easy to misunderstand
Older Younger p-value Older Younger p-value Older Younger p-value
Familiarity
Concreteness
Simplicity
Meaningfulness
Accuracy of semantic depiction
81.827 80.581 0.539 29.216 41.4 0.013 29.22 46.223 0.000
80.263 84.465 0.041 47.284 49.88 0.307 28.5 38.497 0.038
88.750 85.052 0.016 70.05 58.724 0.000 60.223 57.257 0.730
90.468 86.571 0.001 67.233 50.53 0.000 47.887 42.947 0.123
87.807 86.598 0.327 60.1 53.83 0.004 36.443 40.333 0.219
follow routes and number of errors as compared to poorly-designed symbols. These results indicated that tourists are more confused by easy to misunderstand symbols than poorly-designed symbols in central railway hubs, and this could cause them to get lost. The three easy to misunderstand symbols indicated Kaohsiung Rapid Transit (
), a platform at a high speed rail station (
and direction of the rail train ( symbol (
),
). In genuine circumstances, the
) is arranged with “platform” in Chinese words inside
central rail hubs, and most passengers could arrive to platform successfully. However, the foreign tourists and few older passengers don’t understand the meaning in Chinese words, so this symbol is misunderstood easily. Personnel in related railway departments should consider modifying the symbol to be the symbol with concrete platform. When meeting Kaohsiung Rapid Transit (
), most participants imagined Kaohsiung rail train
station, rather than Kaohsiung Rapid Transit. The results showed most participants were more familiar with Kaohsiung Rail Train Station than Kaohsiung Rapid Transit. They also misunderstood the meaning of the rail train station’s logo (
) to other meanings
(e.g. office etc.). The phenomenon implied corporate identities of both transportation systems needed to be strengthened. The idea of a corporate identity integrates the look and feel of designs and communications, along with the corporation’s behavior, including logos, corporate colors, uniforms etc. It is an easily recognizable sign because of an increased sense of familiarity due to a welldesigned corporation’s logo (Gregg, 2003). The result showed that the corporate logos of both transportation systems were not clear enough to make users familiar. Signs in related transportation systems need to be strengthened by the corporate identity, to fit the
architecture, spaces and interiors of the institution, henceforth tourists and passengers will be more familiar with the symbols. Past research has indicated that many symbols are poorly understood and may pose particular difficulty to the elderly (Collins and Lerner, 1982; Dewar et al., 1994; Easterby and Hakiel, 1981; Hancock et al., 1999; Jones, 1992; Lesch, 2003; Morrell et al., 1990; Shinar et al., 2003; Zwaga and Boersema, 1983; Al-Gadhi et al., 1994). This study provided additional evidence that older adults experience greater difficulty in understanding particular symbols as compared to younger adults and that older adults as a group merit particular attention in symbol design. Older participants performed much more poorly than younger participants, obtaining a comprehension score of only 1.0248 in symbols comprehension as compared to the comprehension score of 1.4607 obtained by younger participants. The older participants possibly preferred to restrict themselves to familiar environments and thus were unfamiliar with these directional symbols from central railway hubs. Some of the symbols are also likely to be used for the first time today, so the elderly participants had never learned them. This supposition is supported by the findings of Lajunen et al. (1996) which indicated that traffic signs introduced after elderly drivers obtained their licenses were less familiar to them than previously existing signs. Previous studies have also found that older adults have reduced visual processing speed and visual attention (Batchelder et al., 2004; Caird et al., 2005; Ho et al., 2001). The findings of experiment 2 were consistent with those of previous studies which indicated that older passengers required more time to arrive at arranged destinations. Older participants often spent more time to search directional signs in the photos than younger participants, and were dull for a few seconds by getting lost. Furthermore, older participants’ physiological movements were slowed down by age. The results obviously showed poor visual
Table 5 Canonical correlation analysis results. Independent variable
Familiarity Concreteness Simplicity Meaningfulness Accuracy of semantic depiction Proportion of variance Redundancy coefficient Canonical Correlation rib Eigenvalue rib Significant p a b
Canonical loadings
c1
c2
c3
c4
0.992a 0.952a 0.950a 0.942a 0.930a 90.9 0.864
0.087 0.185 0.169 0.279 0.561a 5.6 0.024
0.007 0.172 0.043 0.012 0.029 0.7 0.000
0.019 0.051 0.106 0.176 0.054 1.0 0.000
0.975 0.951 0.000
0.650 0.423 0.040
0.183 0.033 0.972
0.070 0.004 0.921
Absolute value of canonical loadings >0.5. ri is the index of canonical correlation. i ¼ 1,2,3,4.
Dependent variable
Canonical loadings
h1
h2
h3
h4
Avg. Avg. Avg. Avg.
0.764a 0.987a 0.599a 0.726a
0.223 0.012 0.800a 0.592a
0.601 0.150 0.004 0.141
0.074 0.060 0.016 0.319
61.1 0.581
26.0 0.110
10.1 0.003
2.8 0.000
response time score of comprehension time required to follow routes number of errors
Proportion of variance % Redundancy coefficient
1024
Y.-C. Liu, C.-H. Ho / Applied Ergonomics 43 (2012) 1016e1025
searching ability, longer information processing time, and motoric slowing have led to slower destination arrival time for older adults. Results also indicated that well-designed symbols improved information processing ability and time required to follow routes, particularly for older participants. Older participants rated lower familiarity and lower accuracy of semantic depiction as being greater challenges than younger participants in the categories of “poorly-designed” and “easy to misunderstand” symbols. This result indicates that practitioners in their application of ergonomic principles and practices pay attention to familiarity and accuracy of semantic depiction of elderly-friendly symbols when designing. Canonical correlation analysis demonstrated that higher ratings of symbol features implied better symbol comprehension and performance in following routes. Lower ratings of accuracy of semantic depiction resulted in increased time required to follow routes and number of errors. Canonical correlation analysis explained almost 70% of variance among the features of familiarity, concreteness, simplicity, meaningfulness, and accuracy of semantic depiction. All five features were highly positively correlated (all correlations >0.9) with canonical variates. Past research has indicated that familiarity, concreteness, simplicity, meaningfulness, and accuracy of semantic depiction affect comprehension and guessability of symbols (Lin, 1992; Wolff and Wogalter, 1993; Dewar, 1999; McDougall et al., 1999; Huang et al., 2002; Rosson, 2002; Ben-Bassat and Shinar, 2006; Ng and Chan, 2007; Passini et al., 2008). This study also showed that the five symbol features significantly influence comprehension of symbols and performance in following routes. Symbol comprehension score, in particular, was significantly influenced by these features (correlation ¼ 0.987), and this provided additional evidence to support the above-described outcome, which is of considerable concern to research on symbols and icons. Familiarity had the highest correlation with symbol comprehension performance (response time and comprehension scores), which was similar to the result of past studies (Rosson, 2002; BenBassat and Shinar, 2006). This result implies that designers should try to design symbols that users are familiar with, which would assist users in comprehending these symbols promptly. Familiar aspects of daily life are combined into new symbols. For instance, the symbol of a public telephone (
) and a refreshment (
)
show usage of common appliances as icon representations. Furthermore, standardized symbol provided an advantage in terms of increasing users’ familiarity as well (i.e the symbol for toilet, ). Familiar aspects and standard of symbolic representations both help to build and maintain a user’s mental model of symbolic comprehension. To reflect genuine circumstances, this study simulated the central railway hub in Kaohsiung City, Taiwan, and found that accuracy of semantic depiction was the best predictor of behavior in following routes for both older and younger adults. This confirmed the findings of McDougall et al. (2001): accuracy of semantic depiction is more significant than concreteness in evaluation of icon effectiveness. Ng and Chan (2007) also indicated that accuracy of semantic depiction is a better guessability of traffic symbols compared to other symbol features. To improve poor comprehension of certain symbols and the reduced efficiency of these symbols in route guidance, symbol comprehension training or promotion is effective in enhancing comprehension of directional symbols. TV advertisements or programs, newspaper inserts and introduction in network are useful training and promotion methods. Promotion in clubs of senior citizens may bring an outstanding benefit for older adults in Taiwan because older adults are used to gathering in such clubs.
