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Usability guideline for banking software design
Computers in Human Behavior 62 (2016) 277e285
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Computers in Human Behavior journal homepage: www.elsevier.com/locate/comphumbeh
Full length article
Usability guideline for banking software design Cigdem Altin Gumussoy* Industrial Engineering Department, Faculty of Management, Istanbul Technical University, Istanbul, Turkey
a r t i c l e i n f o
a b s t r a c t
Article history: Received 15 July 2015 Received in revised form 18 March 2016 Accepted 1 April 2016
As the diversity of services in the financial market increases, it is critical to design usable banking software in order to overcome the complex structure of the system. The current study presents a usability guideline based on heuristics and their corresponding criteria that could be used during the early stages of banking software design process. In the design of a usability guideline, the heuristics and their criteria are categorized in terms of their effectiveness in solving usability problems grouped and ranging from usability catastrophe to cosmetic problems. The current study comprises of three main steps: First, actual usability problems from three banking software development projects are categorized according to their severity level. Secondly, usability criteria are rated for how well they explain the usability problems encountered. Finally, usability heuristics are categorized according to the severity level of usability problems through two analytical models; corresponding and cluster analyses. As the result, designers and project managers may give more importance to the heuristics related with the following usability problem categories: Usability catastrophe and then major usability problems. Furthermore, the proposed guideline can be used to understand which usability criteria would be helpful in explaining usability problems as well as preventing banking system catastrophes, by highlighting the critical parts in system design of banking software. © 2016 Elsevier Ltd. All rights reserved.
Keywords: Banking software Usability guideline Heuristics
1. Introduction Usability is an important attribute of software quality (ISO/ IEC9126, 1991) and impacts the system's acceptability level (Nielsen, 1993). Usability is defined as “the extent to which a product can be used by specified users to achieve specified goals with effectiveness, efficiency and satisfaction in a specific context of use” (ISO, 9241-11, 1998). According to Nielsen (1993), usability is associated with the learnability of the system, its efficiency of use, its easiness to remember, its ability to prevent and recover from errors, and user satisfaction. Usability offers several advantages for the system users such as “increased user productivity, decreased user errors, decreased training costs, increased savings from making changes earlier in design lifecycle, and decreased user support” (Mayhew, 2005). Although in the past, usability costs were considered as an additional cost in the project expense, the studies conducted by NetRaker revealed that revenues can be increased by 10%e35% with
* ITU Isletme Fakultesi, Endustri Muhendisligi Bolumu, 34367 Besiktas, Istanbul, Turkey. E-mail address: [email protected]. http://dx.doi.org/10.1016/j.chb.2016.04.001 0747-5632/© 2016 Elsevier Ltd. All rights reserved.
a 5% improvement in usability (Black, 2002). For example, IBM redesigned its site in accordance with usability principles with more than 100 employees “at a cost estimated in millions”. Before the redesign process, one of the most popular features of the site was ‘help button’. After the changes, the help button usage decreased 84% and sales increased 400% (Rauterberg, 2003). Accordingly, usability may be considered as one of the strategic factors to be dealt with during software design to improve the organizational productivity and performance (Juristo, Moreno, & Sanchez-Segura, 2007; Seffah & Metzker, 2004). As financial institutions regularly conduct their financial transactions via software, designing usable systems may be critical to enhance the quality of the software used in banking institutions. Usability issues need to be considered early during the software development phase to avoid design rework. Designing a usable system requires software developers to have “knowledge related to psychology, ergonomics, linguistics, etc. proper to the Human Computer Interaction field” (Juristo et al., 2007, p.1507). Software engineers do not prioritize this area as they generally have expertise in technical subjects such as techniques, methods and tools used in software design. Therefore, the generation of a usability guideline may help software engineers to build usable systems at the earlier development stage (Juristo et al., 2007).
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Our aim in this study is to propose a usability guideline specific to banking systems that can be used in banking software development projects by software developers. By doing so, software development professionals can conduct their own usability evaluations during the development phase with the first hand experience of the system. Furthermore, usability heuristics and their criteria are categorized according to the severity level of usability problems. Therefore, in the current usability guideline, not only the usability heuristics and their criteria are listed, but also they are categorized in terms of their effect in solving usability problems grouped within the range from usability catastrophe to cosmetic problems. This approach may solve the software designers' criticism about guidelines being detailed and hard to follow (Lin, Choong, & Salvendy, 1997). The remaining portion of the current paper is set out as follows: A review of the literature is presented in Section 2. Section 3 discusses the research methodology, and Section 4 includes the results obtained. The article concludes with the discussion of the findings, managerial implications and recommendations for future studies. 2. Literature review The literature on banking systems and usability revealed that there is a huge amount of studies that analyzes the effect of usability factors on the decision to use different banking systems (Kang, Lee, & Lee, 2012; Mohammadi, 2015; Susanto, Lee, Zo, & Ciganek, 2012; Thakur, 2014; Tsai, Chien, & Tsai, 2014; Yoon & Occena, 2014; Yoon & Steege, 2013). Yoon and Occena (2014) find that perceived security concerns as a security dimension, perceived usefulness and perceived ease of use as perceived usability have an impact on the perception of internet banking use via smart phones. Susanto et al. (2012) find the significant effect of website usability on initial trust and usage intention of internet banking. Kang et al. (2012) indicate that perceived usability, channel preference and perceived value are the determinants of continued mobile banking usage. Yoon and Steege (2013) define usability dimension with two factors; perceived usefulness, perceived ease of use and reveal that openness, website usability and perceived security concern influence the use of internet banking. Thakur (2014) finds that usability is a significant predictor of both customer satisfaction and trust in mobile banking. Tsai et al. (2014) define perceived system usability with the factors-perceived usefulness, perceived compatibility and perceived ease of use and they analyze the effect of perceived system usability on user satisfaction and continuous usage intention. The results show that perceived usefulness, perceived compatibility and satisfaction level affect the continuance intention of internet banking users. Mohammadi (2015) explores the mediating effect of both perceived ease of use and perceived usefulness in a mobile banking system between a system's barriers-resistance, perception of risk, lack of compatibility, low awareness, and consumers' attitudes. The results reveal that resistance has a significant negative effect on usefulness and ease of use, which in turn affect users' attitude and usage intention. As a result, those studies revealed that usability related factors are significant in the decision to use a banking system. However, the literature about the acceptance of a banking system do not direct the software developers about the significant usability related criteria that the developers can utilize during software development. In the current study, we aim to generate a usability guideline based on heuristics and their criteria that can be used as a toolkit during software development process. Several studies in the literature have used usability guidelines based on heuristics as a usability evaluation method for different systems: m-business and m-government (Alotaibi, 2016), Enterprise Resource Planning (ERP) (Singh & Wesson, 2009), electronic
shopping (Chen & Macredie, 2005), web-based information systems (Oztekin, Nikov, & Zaim, 2009), firms' websites (Agarwal & Venkatesh, 2002), university websites (Hasan, 2013), online health social networking paradigm (Yeratziotis, Pottas, & Van € r, Greunen, 2012), university library website (Delice & Güngo 2009), and IT security management tools (Jaferian, Hawkey, Sotirakopoulos, Velez-Rojos, & Beznosov, 2014). Alotaibi (2016) compares the usability of m-business and m-government software based on revised Nielsen's heuristics. The results reveal that there is a significant gap between m-business and m-government usability practices. Singh and Wesson (2009) define specific usability heuristics as navigation, presentation, task support, learnability and customization in assessing the usability of ERP systems. Chen and Macredie (2005) use heuristic evaluation to examine the usability of four shopping websites. They find that common usability problems are related with the heuristics user control and freedom and help/documentation. Oztekin et al. (2009) propose a methodology to assess the usability level of web-based information systems. They have found that among the usability factors, assurance, quality of information, reliability, and integration of communication are critical for the usability level of web-based information systems. Agarwal and Venkatesh (2002) compare the relative importance of five different attributes; content, ease of use, promotion, made-for-the-medium, and emotion in four industries: airlines, online bookstores, automobile manufacturers, and car rental agencies. They have found that content and made-for-themedium are the most significant attributes for airline, bookstore and car rental industries. Hasan (2013) identifies the usability problems encountered on the universities' websites and categorize the problems as navigational, design, content and ease of use and communication. Yeratziotis et al. (2012) develop a usable security heuristic evaluation in the context of online health social € r (2009) use Nielsen's ten networking paradigm. Delice and Güngo heuristics to reveal the usability problems of the university library's website and then rank the factors in regards to their importance with the Analytic Hierarchy Process. Jaferian et al. (2014) define a set of heuristics specific to IT security management tools such as visibility of activity status, history of actions and changes, flexible representation of information. In short, those studies revealed that the usability is context dependent; in case of banking software, usability criteria that are important should be specific to the banking software design. Therefore, a usability guideline that includes heuristics and their corresponding criteria are developed in order to use in the early banking software development projects. 3. Methodology The methodology can be divided into five main parts: First, the data on usability problems from three banking software development projects are used as the input data. Second, the problems are rated in terms of their severity by three experts. Third, each of the usability criteria is rated for how well each explains the usability problems. Fourth, a cross-tabulation which shows the total explanation ratings in terms of severity level of usability problems with respect to usability heuristics is prepared. Finally, usability heuristics and their criteria are categorized according to the severity level of usability problems with two analytical models- correspondence and cluster analyses. 3.1. Data The usability problems are gathered from a database of usability evaluations of the three banking software development projects, conducted by three experts including the author in the last two years. The software of the projects is aimed to be used by the
