Human Information Interaction

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Background and Aim: Information Architecture is an organizational meta-method that adopts a tactical approach to the specialized information of the organization using an integrated framework and empowers the management of organizational resources by acquiring and upgrading new information technologies. Therefore, it is essential to be part of the current processes of the organization and the insurance industry.
Method: Mixed research method is applied, in which  evaluation,   observation and researcher experience, checklist, and analytical review of the website is analyzed.   Population is the Central Insurance of the Islamic Republic of Iran website, in which the four components of Information Architecture including organization, labeling, navigation, and search systems are examined based on context, content and user.
Findings: Conferring the research findings, the checklist score of the Insurance website for organization system is 17 points. Furthermore, the rates of positive answers to the total number of questions are 45.94%, 54.38%, 53.84% and 45.65% for organization, labeling, navigation, and search systems respectively. The results indicate that the Insurance website has a moderate to weak position from the organization and search point of view and has a moderate position in the labeling and navigation system.
Conclusion: Information Architecture provides the foundation of the information systems architecture from the underlying level to the interface level for the improvement and excellence of the organization. The checklist used in this research can be useful for evaluating organizational websites and provide added value to the organization, and also can improve the planning and strategic decisions of organization's policy makers.

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Background: The purpose of this study is to evaluate the University of Tehran website based on information architecture indicators to inform, assess the status and quality of the website.
Methodology: The research method is applied descriptive. Checklist was used to analyze the website in terms of organization, labeling, navigation, and search systems.
Findings: The results of the checklists showed that the University of Tehran website earned 20 points out of 37 in the organization, 37 points out of 57 in the labeling, 53 points out of 78 in the navigation, and 14 points out of 46 in the search, which imply a poor status in search system, an average status in organization system, and a good status in labeling and navigation systems. According to the obtained results, the search system of the University of Tehran website can be redesigned, its organization system can be reviewed, and other mentioned systems can be improved. In general, in order to increase the efficiency of websites, it is necessary to consider the principles of information architecture in their design. Moreover, this research can be a suitable source for future website policies by identifying the strengths and weaknesses of the website.
Conclusion: Evaluating the information architecture of a website is a new field that, while important, has received less attention. This research tries to show the applicability of the method for use in similar cases by presenting a systematic evaluation in a case study for the University of Tehran website while introducing the strengths and weaknesses of this website.
 