The design of directional and informational signs involves the concept of repetitive reinforcement. Signs designed with both text descriptions and icons/pictures would improve user recognition ability (Wiseman et al., 1985) and significantly enhance user comprehension (Wolff and Wogalter, 1998; Scialfa et al., 2008). In addition, the findings of this study provide beneficial recommendations to design new interactive signs. An interactive sign such as a warning sign is a sign that reacts to the behavior of users and presents a corresponding meaning. The designers should use a familiar component in real life as a principal element of an interactive sign, and consider the semantic distance between symbol and meaning. This would lead to users comprehending the meaning of the interactive sign clearly and responding rapidly when they encounter it. In conclusion, this study demonstrated that age and symbol features are factors of influence in effective communication through symbols in public transportation systems. We suggest that training in symbol comprehension or material articulating symbol meaning be provided to residents or tourists, especially older adults. This would improve the usefulness of these symbols and satisfaction rate of passengers regarding the use of such icons, as well as avoid wasted time due to misunderstanding of confusion of symbol meaning.
References Al-Gadhi, S.A., Naqvi, S.A., Abdul-Jabbar, A.S., 1994. Driver factors affecting traffic sign detection and recall. Transp. Res. Rec. 1464, 36e41. Al-Madani, H., Al-Janahi, A.R., 2002. Assessment of drivers’ comprehension of traffic signs based on their traffic, personal and social characteristics. Transp. Res. Part F 5, 63e76. Batchelder, S., Rizzo, M., Vanderleest, R., Vecera, S., 2004. Traffic scene related change blindness in older drivers. In: Proceedings of the Second International driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design. Ben-Bassat, T., Shinar, D., 2006. Ergonomic guidelines for traffic symbol design to increase symbol comprehension. Hum. Factors 48, 182e195. Bruyas, M.P., Le Breton, B., Pauzié, A., 1998. Ergonomic guidelines for the design of pictorial information. Int. J. Ind. Ergon. 21, 407e413. Building Research Establishment for Office of the Deputy Prime Minister, 2006. Final Report for Signage and Wayfinding for People with Learning Difficulties. Retrieved February 10, 2012, from: http://www.communities.gov.uk/ documents/planningandbuilding/pdf/144248.pdf. Caird, J.K., Edwards, C.J., Creaser, J.I., Horrey, W.J., 2005. Older driver failures of attention at intersections: using change blindness methods to assess turn decision accuracy. Hum. Factors 47, 235e249. Collins, B.L., Lerner, N.D., 1982. Assessment of fire-safety symbols. Hum. Factors 24, 75e84. Chan, A.H.S., Ng, A.W.Y., 2010. Investigation of guessability of industrial safety signs: effects of prospective-user factors and cognitive sign features. Int. J. Ind. Ergon. 40, 689e697. Davies, S., Haines, H., Norris, B., Wilson, J.R., 1998. Safety pictograms: are they getting the message across? Appl. Ergon. 29 (1), 15e23. Dewar, R.E., Kline, D.W., Swanson, H.A., 1994. Age differences in comprehension of traffic symbol symbols. Transp. Res. Rec. 1456, 1e10. Dewar, R., 1999. Design and evaluation of public information symbols. In: Zwaga, H.J.G., Boersema, T., Hoonhout, H.C.M. (Eds.), Visual Information for Everyday Use: Design and Research Perspectives. Taylor & Francis, London, pp. 285e303. Easterby, R.S., Hakiel, S.R., 1981. Field testing of consumer safety symbols: the comprehension of pictorially presented messages. Appl. Ergon. 12, 143e152. Foster, J.J., Afzainia, M.R., 2005. International assessment of judged symbol comprehensibility. Int. J. Psychol. 40 (3), 169e175. Gregg, B., 2003. UTS Sign Standards. Retrieved February 10, 2012, from: http:// www.fmu.uts.edu.au/for/consultants/docs/UTSSignStandards.pdf. Hancock, H.E., Rogers, W.A., Fisk, A.D., 1999. Understanding age-related differences in the perception and comprehension of symbolic warning information. In: Proceedings of the Human Factors and Ergonomics Society 43rd Annual Meeting. Human Factors Society, Santa Monica, CA, pp. 617e621. Ho, G., Scialfa, C.T., Caird, J.K., Graw, T., 2001. Visual search for traffic symbols: the effects of clutter, luminance, and aging. Hum. Factors 43 (2), 194e207. Huang, S.M., Shieh, K.K., Chi, C.F., 2002. Factors affecting the design of computer icons. Int. J. Ind. Ergon. 29, 211e218. Ishihara, S., 1993. Ishihara’s Test for Colour-Blindness. Kanehara, Tokyo. Jones, R.W., 1992. Older drivers say... Stop symbols of confusion. Traffic Saf. 11 (6), 6e9.