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employees of the banks and they are conducted by the IT departments of the banking institutions. In those projects, the banking software includes several functions such as money withdrawal, accounts inquiry, deposit, bank counter transactions, customer account information, message monitoring and SWIFT (Society for Worldwide Interbank Financial Telecommunication). Cognitive walkthrough technique is used to evaluate the banking software packages. In this technique, a team of three to five usability experts “simulate users walking through the interface to carry out typical tasks” (Shneiderman & Plaisant, 2010). Generally, representative tasks such as high-frequency tasks or critical tasks are selected to be walked through. A set of usability problems and their possible solutions are provided for system developers and project managers to review the software. In these projects, a total of 266 usability problems are identified. As an example for usability problems are as follows: “A consistent color coding is not used for the selected icons”, “The icon just near the Accounts menu does not conform the expectations of the users”, “In the search results, it is not easy to track the line”, “The error message does not inform the user about the severity of the message”, “Red and green colors cannot be recognized by the people with color blindness, these two colors cannot be used together”. 3.2. Severity ratings for usability problems Nielsen (1995a) defines the severity level of a usability problem by three factors: frequency, impact and persistence, which are identified with the following questions: Frequency: “Is it common or rare?” Impact: “Will it be easy or difficult for the users to overcome?” Persistence: “Is it a one-time problem that users can overcome once they know about it or will users repeatedly be bothered by the problem?” In the evaluation, Nielsen (1995a) combines these three factors into “a single severity rating for an overall assessment of each usability problem”. A scale of 0e4 rating is used to rate the severity of each usability problem, as shown in Table 1 (Nielsen, 1995a). In the current study, the severity of each 266 usability problems is rated based on above scale by three experts. Nielsen (1995a) indicates that “severity ratings from a single evaluator are too unreliable” and suggests that the “mean of a set of ratings from three evaluators” to be used. Each expert provides individual severity ratings independently of the other experts. The mean of the severity ratings of each expert is determined as the final severity rating of each problem. 3.3. Rating the usability heuristics in explaining usability problems Usability heuristics are defined as the general principles for interaction design (Nielsen, 1995b). Nielsen's (1994) set of heuristics are the “most widely used and regarded set of heuristics adopted for usability inspections” (Chen & Macredie, 2005). Therefore, ten heuristics proposed by Nielsen are used as the basis of heuristics in the current study. In the study of Muller, Matheson,
Page, and Gallup (1998), Nielsen's heuristics are criticized in terms of their focus only on systems itself and not on work processes. In their study, three additional heuristics related with human work processes are included as complementary to Nielsen's heuristics (Floyd, 1997; Muller et al., 1998). Therefore, in this study, a total of 13 heuristics adapted from Muller et al. (1998) are used and their explanations are given in Table 2. Usability heuristics includes broad rules of thumb (Nielsen, 1995b) and do not prescribe a step by step way to check an interface for usability conformance. The experts who are familiar with the rules, and are able to interpret and apply them may conduct the evaluation (Shneiderman & Plaisant, 2010). Therefore, it is necessary to develop a detailed and structured criteria list for each heuristic for thorough evaluation of the banking software. Subsequently, a set of usability criteria for each heuristic is adapted from Pierotti (1995). This detailed list includes 296 criteria in total for 13 heuristics. To define the heuristic ‘Visibility of system status’, a total of 29 criteria such as ‘Does the display begin with a title or header that describes screen contents’, ‘Is there a consistent icon design schema and stylistic treatment across the system’ or ‘ Is a single, selected icon clearly visible when surrounded by unselected icons?’ are used. To reveal the interaction between the severity level of the usability problems and the usability heuristics, each of the usability problems is rated by three experts for how well each of them is explained by any of the criteria of the heuristics. During the rating process, each problem may be mapped on more than one criterion since a usability problem can be reasoned from more than one criterion. A 6 point scale proposed by Nielsen (1994) with ‘0’ representing “does not explain the problem at all”, to ‘5’ representing “complete explanation of why this is a problem” is used. Due to the subjectivity of the rating, the final rating between any criteria and usability problems is admitted when at least two of the experts agree on the rating scale. Finally, a cross-tabulation including the total explanation ratings of usability problems for each severity level and usability heuristics is prepared. 3.4. Analytical procedures The analysis of cross-tabulation shows that it is not easy to understand which usability problems for each severity level has a close relationship with the usability heuristics. Therefore, two statistical techniques are used to reveal the proximity of each severity rating of usability problems to the usability heuristics. First, correspondence analysis is used for dimensional reduction and perceptual mapping. Dimensions show the features of an object. In determining the number of dimensions, a trade-off must be made between the explanatory power and interpretability (Hair, Anderson, Tatham, & Black, 1998). Perceptual map is a visual representation of responses on dimensions. In this study, the perceptual map reveals the position of severity rating of usability problems with respect to usability heuristics (Hair et al., 1998). However, with perceptual mapping, interpreting the classifications of usability heuristics and severity rating of the problems is difficult. Therefore, cluster analysis is performed as a complementary technique to group the severity
Table 1 Severity ratings of the usability problems (Nielsen, 1995a). S0 S1 S2 S3 S4
0 1 2 3 4
¼ ¼ ¼ ¼ ¼
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I don't agree that this is a usability problem at all Cosmetic problem only: Need not be fixed unless extra time is available on project Minor usability problem: Fixing this should be given low priority Major usability problem: Important to fix, so should be given high priority Usability catastrophe: Imperative to fix this before product can be released
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Table 2 Usability heuristics and their explanations. Usability heuristics
Explanations (Chen & Macredie, 2005)
H1: Visibility of system status H2: Match between system and the real world H3: User control and freedom H4: Consistency and standards
“The system should keep user informed about what is going on, through appropriate feedback within reasonable time.” “The system should speak the user's language, with words, phrases and concepts familiar to the user, rather than systemoriented terms. Follow real world conventions, making information appear in a natural and logical order.” “Users should be free to select and sequence tasks, rather than having the system do this for them.” “Users should not have to wonder whether different words, situations, or actions mean the same thing. Follow platform conventions.” “Error messages should be expressed in plain language (No Codes)”
H5: Help users recognize, diagnose, and recover from errors H6: Error prevention H7: Recognition rather than recall
“Even better than good error message in a careful design which prevents a problem from occurring in the first place?” “Make objects, actions, and options visible. The user should not have to remember information from one part of the dialog to another. Instructions for use of the system should be visible or easily retrievable whenever appropriate.” H8: Flexibility and minimalist design “Accelerators-unseen by the novice user- may often speed up the interaction for the expert user such that the system can cater to both inexperienced and experienced users. Allow users to tailor frequent actions. Provide alternative means of access and operation for users who differ from the average user (e.g., physical or cognitive ability, culture, language, etc.)” H9: Aesthetic and minimalist design “Dialogues should not contain information which is irrelevant or rarely needed. Every extra unit of information in a dialog competes with the relevant units of information and diminishes their relative visibility.” H10: Help and documentation “Even though it is better if the system can be used without documentation, it may be necessary to provide help and documentation. Any such information should be easy to search, focused on the user's task, list concrete steps to be carried out, and not be too large.” H11: Skills “The system should support, extend, supplement, or enhance the user's skills, background knowledge, and expertise—not replace them.” H12: Pleasurable and respectful interaction “The user's interaction with the system should enhance the quality of her or his work-life. The user should be treated with with the user respect. The design should be aesthetically pleasing- with artistic as well as functional value.” H13: Privacy “The system should help the user to protect personal or private information belonging to the user or his/her clients.”
rating of usability problems with the usability heuristics. The scores in dimensions of correspondence analysis are used as an input for the cluster analysis.
4. Results 4.1. Severity rating for usability problems A total of three problems are considered not to be a usability problem. Therefore, these three problems are extracted for further analysis. Among the remaining problems, experts agree that 34 problems relate to being only cosmetic problems, 74 problems to minor usability problems, 114 to major usability problems and the remaining 41 to be usability catastrophe.
4.2. Rating the usability heuristics in explaining usability problems Table 3 shows the total explanation ratings of usability problems in each severity level by each usability heuristics. As an example, the experts agree that the usability problems in the severity level of two (S2) are explained by the heuristic H7 with a total explanation rating of 219. The details are given in Table 3. The heuristic H13 is not included in Table 3, because none of the usability problems are mapped to this heuristic. Although Table 3 shows the total ratings of each severity level with respect to usability heuristics, it is not easy to understand which one has a proximity to another. Therefore, two analytical procedures-correspondence analysis and cluster analysis are used to explore the relative proximities to each other.
Table 3 Total explanation ratings for each severity level with respect to usability heuristics.
S1 S2 S3 S4
H1
H2
H3
H4
H5
H6
H7
H8
H9
H10
H11
H12
39 96 234 89
42 55 167 97
1 7 51 26
64 120 244 91
9 18 84 56
1 8 64 31
128 219 236 93
1 1 13 6
19 124 89 56
1 1 39 5
1 12 28 11
15 88 70 40
4.3. Correspondence analysis First, the appropriate number of dimensions is determined within a trade-off between the explanation rate and easy interpretation of the dimensions. The term inertia in correspondence analysis “indicates the relative contribution of each dimension in explaining the variance in the categories” (Hair et al., 1998). Table 4 shows the dimensionality, proportion explained and cumulative proportion explained. As seen from Table 4, 91.7% of the variance is explained by a two-dimensional solution. Only 8.3% of the explained variance can be increased by moving from two to three dimensions. The contribution of severity level of usability problems and the heuristics to the inertia of each dimension is shown in Table 5. As the two-dimensional solution is balancing the explanation rate of variance and interpretability, it is selected as the most appropriate dimensionality for further analysis. The perceptual map of the two-dimensional solution of severity level of usability problems and usability heuristics is shown in Fig. 1. The horizontal dimension is strongly influenced by the heuristics-help and documentation (H10) on one end, and aesthetic and minimalist design (H9) and pleasurable and respectful interaction with the user (H12) on the other. Therefore, this dimension differentiates the severity level of usability problems that are help oriented from those that are more aesthetic oriented. The vertical dimension is mainly constituted of the usability heuristics- recognition rather than recall (H7) and help/documentation (H10). Therefore, this dimension primarily differentiates the severity level of usability problems that are more information/documentation oriented from those that are low information/documentation oriented. As shown in Fig. 1, cosmetic problems have close associations with the heuristic- H7. Minor usability problems are related with the heuristics- H12 and H9. On the other hand, it is not easy to understand which heuristics are closely associated with major usability problems and usability catastrophe. Therefore, although some relationships between the heuristics and the severity level of usability problems are revealed with the correspondence analysis, some of them are not clearly shown; so cluster analysis is used as the complementary technique to the correspondence analysis.
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Table 4 The dimensions and its proportion explained. Dimension
Inertia
Proportion explained (%)
Cumulative proportion explained (%)
1 2 3 Total
0.075 0.019 0.007 0.101
71.9 19.9 8.3 100.0
71.9 91.7 100.0 100.0
Table 5 The contribution of severity level of usability problems and usability heuristics to the inertia of each dimension. Severity level of usability problems and usability heuristics
Marginal profile
Dimension 1
Dimension 2
S1: Cosmetic problem only S2: Minor usability problem S3: Major usability problem S4: Usability catastrophe H1: Visibility of system status H2: Match between system and the real world H3: User control and freedom H4: Consistency and standards H5: Help users recognize, diagnose, and recover from errors H6: Error prevention H7: Recognition rather than recall H8: Flexibility and minimalist design H9: Aesthetic and minimalist design H10: Help and documentation H11: Skills H12: Pleasurable and respectful interaction with the user
0.107 0.251 0.441 0.201 0.153 0.121 0.028 0.174 0.056 0.035 0.226 0.007 0.096 0.015 0.017 0.071
0.137 0.508 0.244 0.112 0.031 0.056 0.102 0.000 0.113 0.132 0.268 0.026 0.114 0.080 0.009 0.069
0.664 0.212 0.025 0.099 0.000 0.026 0.018 0.052 0.016 0.018 0.250 0.000 0.368 0.017 0.026 0.209
Fig. 1. The two-dimensional perceptual map of severity level of usability problems and usability heuristics.
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4.4. Cluster analysis Fig. 2 shows the proximity of the severity level of usability problems to the usability heuristics. As seen from Fig. 2, usability problems that are counted as catastrophic seem to have a close proximity to the heuristic- H11. Furthermore, the proximity of major usability problems to the heuristics- H2, H1, and H4 implies that major usability problems are related to these heuristics. Therefore, the design of a banking software system with regard to these heuristics may prevent the occurrence of catastrophic problems and major usability problems. In addition, cosmetic problems are found to be related with the heuristics-H7, and minor usability problems seem to have optimal proximity to the heuristics- H12 and H9. Furthermore, as it can be seen from Fig. 2, the severity level of major usability problem, its related usability heuristics (H2, H1, H4), usability catastrophe and its related usability heuristic (H11) are related to each other. This implies that conformity to the heuristicsH2, H1, and H4 may also decrease the problems that are accounted as usability catastrophe. Furthermore, enhancements in the heuristic- H11 may also reduce the problems that are accounted as major usability problems. In addition to these findings, the heuristics-H10, H8, H5, H6 and H3 are related with the problems that are considered as catastrophic and major.
4.5. Development of usability guideline for banking software Based on the findings from the analysis, a usability guideline can be proposed to software designers that can be used in the design process of usable banking software. For each severity level of usability problems, the heuristics and their corresponding criteria obtained from cluster analysis are listed in descending order in terms of their total explanation ratings. For example, the heuristic H2 has a close proximity to the major usability problems. Therefore, the criteria of the heuristic H2 are taken to the guideline list if any of them explain major usability problems. In the criteria list of each heuristics, the ones early in the list have a higher total explanation rating and importance in the banking software design than the others. In addition, the cluster analysis reveals that the heuristics H10, H8, H5, H6 and H3 are found to have positive effects in solving the problems that are considered as catastrophic and major. That is why; these heuristics and their corresponding criteria are placed
after the severity levels of S3 and S4. Table 6 shows the usability heuristics, their criteria and severity level of usability problems. As it can be seen from the table, the proposed guideline allows a detailed list of usability heuristics and criteria for each severity level of the usability problems. This guideline can be used at the beginning of the design process, designers and project managers may give more importance to the heuristics related with the following usability problem categories: Usability catastrophe and then major usability problems. This will reduce the rework time at later stages of software development. 5. Conclusion & discussion The aim of the current study is to propose a usability guideline that system developers may use in banking software design. This guideline highlights the critical points in the design of software to prevent banking system catastrophe. In the analysis, actual usability problems from three banking software development projects are considered as potential problems that users may experience during system usage. These problems are grouped according to their severity by three experts. Then, each of the usability criteria from 13 heuristics are rated for how well it explains the usability problems in order to reveal the interaction between the severity level of usability problems and the usability heuristics. As a result, in the usability guideline, a set of heuristics and their criteria are categorized in terms of their effect in solving usability problems that are grouped within the range of usability catastrophe to cosmetic problems. The current study reveals that the usability heuristics- skills (H11) is related with the usability problems that are considered to be usability catastrophe, and the heuristics- match between the system and the real world (H2), visibility of system status (H1), consistency and standards (H4) are found to be related with major usability problems. In addition to these heuristics, enhancements to the usability heuristics- user control and freedom (H3), error prevention (H6), help users recognize, diagnose and recover from errors (H5), flexibility and minimalist design (H8), and help and documentation (H10) may also prevent the problems that are considered to be both catastrophic and major usability problems. In the design process of banking software, system designers may give higher priority to these heuristics in order to prevent usability problems that have to be fixed before the release of banking
H3: User control and freedom H6: Error prevention H5: Help users recognize, diagnose and recover from errors H8: Flexibility and minimalist design H10: Help and documentation S4: Usability catastrophe H11: Skills S3: Major usability problem H2: Match between system and the real world H1: Visibility of system status H4: Consistency and standards H9: Aesthetic and minimalist design H12: Pleasurable and respectful interaction with the user S2: Minor usability problem S1: Cosmetic problem only H7: Recognition rather than recall Fig. 2. Dendrogram of severity level of usability problems and usability heuristics.