Y.-C. Liu, C.-H. Ho / Applied Ergonomics 43 (2012) 1016e1025 Lajunen, T., Hakkarainen, P., Summala, H., 1996. Ergonomics of road symbols: explicit and embedded speed limits. Ergonomics 39, 1069e1083. Lesch, M.F., 2003. Comprehension and memory for warning symbols: age-related differences and impact of training. J. Saf. Res. 34, 495e505. Lesch, M.F., 2008. A comparison of two training methods for improving warning symbol comprehension. Appl. Ergon. 39, 135e143. Lin, R., 1992. An application of the semantic differential to icon design. In: Proceeding of the Human Factors Society 36th Annual Meeting, Santa Monica, CA, pp. 336e340. McDougall, S.J.P., Curry, M.B., de Bruijn, O., 1999. Measuring symbol and icon characteristics: norms for concreteness, simplicity, meaningfulness, familiarity, and semantic distance for 239 symbols. Behav. Res. Methods Instrum. Comput. 31 (3), 487e519. McDougall, S.J.P., Curry, M., de Bruijn, O., 2001. The effects of visual information on users’ mental models: an evaluation of pathfinder analysis as a measure of icon usability. Int. J. Cogn. Ergon. 5 (1), 59e84. Morrell, R.W., Park, D.C., Poon, L.W., 1990. Effects of labeling techniques on memory and comprehension of prescription information in young and old adults. J. Gerontol. 45, 166e172. Ng, A.W.Y., Chan, A.H.S., 2007. The guessability of traffic symbols: effects of prospective-user factors and symbol design features. Accid. Anal. Prev. 39, 1245e1257. Ng, A.W.Y., Chan, A.H.S., 2008. The effects of driver factors and sign design features on the comprehensibility of traffic signs. J. Saf. Res. 39, 321e328. Ng, A.W.Y., Chan, A.H.S., 2009. What makes an icon effective? In: Ao, S.I., et al. (Eds.), IAENG Transactions on Engineering Technologies Special Edition of the International Multi Conference of Engineers and Computer Scientists 2008, vol. I. American Institute of Physics Press, pp. 104e114. Paninti, J.F., 1989. Redesign and evaluation of selected work zone symbol symbols. Transp. Res. Rec. 1213, 47e55. Passini, S., Strazzari, F., Borghi, A., 2008. Icon-function relationship in toolbar icons. Displays 29 (5), 521e525. Picha, D.L., Hawkins Jr., H.G., Womack, K.N., 1995. Motorist Understanding of Alternative Designs for Traffic Symbols. Texas Transportation Institute, Texas
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A&M University, College Station, TX. Final Report to the Federal Highway Administration. Picha, D.L., Hawkins Jr., H.G., Womack, K.N., Rhodes, L.R., 1997. Driver understanding of alternative traffic symbols. Transp. Res. Rec. 1605, 8e16. Preece, J., Rogers, Y., Sharp, H., Beryon, D., Holland, S., Carey, T., 1994. Humancomputer Interaction. Addison-Wesley Press, England, pp. 99e122. Rosson, M.B., 2002. Usability Engineering: Scenario-Based Development of HumanComputer Interaction. Academic Press, San Francisco, pp. 119e125. Rousek, J.B., Hallbeck, M.S., 2011. Improving and analyzing signage within a healthcare setting. Appl. Ergon. 42, 771e784. Scialfa, C., Spadafora, P., Klein, M., Lesnik, A., Dial, L., Heinrich, A., 2008. Iconic signcomprehension in older adults: the role of cognitive impairment and text enhancement. Can. J. Aging 27 (3), 253e265. Shinar, D., Dewar, R.E., Summala, H., Zakowska, L., 2003. Traffic symbol comprehension: a cross-cultural study. Ergonomics 46 (15), 1549e1565. Statistical Yearbook of Interior, 2010. The Ministry of Interior, Taiwan. Retrieved September 20, 2010, from: http://sowf.moi.gov.tw/stat/year/y02-01.xls. United Nations Population Division, 2009. World Population Aging 2009. United Nations, New York. U.S. Census Bureau, International Data Base, 2010. Retrieved September 21, 2010, from: http://www.census.gov/ipc/www/idb/excel_output.php?Taiwan. Wiseman, S., Macleod, C.M., Lootsteen, P.J., 1985. Picture recognition improves with Subsequent verbal information. J. Exp. Psychol. Learn. Mem. Cogn. 11, 588e595. Wogalter, M.S., Sojourner, R.J., Brelsford, J.W., 1997. Comprehension and retention of safety pictorials. Ergonomics 40, 531e542. Wolff, J.S., Wogalter, M.S., 1993. Test and development of pharmaceutical pictorials. In: Proceedings of Interface ’93, Santa Monica, CA. Human Factors and Ergonomics Society, pp. 187e192. Wolff, J.S., Wogalter, M.S., 1998. Comprehension of pictorial symbol: effects of context and test method. Hum. Factors 40, 173e186. Young, S.L., Wogalter, M.S., 1990. Comprehension and memory of instruction manual warnings: conspicuous print and pictorial icons. Hum. Factors 32, 637e649. Zwaga, H.J., Boersema, T., 1983. Evaluation of a set of graphic symbols. Appl. Ergon. 14, 43e54.
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The effects of age on symbol comprehension in central rail hubs in Taiwan Yung-Ching Liu*, Chin-Heng Ho Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Douliu, Yunlin 640, Taiwan
a r t i c l e i n f o
a b s t r a c t
Article history: Received 15 April 2011 Accepted 15 February 2012
The purpose of this study was to investigate the effects of age and symbol design features on passengers’ comprehension of symbols and the performance of these symbols with regard to route guidance. In the first experiment, 30 young participants and 30 elderly participants interpreted the meanings and rated the features of 39 symbols. Researchers collected data on each subject’s comprehension time, comprehension score, and feature ratings for each symbol. In the second experiment, this study used a series of photos to simulate scenarios in which passengers follow symbols to arrive at their destinations. The length of time each participant required to follow his/ her route and his/her errors were recorded. Older adults experienced greater difficulty in understanding particular symbols as compared to younger adults. Familiarity was the feature most highly correlated with comprehension of symbols and accuracy of semantic depiction was the best predictor of behavior in following routes. Ó 2012 Elsevier Ltd and The Ergonomics Society. All rights reserved.
Keywords: Age Comprehension of symbols Central rail hub
1. Introduction The Taiwan High Speed Rail (THSR) was established in 2007 to solve traffic problems in western Taiwan, and the Kaohsiung rapid transit system was completed in 2008. Following the completion of these two transportation systems, central rail hubs, which include a railway station, a THSR station, and a rapid transit station, have appeared in Taiwan. Many different people pass through these stations for different purposes. For example, local residents in the Kaohsiung rapid transit station may actually intend to take the train or high speed rail, and tourists in the THSR or railway station may intend to travel by rapid transit. Various systems of directional and informational symbols are used in these hubs to guide passengers, provide them with information, and help them identify directions according to their travel intentions. However, not only are these three systems of symbols in the central rail hubs different but many symbols have been modified and new symbols have been added. Because the symbols in these transportation hubs are not standardized and some residents or foreign tourists may not have seen these symbols before, comprehension of symbols in such hubs is important for prospective users of transportation systems. It was hypothesized that detailed investigation of the comprehension of directional and informational symbols in central railway hubs was an important need. When the tourists can’t understand the
* Corresponding author. Tel.: þ886 5 5342601; fax: þ886 5 5312073. E-mail addresses: [email protected], [email protected] (Y.-C. Liu).