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Table 6 A guideline for banking software. Severity level of usability problems
Usability heuristics and their criteria (Muller et al., 1998; Pierotti, 1995)
S4: Usability catastrophe
H11: Skills Are window operations easy to learn and use? If the system has deep (multilevel) menus, do users have the option of typing ahead? H2: Match between system and the real world If shape is used as a visual cue, does it match cultural conventions? Are field level prompts provided for data entry screens? Do related and interdependent fields appear on the same screen? Do the selected colors correspond to common expectations about color codes? Are icons concrete and familiar? When prompts imply a necessary action, are the words in the message consistent with that action? Do menu choices fit logically into categories that have readily understood meanings? Are menu choices ordered in the most logical way, given the user, the item names, and the task variables? H1: Visibility of system status Does the system provide visibility: that is, by looking, can the user tell the state of the system and the alternatives for action? Is the current status of icon clearly indicated? Is there visual feedback in menus or dialog boxes about which choices are selectable? Is the menu naming terminology consistent with the user's task domain? Does every display begin with a title or header that describes screen contents? Is there a consistent icon design scheme and stylistic treatment across the system? Is a single, selected icon clearly visible when surrounded by unselected icons? H4: Consistency and standards Have industry or company formatting standards been followed consistently in all screens within a system? Are icons labeled? Are there salient visual cues to identify the active window? Are field labels consistent from one data entry screen to another? Do abbreviations follow a simple primary rule and, if necessary, a simple secondary rule for abbreviations that otherwise would be duplicates? H10: Help and documentation Are data entry screens and dialog boxes supported by navigation and completion instructions? Is the help function visible; for example, a key labeled HELP or a special menu? If menu choices are ambiguous, does the system provide additional explanatory information when an item is selected? H8: Flexibility and minimalist design Does the system offer “find next” and “find previous shortcuts” for database search? If the system uses a type-ahead strategy, do the menu items have mnemonic codes? If menu lists are short (seven items or fewer), can users select an item by moving the cursor? H5: Help users recognize, diagnose, and recover from errors Do error messages inform the user of the error's severity? Do error messages provide appropriate semantic information? Do error messages indicate what action the user needs to take to correct the error? Do all error messages in the system use consistent grammatical style, form, terminology and abbreviations? H6: Error prevention Does the system prevent users from making errors whenever possible? Are menu choices logical, distinctive, and mutually exclusive? If the database includes groups of data, can users enter more than one group on a single screen? Is the menu choice name on a higher-level menu used as the menu title of the lower-level menu? H3: User control and freedom In systems that use overlapping windows, is it easy for users to switch between windows? Can users easily reverse their actions? Is there an “undo” function at the level of a single action, a data entry, and a complete group of actions? Are menus broad (many items on a menu) rather than deep (many menu levels)? H12: Pleasurable and respectful interaction with the user Has color been used specifically to draw attention, communicate organization, indicate status changes, and establish relationships? Has color been used with discretion? H9: Aesthetic and minimalist design Does each icon stand out from its background? Have large objects, bold lines, and simple areas been used to distinguish icons? Is only (and all) information essential to decision making displayed on the screen? Are all icons in a set visually and conceptually distinct? Are field labels brief, familiar, and descriptive? H7: Recognition rather than recall Have zones been separated by spaces, lines, color, letters, bold titles, rules lines, or shaded areas? Are borders used to identify meaningful groups? Are size, boldface, underlying, color, shading, or typography used to show relative quantity or importance of different screen items? Is color coding consistent throughout the system?
S3: Major usability problems
For S4: Usability Catastrophe and S3: Major Usability problems
S2: Minor usability problem
S1: Cosmetic problem only
software. This list can also be used as a guideline in the early stages of software development in order to decrease the required rework at the later stages of development. Similar to our findings, Bass and
John (2003) define usability recommendations that have a high impact on architectural design. The recommendations defined in that study overlap in some aspects with the findings of our study.
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For example, the current study's heuristics- help users recognize, diagnose and recover from errors (H5) and error prevention (H6) are parallel to Bass and John's (2003) recommendations including; ‘maintaining device independence and recovering from failure’ and ‘using applications concurrently and checking for correctness’, respectively. In addition, the current study's criteria regarding to the heuristic- skills (H11) is parallel to Bass and John's (2003) recommendations including; ‘aggregating data or commands’, ‘leveraging human knowledge’ and ‘observing human needs and capabilities’. Software developers can design the software according to the expectations and needs of the potential users in order to improve the learning process and use. Moreover, the usability recommendations of Bass and John; ‘reusing information’, ‘supporting international use’, ‘modifying interfaces’ and ‘supporting personalization’ are related with the heuristic of the current studyuser control and freedom (H3). Furthermore, Bass and John have mentioned that ‘providing good help’ is one of the potential usability benefits for system users. These overlaps ensure the current study's validity that having a list of heuristics and their important criteria is important in guiding system designers in building system software. However, as Carjaval, Moreno, Sanchez-Segura, and Seffah (2013) mention incorporating usability factors in the software design is not straightforward. Software developers may have training on how to integrate these usability factors in the design of software. The second finding of the current study is that minor usability problems are associated with the heuristics; pleasurable and respectful interaction with the user (H12) and aesthetic and minimalist design (H9). Furthermore, cosmetic problems are found to be related with the heuristic- recognition rather than recall (H7). The examination of the criteria show that the heuristics are related with the user interface part of the system, such as color of the background, color changes with the status changes or the aesthetic design of the icons. These criteria are found to have less importance with respect to the criteria of major usability problems and usability catastrophe. Similar to our findings Juristo et al. (2007) state that user interface related issues require slight modifications and are only “confined to the interface component or subsystem, having no impact on system core”. Furthermore, the aesthetic related issues may be influential in the initial visits but content related issues become more important for the subsequent usages (Sutcliffe, 2001). Finally, the results reveal that usability problems encountered during three usability projects do not map on any of the criteria of the heuristic, privacy. This implies that financial institutions are aware of the importance of privacy in financial software, so they give importance to the protection of customers' information and are sensitive about not sharing any private information. Similar to our findings, Knutson (2007) states that customer trust to software is important in the decision to use e-commerce systems, and privacy can be a strong option for companies to become a market differentiator. 5.1. Managerial implications Software design and usability are related to each other and should be dealt with together at the design process. On the other hand, generally system designers do not pay attention to usability issues or have a lack of expertise about developing usable systems so they only focus on the functionalities of the system. Therefore, companies may organize usability training for the software designers during the project lifecycle about the importance of usability and how to integrate the usability aspects into the software design. In the first place, the criteria that are related with the severity level of major usability problems and usability catastrophe
may be explained in detail to software designers. This will allow the reduction in project completion time and cost, since rework after completing the project may be more costly and more time consuming than the time spent for training. 5.2. Future studies As a further study, similar to the current study, the same approach may be applied to other types of software to explore the significant usability criteria for specific systems and guide software developers. Second, this study only proposes the important criteria for banking software design. So, a further study with experiments analyzing how the criteria can be embedded into the design process could be conducted. References Agarwal, R., & Venkatesh, V. (2002). Assessing a firm's web presence: a heuristic evaluation procedure for the measurement of usability. Information Systems Research, 13(2), 168e186. Alotaibi, M. B. (2016). Comparing the usability of m-business and m-government software in Saudi Arabia. A revise of Nielsen's heuristics method. International Journal of Advanced Computer Science and Applications, 7(1), 1e7. Bass, L., & John, B. E. (2003). Linking usability to software architecture patterns through general scenarios. Journal of Systems and Software, 66(3), 187e197. Black, J. (2002). Usability is next to profitability. Business Week Online. http://www. bloomberg.com/bw/stories/2002-12-03/usability-is-next-to-profitability. Carjaval, L., Moreno, A. M., Sanchez-Segura, M., & Seffah, A. (2013). Usability through software design. IEEE Transactions on Software Engineering, 39(11), 1582e1596. Chen, S. Y., & Macredie, R. D. (2005). The assessment of usability of electronic shopping: a heuristic evaluation. International Journal of Information Management, 25, 516e532. € r, Z. (2009). The usability analysis with heuristic evaluation Delice, E. K., & Güngo and analytic hierarchy process. International Journal of Industrial Ergonomics, 39, 934e939. Floyd, C. (1997). Outline of a paradigm change in software engineering. In G. Bjerkness, P. Ehn, & M. Kyng (Eds.), Computers and democracy: A Scandinavian challenge. Brookfield VT: Gower. Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis with readings. Englewood Cliffs, NJ: Prentice-Hall. Hasan, L. (2013). Heuristic evaluation of three Jordanian university websites. Informatics in Education, 12(2), 231e251. ISO 9241-11. (1998). Guidelines for specifying and measuring usability. ISO/IEC. (1991). ISO 9126. Information technology-Software quality characteristics and metrics. Jaferian, P., Hawkey, K., Sotirakopoulos, A., Velez-Rojos, M., & Beznosov, K. (2014). Heuristics for evaluating IT security management tools. Human-Computer Interaction, 29, 311e350. Juristo, N., Moreno, A. M., & Sanchez-Segura, M. (2007). Analyzing the impact of usability on software design. The Journal of Systems and Software, 80, 1506e1516. Kang, H., Lee, M. J., & Lee, J. K. (2012). Are you still with us? A study of the postadoption determinants of sustained use of mobile-banking services. Journal of Organizational Computing and Electronic Commerce, 22(2), 132e159. Knutson, T. R. (2007). Building privacy into software products and services. IEEE Security and Privacy, 5(3), 72e74. Lin, H. X., Choong, Y., & Salvendy, G. (1997). A proposed index of usability: a method for comparing the relative usability of different software systems. Behaviour and Information Technology, 16(4/5), 267e278. Mayhew, D. J. (2005). Cost-justifying usability: An update for internet age. http:// www.globalspec.com/reference/42279/203279/chapter-2-user-interfacedesign-s-return-on-investment-examples-and-statistics. Mohammadi, H. (2015). A study of mobile banking loyalty in Iran. Computers in Human Behavior, 44, 35e47. Muller, M., Matheson, L., Page, C., & Gallup, R. (1998). Methods and tools: Participatory heuristic evaluation. Interaction, 5, 13e18. Nielsen, J. (1993). Usability engineering. Boston, MA: AP Professional. Nielsen, J. (1994). Enhancing the explanatory power of usability heuristics. In Proceedings of CHI-94 conference (pp. 152e158). Nielsen, J. (1995a). Severity ratings for usability problems. http://www.nngroup.com/ articles/how-to-rate-the-severity-of-usability-problems/. Nielsen, J. (1995b). 10 usability heuristics for user interface design. http://www. nngroup.com/articles/ten-usability-heuristics/. Oztekin, A., Nikov, A., & Zaim, S. (2009). UWIS: an assessment methodology for usability of web-based information systems. The Journal of Systems and Software, 82, 2038e2050. Pierotti, D. (1995). Usability analysis & design. Xerox Corporation. https://web.fe.up. pt/~ei08119/wiki/lib/exe/fetch.php?media¼heuristic_evaluation_-_system_ checklist.pdf.