symbols of the central rail hubs, they experience confusion and are at a loss of what to do next. They are likely to feel even more lost in central rail hubs, and can easily become more flustered. These conditions could disrupt order and be a serious issue from a safety perspective (Building Research Establishment for Office of the Deputy Prime Minister, 2006). The advantages of using symbols are: (a) symbols can quickly communicate instructions; (b) use of symbols avoids problems related to inadequate reading skills or linguistic unfamiliarity; and (c) passengers may remember symbols better than they remember text (Wogalter et al., 1997). Because symbols provide a languagefree method of communicating, they can potentially be understood by diverse groups which vary in life experience and reading ability. However, studies have proved that many symbols currently in use are difficult to understand (Collins and Lerner, 1982; Davies et al., 1998; Wolff and Wogalter, 1993; Lesch, 2003). Picha et al. (1995, 1997) conducted multiple evaluations of comprehension of different traffic symbols by more than 3000 drivers in Texas. They found that low comprehension levels existed for several symbols. Paninti (1989) compared several alternative symbols used to signify work zones, and found that symbols with good physical resemblance to what they were meant to signify were all well understood, even when the details of these symbols were different. In contrast, when the intended message was difficult to convey symbolically, all proposed alternative symbols were not clearly understood. Measurement of symbol design is based on recognition rate. McDougall et al. (1999) measured characteristics of symbols or
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Y.-C. Liu, C.-H. Ho / Applied Ergonomics 43 (2012) 1016e1025
icons according to features like familiarity, concreteness, simplicity, meaningfulness, and accuracy of semantic depiction. Ng and Chan (2007) also used these same features to investigate the guessability of traffic symbols. These features have become central concerns in research on symbols and icons (Ng and Chan, 2008, 2009; Chan and Ng, 2010). Familiarity indicates the frequency with which icons have been encountered. Rosson (2002) also found that familiarity improves comprehension, a conclusion similar to that of Ben-Bassat and Shinar (2006). Concreteness indicates the degree to which something is material and genuine. Icons or symbols are concrete if they depict real objects, materials, or people; otherwise, they are abstract. Symbols with concrete design are more easily understood than those with ambiguous designs (Wolff and Wogalter, 1993; Foster and Afzainia, 2005; Passini et al., 2008; Rousek and Hallbeck, 2011). Symbols are regarded as complex if they are intricate or depict a lot of detail, and simple if they contain only few elements or details. Dewar (1999) noted that complicated symbols are more difficult to comprehend compared to simpler symbols. Meaningfulness refers to the degree of significance viewers attribute to icons and is seen as an important characteristic of symbol design (Huang et al., 2002; Lin, 1992). Accuracy of semantic depiction indicates how closely, accurately, and comprehensively the design of the symbol represents what the symbol is meant to signify. Lesch (2008) suggested that positive symbol characteristics are easy to understand, and that using positive symbol characteristics is more effective in helping viewers to understand symbols as compared to symbol comprehension training. Shinar et al. (2003) found that ‘infrequent symbols are more likely to be miscomprehended and less likely to be correctly learned’ by drivers. It was hypothesized that users identify symbols more easily if the symbols are familiar to them. Another hypothesis of this study was that because concrete symbols provide a direct visual aid to help viewers comprehend the intended meaning of such depiction, users comprehend concrete symbols better than they do abstract symbols. It was expected that simple symbols to be easier to identify than complex symbols because complex symbols have the potential to confuse or complicate understanding (Bruyas et al., 1998). The meaningfulness of a symbol refers to the ability of a symbol to elicit attribution of meaning from users (Preece et al., 1994), so researchers expected higher comprehension scores for meaningful symbols. Higher ratings for accuracy of semantic depiction indicated that symbols given such ratings were clearly associated with the concepts they were meant to signify, and thus should lead to higher comprehension scores. Young and Wogalter (1990) indicated that users will better identify a symbol that precisely communicates the semantic meaning. Statistics show that 20% of the population in most developed countries is older than 60, and that one third of the earth’s population will be over 60 years old by 2050 (United Nations Population Division, 2009). In 2009, 2.45 million people (10.7% of the population) in Taiwan were over 65 years old (Statistical Yearbook of Interior, Taiwan, 2010), and the population of elderly adults will only continue to increase. According to population statistics forecast in the international database of the U.S. Census Bureau, 12.3% of the population in Taiwan will be older than 65 in 2015. The 65 þ age group represents the fastest growing age group in Taiwan (U.S. Census Bureau, 2010). Regarding the effect of aging, past studies have indicated that the use of symbols may pose special problems for the elderly (Collins and Lerner, 1982; Dewar et al., 1994; Easterby and Hakiel, 1981; Hancock et al., 1999; Jones, 1992; Lesch, 2003; Morrell et al., 1990; Shinar et al., 2003; Zwaga and Boersema, 1983; AlGadhi et al., 1994; Scialfa et al., 2008). Hancock et al. (1999) indicated that aging is generally associated with a decline in various
1017
perceptual and cognitive abilitiesdfor example, vision and working memory. However, Al-Madani and Al-Janahi (2002) found that the differences between the different age groups showed that drivers in the younger age group (16e24 years) comprehend significantly less well than those in the older groups (35e44 and over 44 years). Ng and Chan (2008) also indicated that the poor performance of older subjects was not evident. In related experimentation, Dewar et al. (1994) showed 85 color slides of standard US symbol symbols to 480 volunteer licensed drivers from the USA and Canada. Results showed that for 39% of the symbols examined, the understanding of older drivers was poorer than that of younger drivers. Regarding the remaining symbols, no difference in comprehension with regard to age was observed. Jones (1992) reported that a survey of older drivers in Illinois also showed that older drivers failed to understand some common traffic control signs. Shinar et al. (2003) evaluated levels of comprehension of highway traffic symbols used in different countries, and found that “older drivers tend to do less well at symbol comprehension than other drivers, including novice drivers and repeated violators”. Lesch (2003) examined the effectiveness of three different training conditions to improve comprehension and remembrance of warning symbols for younger (18e35 years old) and older (50e67 years old) participants. Results showed that although training improved participants’ accuracy and speed of response on a comprehension test, the performance of older participants was significantly poorer than that of younger participants, both before (37% vs. 52% of correct answers, respectively, in the older and younger participant groups) and after training (68% vs. 88% of correct answers on the immediate post-test). Older participants also found it more difficult to reject incorrect meanings (55% vs. 68% of correct answers). According to the abovementioned research, the effects of aging significantly influence comprehension of symbols, and the design of symbols in central rail hubs should take elderly adults into account. The aims of this study were as follows, and two experiments were used to assess the objectives. (1) To evaluate the comprehension of symbols in Taiwanese central rail hubs by two user groups of different ages; (2) To explore the correlation between symbol design features and symbol comprehension; (3) To examine the differences among participants’ symbol comprehension performance with regard to various categories of symbols
2. Methods 2.1. Participants Thirty older participants (20 males and 10 females) and thirty younger participants (18 males and 12 females) were recruited to participate in this study. The older participants ranged from 65 to 74 years old (average ¼ 67.6 years old), and the younger participants ranged from 23 to 30 years old (mean ¼ 26.5 years old). Because the users of these central rail hubs in real life include local residents and outside tourists and their demographic details reflect diversity, one-third of the participants recruited for this study were local residents and the others were from other places. All participants had never been to the central railway hub in Kaohsiung City, Taiwan and had to pass a health screening examination which tested vision (near sightedness, farsightedness, 18/20 vision or better), and color blindness (ability to pass the Ishihara card color blindness test) (Ishihara, 1993). Each participant was paid 10 US dollars for participating in this study.