C. Altin Gumussoy / Computers in Human Behavior 62 (2016) 277e285 Rauterberg, M. (2003). Cost justifying usability-State of the art overview. http://www. idemployee.id.tue.nl/g.w.m.rauterberg/publications/ CostJustifyingUsability2003.pdf. Seffah, A., & Metzker, E. (2004). The obstacles and myths of usability and software engineering. Communications of the ACM, 47(1), 71e76. Shneiderman, B., & Plaisant, C. (2010). Designing the user interface strategies for effective human-computer interaction (5th ed.). Addison-Wesley. Singh, A., & Wesson, J. (2009). Evaluation criteria for assessing the usability of ERP systems. In SAICSIT, 12e14 october, riverside, Vanderbijlpark, South Africa. Susanto, A., Lee, H., Zo, H., & Ciganek, A. P. (2012). User acceptance of internet banking in Indonesia: initial trust formation. Information Development, 29(4), 309e322. Sutcliffe, A. (2001). Heuristic evaluation of website attractiveness and usability. In Johnson (Ed.), DSV-IS 2001, LNCS 2220 (pp. 183e198).
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Full length article
Usability guideline for banking software design Cigdem Altin Gumussoy* Industrial Engineering Department, Faculty of Management, Istanbul Technical University, Istanbul, Turkey
a r t i c l e i n f o
a b s t r a c t
Article history: Received 15 July 2015 Received in revised form 18 March 2016 Accepted 1 April 2016
As the diversity of services in the financial market increases, it is critical to design usable banking software in order to overcome the complex structure of the system. The current study presents a usability guideline based on heuristics and their corresponding criteria that could be used during the early stages of banking software design process. In the design of a usability guideline, the heuristics and their criteria are categorized in terms of their effectiveness in solving usability problems grouped and ranging from usability catastrophe to cosmetic problems. The current study comprises of three main steps: First, actual usability problems from three banking software development projects are categorized according to their severity level. Secondly, usability criteria are rated for how well they explain the usability problems encountered. Finally, usability heuristics are categorized according to the severity level of usability problems through two analytical models; corresponding and cluster analyses. As the result, designers and project managers may give more importance to the heuristics related with the following usability problem categories: Usability catastrophe and then major usability problems. Furthermore, the proposed guideline can be used to understand which usability criteria would be helpful in explaining usability problems as well as preventing banking system catastrophes, by highlighting the critical parts in system design of banking software. © 2016 Elsevier Ltd. All rights reserved.
Keywords: Banking software Usability guideline Heuristics
1. Introduction Usability is an important attribute of software quality (ISO/ IEC9126, 1991) and impacts the system's acceptability level (Nielsen, 1993). Usability is defined as “the extent to which a product can be used by specified users to achieve specified goals with effectiveness, efficiency and satisfaction in a specific context of use” (ISO, 9241-11, 1998). According to Nielsen (1993), usability is associated with the learnability of the system, its efficiency of use, its easiness to remember, its ability to prevent and recover from errors, and user satisfaction. Usability offers several advantages for the system users such as “increased user productivity, decreased user errors, decreased training costs, increased savings from making changes earlier in design lifecycle, and decreased user support” (Mayhew, 2005). Although in the past, usability costs were considered as an additional cost in the project expense, the studies conducted by NetRaker revealed that revenues can be increased by 10%e35% with
* ITU Isletme Fakultesi, Endustri Muhendisligi Bolumu, 34367 Besiktas, Istanbul, Turkey. E-mail address: [email protected]. http://dx.doi.org/10.1016/j.chb.2016.04.001 0747-5632/© 2016 Elsevier Ltd. All rights reserved.
a 5% improvement in usability (Black, 2002). For example, IBM redesigned its site in accordance with usability principles with more than 100 employees “at a cost estimated in millions”. Before the redesign process, one of the most popular features of the site was ‘help button’. After the changes, the help button usage decreased 84% and sales increased 400% (Rauterberg, 2003). Accordingly, usability may be considered as one of the strategic factors to be dealt with during software design to improve the organizational productivity and performance (Juristo, Moreno, & Sanchez-Segura, 2007; Seffah & Metzker, 2004). As financial institutions regularly conduct their financial transactions via software, designing usable systems may be critical to enhance the quality of the software used in banking institutions. Usability issues need to be considered early during the software development phase to avoid design rework. Designing a usable system requires software developers to have “knowledge related to psychology, ergonomics, linguistics, etc. proper to the Human Computer Interaction field” (Juristo et al., 2007, p.1507). Software engineers do not prioritize this area as they generally have expertise in technical subjects such as techniques, methods and tools used in software design. Therefore, the generation of a usability guideline may help software engineers to build usable systems at the earlier development stage (Juristo et al., 2007).
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Our aim in this study is to propose a usability guideline specific to banking systems that can be used in banking software development projects by software developers. By doing so, software development professionals can conduct their own usability evaluations during the development phase with the first hand experience of the system. Furthermore, usability heuristics and their criteria are categorized according to the severity level of usability problems. Therefore, in the current usability guideline, not only the usability heuristics and their criteria are listed, but also they are categorized in terms of their effect in solving usability problems grouped within the range from usability catastrophe to cosmetic problems. This approach may solve the software designers' criticism about guidelines being detailed and hard to follow (Lin, Choong, & Salvendy, 1997). The remaining portion of the current paper is set out as follows: A review of the literature is presented in Section 2. Section 3 discusses the research methodology, and Section 4 includes the results obtained. The article concludes with the discussion of the findings, managerial implications and recommendations for future studies. 2. Literature review The literature on banking systems and usability revealed that there is a huge amount of studies that analyzes the effect of usability factors on the decision to use different banking systems (Kang, Lee, & Lee, 2012; Mohammadi, 2015; Susanto, Lee, Zo, & Ciganek, 2012; Thakur, 2014; Tsai, Chien, & Tsai, 2014; Yoon & Occena, 2014; Yoon & Steege, 2013). Yoon and Occena (2014) find that perceived security concerns as a security dimension, perceived usefulness and perceived ease of use as perceived usability have an impact on the perception of internet banking use via smart phones. Susanto et al. (2012) find the significant effect of website usability on initial trust and usage intention of internet banking. Kang et al. (2012) indicate that perceived usability, channel preference and perceived value are the determinants of continued mobile banking usage. Yoon and Steege (2013) define usability dimension with two factors; perceived usefulness, perceived ease of use and reveal that openness, website usability and perceived security concern influence the use of internet banking. Thakur (2014) finds that usability is a significant predictor of both customer satisfaction and trust in mobile banking. Tsai et al. (2014) define perceived system usability with the factors-perceived usefulness, perceived compatibility and perceived ease of use and they analyze the effect of perceived system usability on user satisfaction and continuous usage intention. The results show that perceived usefulness, perceived compatibility and satisfaction level affect the continuance intention of internet banking users. Mohammadi (2015) explores the mediating effect of both perceived ease of use and perceived usefulness in a mobile banking system between a system's barriers-resistance, perception of risk, lack of compatibility, low awareness, and consumers' attitudes. The results reveal that resistance has a significant negative effect on usefulness and ease of use, which in turn affect users' attitude and usage intention. As a result, those studies revealed that usability related factors are significant in the decision to use a banking system. However, the literature about the acceptance of a banking system do not direct the software developers about the significant usability related criteria that the developers can utilize during software development. In the current study, we aim to generate a usability guideline based on heuristics and their criteria that can be used as a toolkit during software development process. Several studies in the literature have used usability guidelines based on heuristics as a usability evaluation method for different systems: m-business and m-government (Alotaibi, 2016), Enterprise Resource Planning (ERP) (Singh & Wesson, 2009), electronic
shopping (Chen & Macredie, 2005), web-based information systems (Oztekin, Nikov, & Zaim, 2009), firms' websites (Agarwal & Venkatesh, 2002), university websites (Hasan, 2013), online health social networking paradigm (Yeratziotis, Pottas, & Van € r, Greunen, 2012), university library website (Delice & Güngo 2009), and IT security management tools (Jaferian, Hawkey, Sotirakopoulos, Velez-Rojos, & Beznosov, 2014). Alotaibi (2016) compares the usability of m-business and m-government software based on revised Nielsen's heuristics. The results reveal that there is a significant gap between m-business and m-government usability practices. Singh and Wesson (2009) define specific usability heuristics as navigation, presentation, task support, learnability and customization in assessing the usability of ERP systems. Chen and Macredie (2005) use heuristic evaluation to examine the usability of four shopping websites. They find that common usability problems are related with the heuristics user control and freedom and help/documentation. Oztekin et al. (2009) propose a methodology to assess the usability level of web-based information systems. They have found that among the usability factors, assurance, quality of information, reliability, and integration of communication are critical for the usability level of web-based information systems. Agarwal and Venkatesh (2002) compare the relative importance of five different attributes; content, ease of use, promotion, made-for-the-medium, and emotion in four industries: airlines, online bookstores, automobile manufacturers, and car rental agencies. They have found that content and made-for-themedium are the most significant attributes for airline, bookstore and car rental industries. Hasan (2013) identifies the usability problems encountered on the universities' websites and categorize the problems as navigational, design, content and ease of use and communication. Yeratziotis et al. (2012) develop a usable security heuristic evaluation in the context of online health social € r (2009) use Nielsen's ten networking paradigm. Delice and Güngo heuristics to reveal the usability problems of the university library's website and then rank the factors in regards to their importance with the Analytic Hierarchy Process. Jaferian et al. (2014) define a set of heuristics specific to IT security management tools such as visibility of activity status, history of actions and changes, flexible representation of information. In short, those studies revealed that the usability is context dependent; in case of banking software, usability criteria that are important should be specific to the banking software design. Therefore, a usability guideline that includes heuristics and their corresponding criteria are developed in order to use in the early banking software development projects. 3. Methodology The methodology can be divided into five main parts: First, the data on usability problems from three banking software development projects are used as the input data. Second, the problems are rated in terms of their severity by three experts. Third, each of the usability criteria is rated for how well each explains the usability problems. Fourth, a cross-tabulation which shows the total explanation ratings in terms of severity level of usability problems with respect to usability heuristics is prepared. Finally, usability heuristics and their criteria are categorized according to the severity level of usability problems with two analytical models- correspondence and cluster analyses. 3.1. Data The usability problems are gathered from a database of usability evaluations of the three banking software development projects, conducted by three experts including the author in the last two years. The software of the projects is aimed to be used by the
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employees of the banks and they are conducted by the IT departments of the banking institutions. In those projects, the banking software includes several functions such as money withdrawal, accounts inquiry, deposit, bank counter transactions, customer account information, message monitoring and SWIFT (Society for Worldwide Interbank Financial Telecommunication). Cognitive walkthrough technique is used to evaluate the banking software packages. In this technique, a team of three to five usability experts “simulate users walking through the interface to carry out typical tasks” (Shneiderman & Plaisant, 2010). Generally, representative tasks such as high-frequency tasks or critical tasks are selected to be walked through. A set of usability problems and their possible solutions are provided for system developers and project managers to review the software. In these projects, a total of 266 usability problems are identified. As an example for usability problems are as follows: “A consistent color coding is not used for the selected icons”, “The icon just near the Accounts menu does not conform the expectations of the users”, “In the search results, it is not easy to track the line”, “The error message does not inform the user about the severity of the message”, “Red and green colors cannot be recognized by the people with color blindness, these two colors cannot be used together”. 3.2. Severity ratings for usability problems Nielsen (1995a) defines the severity level of a usability problem by three factors: frequency, impact and persistence, which are identified with the following questions: Frequency: “Is it common or rare?” Impact: “Will it be easy or difficult for the users to overcome?” Persistence: “Is it a one-time problem that users can overcome once they know about it or will users repeatedly be bothered by the problem?” In the evaluation, Nielsen (1995a) combines these three factors into “a single severity rating for an overall assessment of each usability problem”. A scale of 0e4 rating is used to rate the severity of each usability problem, as shown in Table 1 (Nielsen, 1995a). In the current study, the severity of each 266 usability problems is rated based on above scale by three experts. Nielsen (1995a) indicates that “severity ratings from a single evaluator are too unreliable” and suggests that the “mean of a set of ratings from three evaluators” to be used. Each expert provides individual severity ratings independently of the other experts. The mean of the severity ratings of each expert is determined as the final severity rating of each problem. 3.3. Rating the usability heuristics in explaining usability problems Usability heuristics are defined as the general principles for interaction design (Nielsen, 1995b). Nielsen's (1994) set of heuristics are the “most widely used and regarded set of heuristics adopted for usability inspections” (Chen & Macredie, 2005). Therefore, ten heuristics proposed by Nielsen are used as the basis of heuristics in the current study. In the study of Muller, Matheson,
Page, and Gallup (1998), Nielsen's heuristics are criticized in terms of their focus only on systems itself and not on work processes. In their study, three additional heuristics related with human work processes are included as complementary to Nielsen's heuristics (Floyd, 1997; Muller et al., 1998). Therefore, in this study, a total of 13 heuristics adapted from Muller et al. (1998) are used and their explanations are given in Table 2. Usability heuristics includes broad rules of thumb (Nielsen, 1995b) and do not prescribe a step by step way to check an interface for usability conformance. The experts who are familiar with the rules, and are able to interpret and apply them may conduct the evaluation (Shneiderman & Plaisant, 2010). Therefore, it is necessary to develop a detailed and structured criteria list for each heuristic for thorough evaluation of the banking software. Subsequently, a set of usability criteria for each heuristic is adapted from Pierotti (1995). This detailed list includes 296 criteria in total for 13 heuristics. To define the heuristic ‘Visibility of system status’, a total of 29 criteria such as ‘Does the display begin with a title or header that describes screen contents’, ‘Is there a consistent icon design schema and stylistic treatment across the system’ or ‘ Is a single, selected icon clearly visible when surrounded by unselected icons?’ are used. To reveal the interaction between the severity level of the usability problems and the usability heuristics, each of the usability problems is rated by three experts for how well each of them is explained by any of the criteria of the heuristics. During the rating process, each problem may be mapped on more than one criterion since a usability problem can be reasoned from more than one criterion. A 6 point scale proposed by Nielsen (1994) with ‘0’ representing “does not explain the problem at all”, to ‘5’ representing “complete explanation of why this is a problem” is used. Due to the subjectivity of the rating, the final rating between any criteria and usability problems is admitted when at least two of the experts agree on the rating scale. Finally, a cross-tabulation including the total explanation ratings of usability problems for each severity level and usability heuristics is prepared. 3.4. Analytical procedures The analysis of cross-tabulation shows that it is not easy to understand which usability problems for each severity level has a close relationship with the usability heuristics. Therefore, two statistical techniques are used to reveal the proximity of each severity rating of usability problems to the usability heuristics. First, correspondence analysis is used for dimensional reduction and perceptual mapping. Dimensions show the features of an object. In determining the number of dimensions, a trade-off must be made between the explanatory power and interpretability (Hair, Anderson, Tatham, & Black, 1998). Perceptual map is a visual representation of responses on dimensions. In this study, the perceptual map reveals the position of severity rating of usability problems with respect to usability heuristics (Hair et al., 1998). However, with perceptual mapping, interpreting the classifications of usability heuristics and severity rating of the problems is difficult. Therefore, cluster analysis is performed as a complementary technique to group the severity
Table 1 Severity ratings of the usability problems (Nielsen, 1995a). S0 S1 S2 S3 S4
0 1 2 3 4
¼ ¼ ¼ ¼ ¼
279
I don't agree that this is a usability problem at all Cosmetic problem only: Need not be fixed unless extra time is available on project Minor usability problem: Fixing this should be given low priority Major usability problem: Important to fix, so should be given high priority Usability catastrophe: Imperative to fix this before product can be released
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Table 2 Usability heuristics and their explanations. Usability heuristics
Explanations (Chen & Macredie, 2005)
H1: Visibility of system status H2: Match between system and the real world H3: User control and freedom H4: Consistency and standards
“The system should keep user informed about what is going on, through appropriate feedback within reasonable time.” “The system should speak the user's language, with words, phrases and concepts familiar to the user, rather than systemoriented terms. Follow real world conventions, making information appear in a natural and logical order.” “Users should be free to select and sequence tasks, rather than having the system do this for them.” “Users should not have to wonder whether different words, situations, or actions mean the same thing. Follow platform conventions.” “Error messages should be expressed in plain language (No Codes)”
H5: Help users recognize, diagnose, and recover from errors H6: Error prevention H7: Recognition rather than recall
“Even better than good error message in a careful design which prevents a problem from occurring in the first place?” “Make objects, actions, and options visible. The user should not have to remember information from one part of the dialog to another. Instructions for use of the system should be visible or easily retrievable whenever appropriate.” H8: Flexibility and minimalist design “Accelerators-unseen by the novice user- may often speed up the interaction for the expert user such that the system can cater to both inexperienced and experienced users. Allow users to tailor frequent actions. Provide alternative means of access and operation for users who differ from the average user (e.g., physical or cognitive ability, culture, language, etc.)” H9: Aesthetic and minimalist design “Dialogues should not contain information which is irrelevant or rarely needed. Every extra unit of information in a dialog competes with the relevant units of information and diminishes their relative visibility.” H10: Help and documentation “Even though it is better if the system can be used without documentation, it may be necessary to provide help and documentation. Any such information should be easy to search, focused on the user's task, list concrete steps to be carried out, and not be too large.” H11: Skills “The system should support, extend, supplement, or enhance the user's skills, background knowledge, and expertise—not replace them.” H12: Pleasurable and respectful interaction “The user's interaction with the system should enhance the quality of her or his work-life. The user should be treated with with the user respect. The design should be aesthetically pleasing- with artistic as well as functional value.” H13: Privacy “The system should help the user to protect personal or private information belonging to the user or his/her clients.”