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2.2. Equipment This study used a Nikon COOLPIX S3 (at 800 600 resolution) to take the photographs. The objects in the images were modified with Adobe Photoshop CS. A microcomputer (PC) was used to control the stimulus presentation and the images were shown on a 17 in. color monitor at a viewing distance of approximately 60 cm. Flash software was used to develop the stimuli in both experiments and record response time, and Microsoft PowerPoint was used to display the symbols while participants evaluated the individual features of each symbol. 2.3. Experiment 1: comprehension of symbols and evaluation of symbol features Thirty-nine directional symbols collected from the central rail hub in Kaohsiung City, Taiwan were used in this study. The symbols in central rail hubs collected in the study help passengers identify directions and provide them with information, and were divided into the symbols from the Taiwan High Speed Rail (THSR), Kaohsiung rapid transit, and railway stations. Five traffic signs used in Taiwan were utilized for practice trials prior to testing participants with the 39 symbols. Flash software was used to develop the stimuli for symbol comprehension and record the length of response time each participant required when identifying symbols. All the symbols were shown in 7 cm 7 cm squares with no boundaries and depicted at the centre of the computer screen using Flash software. Participants viewed the symbols at a distance of 60 cm (subtending 6.67 ) from the screen. All participants pressed the “Space” key to begin the trial, after which the symbols began to appear on the screen. Participants were required to view and conceptually grasp the symbol and then press the “Space” key again to stop the display of stimuli. Verbal icons in Chinese to describe the meaning of symbols were used, and the responses of participants were recorded by a recorder. The responses of participants to each symbol were classified into one of the following three categories measuring accuracy: Correct and complete (coded as þ2), partially correct (e.g. “car park No. 1”, instead of “car park”dcoded as þ1), or incorrect (e.g. “tickets”, instead of “convenience store”dcoded as 0). Two researchers used this guide in scoring the comprehension test, and the average comprehension score of each participant for each symbol was calculated. Additionally, participants filled out an evaluation sheet for symbol features. This evaluation sheet allowed participants to rate symbol features and was selected from the research of Ng and Chan (2007). Participants were instructed about the rating instructions and the meanings of the terms familiarity, concreteness, simplicity, meaningfulness, and accuracy of semantic depiction, and three additional symbols were provided to familiarize them with the rating symbol feature at the beginning of the symbol feature evaluation. Participants were asked to subjectively rate the familiarity (0 ¼ very unfamiliar, 100 ¼ very familiar), concreteness (0 ¼ clearly abstract, 100 ¼ clearly concrete), simplicity (0 ¼ very complex, 100 ¼ very simple), meaningfulness (0 ¼ completely meaningless, 100 ¼ completely meaningful), and accuracy of semantic depiction (0 ¼ very weakly related, 100 ¼ very strongly related) for each of the 39 symbols. These symbols were presented on the computer screen using Microsoft PowerPoint. 2.4. Experiment 2: scenarios of following directional signs This study also used a series of photos, taken from inside the central railway hub in Kaohsiung City, to build various scenarios. Flash software was used to simulate scenarios in which passengers followed guide signs to arrive at arranged destinations. Participants
were the same as those in experiment 1 and were asked to imagine that they were inside the central rail hub in Kaohsiung at the beginning. Participants were provided with a verbal description of the experiment procedure and invited to ask questions about the experiment before signing a consent form. Three practice trials that followed an introduction of the experiment were provided to the participants to practice and become familiar with Experiment 2. In each trail Participants were told to do something at the station (i.e. buy a ticket) or use specific public facilities (i.e. car park, or toilet), and then participants pressed the “Space” key to begin the trial and searched for and followed directional signs in the photos. Participants controlled the direction of their routes using the up, down, left, and right keys of the keyboard. If participants selected the correct route from directional signs in a photo, then a photo with related directional signs appeared on the screen; if the wrong route was selected from the first photo, the photo that appeared would be one without any symbols, thereby indicating to the participant that he/she was lost, and he/she would then need to return to the first photo and make another selection for the route to take. When participants had responded with five continuously correct directional choices, the trial ended. Thirty-nine directional route scenarios with guidance symbols were developed using Flash software. The length of time each participant spent to follow routes and the number of errors (wrong direction) was recorded in each trial. The number of errors for a particular trial meant the number of times participants selected the wrong route. 2.5. Procedure Within the first 15 min of the trial, participants completed a consent form and undertook a health screening examination consisting of a formal vision test and color blindness test. Before commencement of the formal experiment, five traffic signs used in Taiwan were used for practice trials. Each participant completed 39 symbol comprehension trials in random order. Next, participants were given brief instructions for evaluating and rating symbol features. Participants subjectively rated each symbol according to familiarity, concreteness, simplicity, meaningfulness, and accuracy of semantic depiction. One week from Experiment 1, each participant was asked to participate in Experiment 2, which involved various scenarios of following directional signs or symbols. After being briefed about this experiment and participating in five practice trials, participants completed 39 trial scenarios in random order. 2.6. Data collection and analysis Researchers collected data on each subject’s comprehension time, comprehension score, and feature ratings for each symbol in experiment 1; and the length of time each participant spent to follow his/her route and the number of errors were recorded in experiment 2. Data collected for this present study were analyzed by means of four types of analyses. First, reliability analysis was used to examine the internal consistency in measuring comprehension rates of the symbols’ features. The second kind of analysis investigated some of the differences in symbol comprehension among the 39 symbols. Cluster analysis was used to classify the categories of symbol comprehension in this study, and Ward’s method was used to conduct clustering. In order to examine the effects of age, the third kind of analysis involved a series of T-tests to assess variations in symbol comprehension and other elements of performance as a function of age. The purpose of the fourth type of analysis was to identify the correlation, among both elderly and younger participants, between symbol comprehension and related
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Fig. 1. Process of testing symbol comprehension: (a) please press SPACE bar to start the experiment; (b) Viewing and comprehension of symbol; and (c) Verbal answer indicating perceived meaning of symbol.
elements of performance and symbol features. Canonical correlation analysis was utilized to examine the relationship between symbol features and symbol comprehension performance. 3. Results
required to follow routes and number of errors). The 39 symbols included 26 well-designed symbols,10 poorly-designed symbols, and 3 easy to misunderstand symbols. Table 1 illustrates the performance of participants in the three symbol categories. Table 2 presents respective comprehension rates for each of the 39 symbols, and Fig. 3 shows the symbol features in each symbol category.