rating of usability problems with the usability heuristics. The scores in dimensions of correspondence analysis are used as an input for the cluster analysis.
4. Results 4.1. Severity rating for usability problems A total of three problems are considered not to be a usability problem. Therefore, these three problems are extracted for further analysis. Among the remaining problems, experts agree that 34 problems relate to being only cosmetic problems, 74 problems to minor usability problems, 114 to major usability problems and the remaining 41 to be usability catastrophe.
4.2. Rating the usability heuristics in explaining usability problems Table 3 shows the total explanation ratings of usability problems in each severity level by each usability heuristics. As an example, the experts agree that the usability problems in the severity level of two (S2) are explained by the heuristic H7 with a total explanation rating of 219. The details are given in Table 3. The heuristic H13 is not included in Table 3, because none of the usability problems are mapped to this heuristic. Although Table 3 shows the total ratings of each severity level with respect to usability heuristics, it is not easy to understand which one has a proximity to another. Therefore, two analytical procedures-correspondence analysis and cluster analysis are used to explore the relative proximities to each other.
Table 3 Total explanation ratings for each severity level with respect to usability heuristics.
S1 S2 S3 S4
H1
H2
H3
H4
H5
H6
H7
H8
H9
H10
H11
H12
39 96 234 89
42 55 167 97
1 7 51 26
64 120 244 91
9 18 84 56
1 8 64 31
128 219 236 93
1 1 13 6
19 124 89 56
1 1 39 5
1 12 28 11
15 88 70 40
4.3. Correspondence analysis First, the appropriate number of dimensions is determined within a trade-off between the explanation rate and easy interpretation of the dimensions. The term inertia in correspondence analysis “indicates the relative contribution of each dimension in explaining the variance in the categories” (Hair et al., 1998). Table 4 shows the dimensionality, proportion explained and cumulative proportion explained. As seen from Table 4, 91.7% of the variance is explained by a two-dimensional solution. Only 8.3% of the explained variance can be increased by moving from two to three dimensions. The contribution of severity level of usability problems and the heuristics to the inertia of each dimension is shown in Table 5. As the two-dimensional solution is balancing the explanation rate of variance and interpretability, it is selected as the most appropriate dimensionality for further analysis. The perceptual map of the two-dimensional solution of severity level of usability problems and usability heuristics is shown in Fig. 1. The horizontal dimension is strongly influenced by the heuristics-help and documentation (H10) on one end, and aesthetic and minimalist design (H9) and pleasurable and respectful interaction with the user (H12) on the other. Therefore, this dimension differentiates the severity level of usability problems that are help oriented from those that are more aesthetic oriented. The vertical dimension is mainly constituted of the usability heuristics- recognition rather than recall (H7) and help/documentation (H10). Therefore, this dimension primarily differentiates the severity level of usability problems that are more information/documentation oriented from those that are low information/documentation oriented. As shown in Fig. 1, cosmetic problems have close associations with the heuristic- H7. Minor usability problems are related with the heuristics- H12 and H9. On the other hand, it is not easy to understand which heuristics are closely associated with major usability problems and usability catastrophe. Therefore, although some relationships between the heuristics and the severity level of usability problems are revealed with the correspondence analysis, some of them are not clearly shown; so cluster analysis is used as the complementary technique to the correspondence analysis.
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Table 4 The dimensions and its proportion explained. Dimension
Inertia
Proportion explained (%)
Cumulative proportion explained (%)
1 2 3 Total
0.075 0.019 0.007 0.101
71.9 19.9 8.3 100.0
71.9 91.7 100.0 100.0
Table 5 The contribution of severity level of usability problems and usability heuristics to the inertia of each dimension. Severity level of usability problems and usability heuristics
Marginal profile
Dimension 1
Dimension 2
S1: Cosmetic problem only S2: Minor usability problem S3: Major usability problem S4: Usability catastrophe H1: Visibility of system status H2: Match between system and the real world H3: User control and freedom H4: Consistency and standards H5: Help users recognize, diagnose, and recover from errors H6: Error prevention H7: Recognition rather than recall H8: Flexibility and minimalist design H9: Aesthetic and minimalist design H10: Help and documentation H11: Skills H12: Pleasurable and respectful interaction with the user
0.107 0.251 0.441 0.201 0.153 0.121 0.028 0.174 0.056 0.035 0.226 0.007 0.096 0.015 0.017 0.071
0.137 0.508 0.244 0.112 0.031 0.056 0.102 0.000 0.113 0.132 0.268 0.026 0.114 0.080 0.009 0.069
0.664 0.212 0.025 0.099 0.000 0.026 0.018 0.052 0.016 0.018 0.250 0.000 0.368 0.017 0.026 0.209
Fig. 1. The two-dimensional perceptual map of severity level of usability problems and usability heuristics.
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4.4. Cluster analysis Fig. 2 shows the proximity of the severity level of usability problems to the usability heuristics. As seen from Fig. 2, usability problems that are counted as catastrophic seem to have a close proximity to the heuristic- H11. Furthermore, the proximity of major usability problems to the heuristics- H2, H1, and H4 implies that major usability problems are related to these heuristics. Therefore, the design of a banking software system with regard to these heuristics may prevent the occurrence of catastrophic problems and major usability problems. In addition, cosmetic problems are found to be related with the heuristics-H7, and minor usability problems seem to have optimal proximity to the heuristics- H12 and H9. Furthermore, as it can be seen from Fig. 2, the severity level of major usability problem, its related usability heuristics (H2, H1, H4), usability catastrophe and its related usability heuristic (H11) are related to each other. This implies that conformity to the heuristicsH2, H1, and H4 may also decrease the problems that are accounted as usability catastrophe. Furthermore, enhancements in the heuristic- H11 may also reduce the problems that are accounted as major usability problems. In addition to these findings, the heuristics-H10, H8, H5, H6 and H3 are related with the problems that are considered as catastrophic and major.