3.1. Categories of symbols In experiment 1, the Standard Cronbach alpha of 0.929 reflected a high internal consistency in the feature ratings for each symbol. Experimental results showed significant differences among symbol comprehension, with some symbols being fully understood by most respondents, and others being either misunderstand or not fully understood by 80% or more of the respondents in a fairly uniform manner in both groups. However, the error patterns were not the same for all symbols. In a very few cases, poorer comprehension performance was due to partially correct answers; however, the most common errors were due to unknown or misunderstood meanings. For example, the symbol “Kaohsiung Rapid Transit” (
) might be misunderstood as Kaohsiung Rail Train Station.
Cluster analysis methods were used to classify the categories of symbol comprehension in this study, and Ward’s method of cluster analysis was used to conduct clustering. The screen plot in Figs.1 and 2 was examined to determine the number of categories in this study. The difference coefficient tended to level off after the first three clusters; thus, this study set three cluster criterions. The three categories of symbols examined were well-designed symbols (optimal performance), poorly-designed symbols (generating longest response time and poorest comprehension scores), and symbols easy to misunderstand (average response time but low comprehension score, as well as significantly poorer scores on time
3.2. Differences between age groups with regard to symbol comprehension performance and evaluation of features According to the data scale, ManneWhitney U test which is a nonparametric statistical measure was used to evaluate age effects on comprehension scores, number of errors, and symbol features in three symbol categories. Highly significant differences were found in symbol comprehension and related elements of performance between the two groups of participants. The results of a classification of symbols into symbol comprehension categories based on participant performance were different for each age group. Results indicated that the average reaction time for symbol comprehension, average comprehension score, average time required to follow routes, and average number of errors (all p-value ＜0.05) were significantly different between the two groups, and that the performance of older participants was poorer than that of younger participants in three symbol comprehension categories. Regardless of any symbol comprehension category, the responses of young participants were faster and more accurate (Well-designed symbols: avg. response time ¼ 4.530 s, avg. comprehension score ¼ 1.817; poorly-designed symbols: avg. response time ¼ 7.343 s, avg. comprehension score ¼ 0.437; easy to misunderstand symbols: avg. response time ¼ 6.167 s, avg. comprehension score ¼ 0.48) regarding symbol comprehension, and younger participants were also able to search for and determine the correct directional symbols more rapidly and smoothly (Well-designed symbols: avg. time required to follow routes ¼ 8.063 , avg. number of errors ¼ 0.096; poorly-designed symbols: avg. time required to follow routes ¼ 10.181 s, avg. number of errors ¼ 0.278; easy to misunderstand symbols: avg. Table 1 Participant performance in the three symbol categories. Symbol categories Avg. Avg. Avg. time required Avg. number response comprehension to follow routes (s) of errors time (s) score
Fig. 2. Screen plot of cluster analysis. Screen plot was examined to determine the number of categories. The difference coefficient tended to level off after the first three clusters.
Well-designed 6.222 Poorly-designed 10.671 Easy to 7.933 misunderstand
1.708 0.306 0.323
9.688 12.487 24.493
0.257 1.171 3.640
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Table 2 Respective comprehension rates for each of the 39 symbols. Well-designed symbols
Familiarity
Concreteness
Simplicity
Meaningfulness
Accuracy of semantic depiction
Public telephone
95.08
87.25
92.17
97.58
92.92
Elevator
89
83.15
87.25
92.235
93.185
Toilet
98.92
97.085
96.585
97.75
98.08
Refreshments
88.58
72.75
80.75
86.085
74.335
Service counter
47.415
45.085
82.085
75.75
71.5
Bus stop
68.815
84.415
83.5
88.67
89.33
Car parking
95.5
94.915
95.415
95.92
96.25
High speed rail station
77
82.665
83.665
85.25
85.335
Nursery room
83.4
89.635
83.585
90.58
90.23
Rapid transit station
53.5
65.5
69.165
66.335
66.17
Elevator
87.22
88.985
88.75
95.665
92.08
Rail Train Station
74.17
82.415
83.415
86.58
89.165
Health Care Room
63.335
61.085
70.335
66.415
63.085
Toilet
94.915
93.25
93.5
93.33
95.415
Police
51.085
60.585
76.33
70.75
69.335
Elevator
91.4
91.33
92.33
93.65
92.98
First car park
85.92
88.75
93.5
91.58
94
High speed rail station
84.335
81
83.65
85.665
83.5
Taxi pickup area
92.47
87.25
94.965
94.715
94.165
Toilet
97.92
94.835
94.585
96.25
96
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1021
Table 2 (continued ) Well-designed symbols
Familiarity
Concreteness
Simplicity
Meaningfulness
Accuracy of semantic depiction
Breastfeeding room
84.315
91.465
91.33
93.415
92.715
Service counter
52.83
56.915
81.915
84.17
78.25
Motorcycle parking
94.15
96.15
95.15
95.985
97
Escalator
83.355
87.37
83.56
90.155
87.915
Disabled facility
84.845
88.725
94.94
92.04
89.57
Motorcycle parking
91.835
88.9
87
94.98
94.75
Poorly-designed symbols
Familiarity
Concreteness
Simplicity
Meaningfulness
Accuracy of semantic depiction
Shuttle services
29.665
38.67
63
45.835
33.5
Rest room
36.33
42.5
57.75
60.165
57.5
Ticket vending machine
33.335
45.585
56.42
54.17
53.665
Kiss and ride
42.835
64.08
66.415
72.415
69.25
Tickets
23.585
38.585
58.835
46.165
47.92
Car rental
34.665
35.165
62.5
42.25
46.335
Ticket barrier
32.5
53.75
69.9
63.415
64.58
MPS liaison section
30.915
50.75
75.5
72.83
68.585
Travel services
46.75
61.17
64.915
61.17
61.08
Ticket vending machine
42.5
55.565
68.635
70.4
67.235
Easy to misunderstand symbols
Familiarity
Concreteness
Simplicity
Meaningfulness
Accuracy of semantic depiction
Kaohsiung rapid transit
30
22.165
65.165
43.835
37.42
Rail train station
47.665
50.08
58.22
61.5
53.83
Platform
35.5
28.25
52.835
30.915
23.915
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Familiarity 100 50
Semanic distance
Concreteness
0 Meaningfulness
Well-designed
Complexity
Bad-designed
Misunderstood
Fig. 3. Radar diagram of the three symbol categories.