4.5. Development of usability guideline for banking software Based on the findings from the analysis, a usability guideline can be proposed to software designers that can be used in the design process of usable banking software. For each severity level of usability problems, the heuristics and their corresponding criteria obtained from cluster analysis are listed in descending order in terms of their total explanation ratings. For example, the heuristic H2 has a close proximity to the major usability problems. Therefore, the criteria of the heuristic H2 are taken to the guideline list if any of them explain major usability problems. In the criteria list of each heuristics, the ones early in the list have a higher total explanation rating and importance in the banking software design than the others. In addition, the cluster analysis reveals that the heuristics H10, H8, H5, H6 and H3 are found to have positive effects in solving the problems that are considered as catastrophic and major. That is why; these heuristics and their corresponding criteria are placed
after the severity levels of S3 and S4. Table 6 shows the usability heuristics, their criteria and severity level of usability problems. As it can be seen from the table, the proposed guideline allows a detailed list of usability heuristics and criteria for each severity level of the usability problems. This guideline can be used at the beginning of the design process, designers and project managers may give more importance to the heuristics related with the following usability problem categories: Usability catastrophe and then major usability problems. This will reduce the rework time at later stages of software development. 5. Conclusion & discussion The aim of the current study is to propose a usability guideline that system developers may use in banking software design. This guideline highlights the critical points in the design of software to prevent banking system catastrophe. In the analysis, actual usability problems from three banking software development projects are considered as potential problems that users may experience during system usage. These problems are grouped according to their severity by three experts. Then, each of the usability criteria from 13 heuristics are rated for how well it explains the usability problems in order to reveal the interaction between the severity level of usability problems and the usability heuristics. As a result, in the usability guideline, a set of heuristics and their criteria are categorized in terms of their effect in solving usability problems that are grouped within the range of usability catastrophe to cosmetic problems. The current study reveals that the usability heuristics- skills (H11) is related with the usability problems that are considered to be usability catastrophe, and the heuristics- match between the system and the real world (H2), visibility of system status (H1), consistency and standards (H4) are found to be related with major usability problems. In addition to these heuristics, enhancements to the usability heuristics- user control and freedom (H3), error prevention (H6), help users recognize, diagnose and recover from errors (H5), flexibility and minimalist design (H8), and help and documentation (H10) may also prevent the problems that are considered to be both catastrophic and major usability problems. In the design process of banking software, system designers may give higher priority to these heuristics in order to prevent usability problems that have to be fixed before the release of banking
H3: User control and freedom H6: Error prevention H5: Help users recognize, diagnose and recover from errors H8: Flexibility and minimalist design H10: Help and documentation S4: Usability catastrophe H11: Skills S3: Major usability problem H2: Match between system and the real world H1: Visibility of system status H4: Consistency and standards H9: Aesthetic and minimalist design H12: Pleasurable and respectful interaction with the user S2: Minor usability problem S1: Cosmetic problem only H7: Recognition rather than recall Fig. 2. Dendrogram of severity level of usability problems and usability heuristics.
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Table 6 A guideline for banking software. Severity level of usability problems
Usability heuristics and their criteria (Muller et al., 1998; Pierotti, 1995)
S4: Usability catastrophe
H11: Skills Are window operations easy to learn and use? If the system has deep (multilevel) menus, do users have the option of typing ahead? H2: Match between system and the real world If shape is used as a visual cue, does it match cultural conventions? Are field level prompts provided for data entry screens? Do related and interdependent fields appear on the same screen? Do the selected colors correspond to common expectations about color codes? Are icons concrete and familiar? When prompts imply a necessary action, are the words in the message consistent with that action? Do menu choices fit logically into categories that have readily understood meanings? Are menu choices ordered in the most logical way, given the user, the item names, and the task variables? H1: Visibility of system status Does the system provide visibility: that is, by looking, can the user tell the state of the system and the alternatives for action? Is the current status of icon clearly indicated? Is there visual feedback in menus or dialog boxes about which choices are selectable? Is the menu naming terminology consistent with the user's task domain? Does every display begin with a title or header that describes screen contents? Is there a consistent icon design scheme and stylistic treatment across the system? Is a single, selected icon clearly visible when surrounded by unselected icons? H4: Consistency and standards Have industry or company formatting standards been followed consistently in all screens within a system? Are icons labeled? Are there salient visual cues to identify the active window? Are field labels consistent from one data entry screen to another? Do abbreviations follow a simple primary rule and, if necessary, a simple secondary rule for abbreviations that otherwise would be duplicates? H10: Help and documentation Are data entry screens and dialog boxes supported by navigation and completion instructions? Is the help function visible; for example, a key labeled HELP or a special menu? If menu choices are ambiguous, does the system provide additional explanatory information when an item is selected? H8: Flexibility and minimalist design Does the system offer “find next” and “find previous shortcuts” for database search? If the system uses a type-ahead strategy, do the menu items have mnemonic codes? If menu lists are short (seven items or fewer), can users select an item by moving the cursor? H5: Help users recognize, diagnose, and recover from errors Do error messages inform the user of the error's severity? Do error messages provide appropriate semantic information? Do error messages indicate what action the user needs to take to correct the error? Do all error messages in the system use consistent grammatical style, form, terminology and abbreviations? H6: Error prevention Does the system prevent users from making errors whenever possible? Are menu choices logical, distinctive, and mutually exclusive? If the database includes groups of data, can users enter more than one group on a single screen? Is the menu choice name on a higher-level menu used as the menu title of the lower-level menu? H3: User control and freedom In systems that use overlapping windows, is it easy for users to switch between windows? Can users easily reverse their actions? Is there an “undo” function at the level of a single action, a data entry, and a complete group of actions? Are menus broad (many items on a menu) rather than deep (many menu levels)? H12: Pleasurable and respectful interaction with the user Has color been used specifically to draw attention, communicate organization, indicate status changes, and establish relationships? Has color been used with discretion? H9: Aesthetic and minimalist design Does each icon stand out from its background? Have large objects, bold lines, and simple areas been used to distinguish icons? Is only (and all) information essential to decision making displayed on the screen? Are all icons in a set visually and conceptually distinct? Are field labels brief, familiar, and descriptive? H7: Recognition rather than recall Have zones been separated by spaces, lines, color, letters, bold titles, rules lines, or shaded areas? Are borders used to identify meaningful groups? Are size, boldface, underlying, color, shading, or typography used to show relative quantity or importance of different screen items? Is color coding consistent throughout the system?
S3: Major usability problems
For S4: Usability Catastrophe and S3: Major Usability problems
S2: Minor usability problem
S1: Cosmetic problem only
software. This list can also be used as a guideline in the early stages of software development in order to decrease the required rework at the later stages of development. Similar to our findings, Bass and
John (2003) define usability recommendations that have a high impact on architectural design. The recommendations defined in that study overlap in some aspects with the findings of our study.
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For example, the current study's heuristics- help users recognize, diagnose and recover from errors (H5) and error prevention (H6) are parallel to Bass and John's (2003) recommendations including; ‘maintaining device independence and recovering from failure’ and ‘using applications concurrently and checking for correctness’, respectively. In addition, the current study's criteria regarding to the heuristic- skills (H11) is parallel to Bass and John's (2003) recommendations including; ‘aggregating data or commands’, ‘leveraging human knowledge’ and ‘observing human needs and capabilities’. Software developers can design the software according to the expectations and needs of the potential users in order to improve the learning process and use. Moreover, the usability recommendations of Bass and John; ‘reusing information’, ‘supporting international use’, ‘modifying interfaces’ and ‘supporting personalization’ are related with the heuristic of the current studyuser control and freedom (H3). Furthermore, Bass and John have mentioned that ‘providing good help’ is one of the potential usability benefits for system users. These overlaps ensure the current study's validity that having a list of heuristics and their important criteria is important in guiding system designers in building system software. However, as Carjaval, Moreno, Sanchez-Segura, and Seffah (2013) mention incorporating usability factors in the software design is not straightforward. Software developers may have training on how to integrate these usability factors in the design of software. The second finding of the current study is that minor usability problems are associated with the heuristics; pleasurable and respectful interaction with the user (H12) and aesthetic and minimalist design (H9). Furthermore, cosmetic problems are found to be related with the heuristic- recognition rather than recall (H7). The examination of the criteria show that the heuristics are related with the user interface part of the system, such as color of the background, color changes with the status changes or the aesthetic design of the icons. These criteria are found to have less importance with respect to the criteria of major usability problems and usability catastrophe. Similar to our findings Juristo et al. (2007) state that user interface related issues require slight modifications and are only “confined to the interface component or subsystem, having no impact on system core”. Furthermore, the aesthetic related issues may be influential in the initial visits but content related issues become more important for the subsequent usages (Sutcliffe, 2001). Finally, the results reveal that usability problems encountered during three usability projects do not map on any of the criteria of the heuristic, privacy. This implies that financial institutions are aware of the importance of privacy in financial software, so they give importance to the protection of customers' information and are sensitive about not sharing any private information. Similar to our findings, Knutson (2007) states that customer trust to software is important in the decision to use e-commerce systems, and privacy can be a strong option for companies to become a market differentiator. 5.1. Managerial implications Software design and usability are related to each other and should be dealt with together at the design process. On the other hand, generally system designers do not pay attention to usability issues or have a lack of expertise about developing usable systems so they only focus on the functionalities of the system. Therefore, companies may organize usability training for the software designers during the project lifecycle about the importance of usability and how to integrate the usability aspects into the software design. In the first place, the criteria that are related with the severity level of major usability problems and usability catastrophe
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