time required to follow routes ¼ 18.397 s, avg. number of errors ¼ 1.39) compared to older participants (Well-designed symbols: avg. response time ¼ 7.914 s, avg. comprehension score ¼ 1.599, avg. time required to follow route ¼ 11.312 s, and avg. number of errors ¼ 0.418; poorly-designed symbols: avg. response time ¼ 13.998 s, avg. comprehension score ¼ 0.176, avg. time required to follow route ¼ 14.793 s, and avg. number of errors ¼ 2.063; easy to misunderstand symbols: avg. response time ¼ 9.7 s, avg. comprehension score ¼ 0.167, avg. time required to follow route ¼ 30.59 s, and avg. number of errors ¼ 5.89). However, the ratio of older to younger participants implied the performances of older participants decreased more greatly than those of younger ones in the “poorly-designed” symbol category, and comprehension scores of the older participants declined more greatly than those of the younger participants in the “easy to misunderstand” symbol category, as shown in Table 3. The results also show the differences in comprehension performance between the two groups significantly influenced time required to follow routes in the “easy to misunderstand” symbol category. Regarding symbol features, this study used ManneWhitney U test to examine the age effect, and the different symbol categories also showed differences in evaluation of symbol features between the two participant groups (Table 4). In the “well-designed” symbol category, the differences in each features evaluation between the two groups were not highly significant, and no significant difference appeared in familiarity and accuracy of semantic depiction. However, older participants reported less familiarity than young participants in the categories of “poorly-designed” (U ¼ 283.00, pvalue ¼ 0.013) and “easy to misunderstand” symbols (U ¼ 202.50, p-value ¼ 0.000). Older participants reported less accuracy of semantic depiction than young participants in the category of “poorly-designed” symbols as well (U ¼ 256.50, p-value ¼ 0.004). 3.3. Correlation between symbol features and symbol comprehension performance Canonical correlation analysis was utilized to examine the relationship between symbol features and symbol comprehension performance. Results of the analysis are shown in Table 5.
Table 5 illustrates the canonical loadings for the two significant canonical functions associated with both data sets. Variables with an absolute canonical loading value exceeding 0.5 indicate high correlation between the variable and the canonical variable (c). The canonical variate c1 explained 90.9% of the variance of all symbol features, and the canonical variate c2 explained 5.6% of the variance of all symbol features. Regarding symbol comprehension and route-following performance, the canonical variate h1 explained 61.1% of the variance of all performances, and the canonical variate h2 explained 26.0% of the variance of all performances. The two redundancy coefficients of participant performance were 0.581 and 0.110, respectively. This indicates that the canonical variates c1, c2 composed of symbol features explained almost 70% of the variance of all performances. Regarding evaluation of symbol features, the five features of familiarity, concreteness, simplicity, meaningfulness, and accuracy of semantic depiction in the first canonical function were highly positively correlated with canonical variate c1, and the canonical loadings were 0.992, 0.952, 0.950, 0.942, and 0.930, respectively. In the second canonical function, accuracy of semantic depiction was negatively correlated with canonical variate c2, and the canonical loading was 0.561. Regarding symbol comprehension performance, in the first canonical function, average response time, average comprehension score, average time required to follow routes, and average number of errors were either highly positively or negatively correlated with the canonical variate h1, and the canonical loadings were 0.764, 0.987, 0.599, and 0.726, respectively. In the second canonical function, only time required to follow routes and number of errors were highly positively correlated with canonical variate h2, the canonical loadings were 0.800 and 0.592, respectively. The results of canonical correlation analysis indicated a higher evaluation of symbol features, thus implying better participant performance, especially in terms of symbol comprehension scores. Familiarity had the highest correlation with symbol comprehension performance. This result implies designing symbols that users are familiar with, which would help users in comprehending these symbols promptly. Additionally, lower scores in terms of accuracy of semantic depiction resulted in increased time required to follow routes and a higher number of errors. This result implies that users would likely be lost in central rail hubs due to lower accuracy of semantic depiction for symbols in genuine circumstances. The designers should try to design symbols that users are familiar with and, in particular, have a higher accuracy of semantic depiction.
4. Discussion and conclusions The cluster analysis method was applied to classify symbols into symbol comprehension categories. Results indicated that 33.33% of directional symbols in central railway hubs were difficult to comprehend or easy to misunderstand for both older and younger adults. Average response time for poorly-designed symbols was longer than that for easy to misunderstand symbols. However, easy to misunderstand symbols resulted in increased time required to
Table 3 Symbol comprehension performance in three categories between two age groups. Well-designed symbols
Avg. Avg. Avg. Avg. a
response time (s) comprehension score time required to follow route(s) number of errors
Poorly-designed symbols
Easy to misunderstand symbols
Older
Younger
Ratioa
Older
Younger
Ratio
Older
Younger
Ratio
7.914 1.599 11.312 0.418
4.530 1.817 8.063 0.096
1.747 0.880 1.403 4.352
13.998 0.176 14.793 2.063
7.343 0.437 10.181 0.278
1.906 0.402 1.453 7.421
9.7 0.167 30.59 5.89
6.167 0.48 18.397 1.39
1.573 0.347 1.663 4.237
Ratio ¼ performance of older participant group/performance of younger participant group.
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Table 4 Evaluation of symbol features in three symbol categories between two age groups by ManneWhitney test.
Well designed
Poorly-designed
Easy to misunderstand
Older Younger p-value Older Younger p-value Older Younger p-value
Familiarity
Concreteness
Simplicity
Meaningfulness
Accuracy of semantic depiction
81.827 80.581 0.539 29.216 41.4 0.013 29.22 46.223 0.000
80.263 84.465 0.041 47.284 49.88 0.307 28.5 38.497 0.038
88.750 85.052 0.016 70.05 58.724 0.000 60.223 57.257 0.730
90.468 86.571 0.001 67.233 50.53 0.000 47.887 42.947 0.123
87.807 86.598 0.327 60.1 53.83 0.004 36.443 40.333 0.219
follow routes and number of errors as compared to poorly-designed symbols. These results indicated that tourists are more confused by easy to misunderstand symbols than poorly-designed symbols in central railway hubs, and this could cause them to get lost. The three easy to misunderstand symbols indicated Kaohsiung Rapid Transit (
), a platform at a high speed rail station (
and direction of the rail train ( symbol (
),
). In genuine circumstances, the
) is arranged with “platform” in Chinese words inside
central rail hubs, and most passengers could arrive to platform successfully. However, the foreign tourists and few older passengers don’t understand the meaning in Chinese words, so this symbol is misunderstood easily. Personnel in related railway departments should consider modifying the symbol to be the symbol with concrete platform. When meeting Kaohsiung Rapid Transit (
), most participants imagined Kaohsiung rail train
station, rather than Kaohsiung Rapid Transit. The results showed most participants were more familiar with Kaohsiung Rail Train Station than Kaohsiung Rapid Transit. They also misunderstood the meaning of the rail train station’s logo (
) to other meanings
(e.g. office etc.). The phenomenon implied corporate identities of both transportation systems needed to be strengthened. The idea of a corporate identity integrates the look and feel of designs and communications, along with the corporation’s behavior, including logos, corporate colors, uniforms etc. It is an easily recognizable sign because of an increased sense of familiarity due to a welldesigned corporation’s logo (Gregg, 2003). The result showed that the corporate logos of both transportation systems were not clear enough to make users familiar. Signs in related transportation systems need to be strengthened by the corporate identity, to fit the
architecture, spaces and interiors of the institution, henceforth tourists and passengers will be more familiar with the symbols. Past research has indicated that many symbols are poorly understood and may pose particular difficulty to the elderly (Collins and Lerner, 1982; Dewar et al., 1994; Easterby and Hakiel, 1981; Hancock et al., 1999; Jones, 1992; Lesch, 2003; Morrell et al., 1990; Shinar et al., 2003; Zwaga and Boersema, 1983; Al-Gadhi et al., 1994). This study provided additional evidence that older adults experience greater difficulty in understanding particular symbols as compared to younger adults and that older adults as a group merit particular attention in symbol design. Older participants performed much more poorly than younger participants, obtaining a comprehension score of only 1.0248 in symbols comprehension as compared to the comprehension score of 1.4607 obtained by younger participants. The older participants possibly preferred to restrict themselves to familiar environments and thus were unfamiliar with these directional symbols from central railway hubs. Some of the symbols are also likely to be used for the first time today, so the elderly participants had never learned them. This supposition is supported by the findings of Lajunen et al. (1996) which indicated that traffic signs introduced after elderly drivers obtained their licenses were less familiar to them than previously existing signs. Previous studies have also found that older adults have reduced visual processing speed and visual attention (Batchelder et al., 2004; Caird et al., 2005; Ho et al., 2001). The findings of experiment 2 were consistent with those of previous studies which indicated that older passengers required more time to arrive at arranged destinations. Older participants often spent more time to search directional signs in the photos than younger participants, and were dull for a few seconds by getting lost. Furthermore, older participants’ physiological movements were slowed down by age. The results obviously showed poor visual
Table 5 Canonical correlation analysis results. Independent variable
Familiarity Concreteness Simplicity Meaningfulness Accuracy of semantic depiction Proportion of variance Redundancy coefficient Canonical Correlation rib Eigenvalue rib Significant p a b
Canonical loadings
c1
c2
c3
c4
0.992a 0.952a 0.950a 0.942a 0.930a 90.9 0.864
0.087 0.185 0.169 0.279 0.561a 5.6 0.024
0.007 0.172 0.043 0.012 0.029 0.7 0.000
0.019 0.051 0.106 0.176 0.054 1.0 0.000
0.975 0.951 0.000
0.650 0.423 0.040
0.183 0.033 0.972
0.070 0.004 0.921
Absolute value of canonical loadings >0.5. ri is the index of canonical correlation. i ¼ 1,2,3,4.
Dependent variable
Canonical loadings
h1
h2
h3
h4
Avg. Avg. Avg. Avg.
0.764a 0.987a 0.599a 0.726a
0.223 0.012 0.800a 0.592a
0.601 0.150 0.004 0.141
0.074 0.060 0.016 0.319
61.1 0.581
26.0 0.110
10.1 0.003
2.8 0.000
response time score of comprehension time required to follow routes number of errors
Proportion of variance % Redundancy coefficient
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searching ability, longer information processing time, and motoric slowing have led to slower destination arrival time for older adults. Results also indicated that well-designed symbols improved information processing ability and time required to follow routes, particularly for older participants. Older participants rated lower familiarity and lower accuracy of semantic depiction as being greater challenges than younger participants in the categories of “poorly-designed” and “easy to misunderstand” symbols. This result indicates that practitioners in their application of ergonomic principles and practices pay attention to familiarity and accuracy of semantic depiction of elderly-friendly symbols when designing. Canonical correlation analysis demonstrated that higher ratings of symbol features implied better symbol comprehension and performance in following routes. Lower ratings of accuracy of semantic depiction resulted in increased time required to follow routes and number of errors. Canonical correlation analysis explained almost 70% of variance among the features of familiarity, concreteness, simplicity, meaningfulness, and accuracy of semantic depiction. All five features were highly positively correlated (all correlations >0.9) with canonical variates. Past research has indicated that familiarity, concreteness, simplicity, meaningfulness, and accuracy of semantic depiction affect comprehension and guessability of symbols (Lin, 1992; Wolff and Wogalter, 1993; Dewar, 1999; McDougall et al., 1999; Huang et al., 2002; Rosson, 2002; Ben-Bassat and Shinar, 2006; Ng and Chan, 2007; Passini et al., 2008). This study also showed that the five symbol features significantly influence comprehension of symbols and performance in following routes. Symbol comprehension score, in particular, was significantly influenced by these features (correlation ¼ 0.987), and this provided additional evidence to support the above-described outcome, which is of considerable concern to research on symbols and icons. Familiarity had the highest correlation with symbol comprehension performance (response time and comprehension scores), which was similar to the result of past studies (Rosson, 2002; BenBassat and Shinar, 2006). This result implies that designers should try to design symbols that users are familiar with, which would assist users in comprehending these symbols promptly. Familiar aspects of daily life are combined into new symbols. For instance, the symbol of a public telephone (
) and a refreshment (
)
show usage of common appliances as icon representations. Furthermore, standardized symbol provided an advantage in terms of increasing users’ familiarity as well (i.e the symbol for toilet, ). Familiar aspects and standard of symbolic representations both help to build and maintain a user’s mental model of symbolic comprehension. To reflect genuine circumstances, this study simulated the central railway hub in Kaohsiung City, Taiwan, and found that accuracy of semantic depiction was the best predictor of behavior in following routes for both older and younger adults. This confirmed the findings of McDougall et al. (2001): accuracy of semantic depiction is more significant than concreteness in evaluation of icon effectiveness. Ng and Chan (2007) also indicated that accuracy of semantic depiction is a better guessability of traffic symbols compared to other symbol features. To improve poor comprehension of certain symbols and the reduced efficiency of these symbols in route guidance, symbol comprehension training or promotion is effective in enhancing comprehension of directional symbols. TV advertisements or programs, newspaper inserts and introduction in network are useful training and promotion methods. Promotion in clubs of senior citizens may bring an outstanding benefit for older adults in Taiwan because older adults are used to gathering in such clubs.
The design of directional and informational signs involves the concept of repetitive reinforcement. Signs designed with both text descriptions and icons/pictures would improve user recognition ability (Wiseman et al., 1985) and significantly enhance user comprehension (Wolff and Wogalter, 1998; Scialfa et al., 2008). In addition, the findings of this study provide beneficial recommendations to design new interactive signs. An interactive sign such as a warning sign is a sign that reacts to the behavior of users and presents a corresponding meaning. The designers should use a familiar component in real life as a principal element of an interactive sign, and consider the semantic distance between symbol and meaning. This would lead to users comprehending the meaning of the interactive sign clearly and responding rapidly when they encounter it. In conclusion, this study demonstrated that age and symbol features are factors of influence in effective communication through symbols in public transportation systems. We suggest that training in symbol comprehension or material articulating symbol meaning be provided to residents or tourists, especially older adults. This would improve the usefulness of these symbols and satisfaction rate of passengers regarding the use of such icons, as well as avoid wasted time due to misunderstanding of confusion of symbol meaning.
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