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• Form a hierarchy
• For the elements of each level of the hierarchy, perform pairwise comparisons according to the expert opinion.
• Calculate the inconsistency rate for each matrix of pairwise comparisons.
• If the inconsistency rate for the matrix was more than 0.1, review the values of the paired comparisons of that matrix.
• Calculate the relative weight of the elements corresponding to each matrix of pairwise comparisons.
• The product of the relative weight of each criterion and the relative weight of all elements related to it, at all higher levels of the hierarchy, gives the final weight of that criterion.
• Now, with the help of the weights calculated for each criterion and using the TOPSIS method, the alternatives will be ranked. The TOPSIS method is based on choosing the alternative that has the smallest distance from the positive ideal solution and the largest distance from the negative ideal solution. In this method, it is assumed that there are m alternatives (organizations) that must be evaluated based on n criteria. The steps of this method are as follows.
• Evaluate the alternatives with respect to each criterion according to the expert opinion.
• Normalize the decision matrix so that the data are on the same scale.
• Obtain the normalized weighted decision matrix by multiplying each component of the normal matrix by the weight of similar criteria.
• Calculate positive ideal and negative ideal solutions.
• Calculate the distance of each alternative from the ideal positive and ideal negative solutions.
• Obtain the closeness coefficient of the alternatives.
• Rank the alternatives in decreasing order of the closeness coefficient.
• Finally, the proposed evaluation method is used to evaluate data governance in five organizations active in the food industry.
  Findings: According to the results of this research, data governance can be evaluated at the first level from three different dimensions, which are data quality, internal organizational effects, and external organizational effects. Then, at the second level, different criteria are considered for each dimension of data governance. Data quality evaluation criteria include data accuracy, data completeness, data consistency, data availability, data timeliness, and data uniqueness. The data accuracy measure refers to issues such as the percentage of incorrect information, the percentage of the need for manual corrective actions on the data, and the percentage of change in the retrieval of incorrect information after the implementation of data governance. The data completeness criterion seeks to ensure the completeness of various dimensions of information that the organization must use in its business line, and is related to issues such as the percentage of information filled in the required fields, the percentage of usable information, and the percentage of incomplete information. Data consistency refers to ensuring that the data is consistent and aligned with the policies, rules and values set for the data in the business. Data availability seeks to assess the time the business group has access to critical information and data elements.
  Data timeliness refers to the degree to which the data represents reality at a particular point in time.
  Data uniqueness means that no information item is recorded more than once in the data set.
  The second dimension of data governance evaluation focuses on its internal organizational effects,andthe main criteria in this dimension are data governance efficiency, data governance productivity, and business cost savings. These criteria seek to evaluate the level of involvement, participation and impact of data governance in the organization. Among the important things in evaluating the efficiency of data governance are the number of business lines, functional areas, system areas, project teams, and other parts of the organization that have come together to support monitoring and providing resources for data governance, and in addition, categorizing and tracking the status of all issues that fall within the scope of data governance tasks. The data governance productivity considers the impact of data governance in relation to the amount of support and investment in this area, and includes issues such as the amount of reduction in resources required to coordinate members, products and other entities in data systems, the amount of reduction in work required to solve existing data problems, the percentage of projects or initiatives within the organization that have been identified and eliminated as redundant by the data governance program, and the number of redundant systems eliminated in order to create a single definition of customer, product, or other master data. Finally, the cost savings measure reflects the business value of data governance in terms of internal organizational impacts.
  The third dimension of data governance evaluation considers the effects that go beyond the internal boundaries of the organization and affect the entire business of the organization. The criteria considered in this dimension include obtaining and improving customer satisfaction, complying with laws and creating business opportunities, which express the main motivations and drivers of the organization to adopt data governance in the current competitive environment. The customer satisfaction criterion measures the fruit of the efforts made to govern and manage data and turn it into a real business value. On the other hand, there are laws and regulations that are defined in relation to data and depending on the type of business at different national, regional and international levels, and failure to comply with them, in addition to monetary fines, will sometimes result in the suspension or even termination of the organization's business. Therefore, the level of compliance with laws is one of the key criteria in the evaluation of data governance from the perspective of external organizational effects. Finally, it is expected that high-quality data along with analysis and reporting systems will lead to informed decisions and data-driven insights and provide new business opportunities for the organization, which is the subject of the last criterion in this dimension.
  Finally, five organizations active in the food industry were examined from the perspective of data governance. According to the information collected from these organizations and using expert opinion, pairwise comparisons were made at different levels of the proposed hierarchy. Then, using AHP, the weight of each dimension and criteria was calculated. According to the results, it can be seen that the external organizational effects is the most important dimension of data governance evaluation in organizations. In addition, customer satisfaction was chosen as the most important evaluation criterion, and compliance with laws and productivity were placed in the next positions. Then, the solutions and the distance of each organization from these solutions were calculated. studied organizations were scored based on the data governance criteria on a scale between zero and ten, and using the TOPSIS method, ideal positive and ideal negative  Afterwards, the closeness coefficient and accordingly the rank of each organization was obtained.The results show that the third organization has the best performance in the field of data governance among other organizations, and
  the second and fourth or ganizations are placed in the next places with a slight difference from each other.
  The fifth organization has a much weaker performance and the first organization is in the last place by a large margin. These results emphasize the applicability of the proposed approach for evaluating data governance and show the steps to perform such an evaluation in a case study in the field of food industry. Such an evaluation in the target organizations can be used as a measure to determine the current state of data governance on the one hand, and on the other hand, it can be used to set goals to reach the desired state in data governance. Moreover, considering the comprehensive and general nature of the proposed approach, it enables its application in other organizations, regardless of their size and type, which is one of the advantages of this approach.
  Conclusion: The volume of data is exploding in the last decade and its complexity is continuously increasing. Moreover, organizations have become more adept at using data, which has created new demands that require different methods to combine, change, store and present information. Leading organizations are finding that traditional solutions for data management are becoming more expensive and unable to truly manage the business. Therefore, organizations need to solve these data problems in another way and by implementing an effective data governance. Data governance, by monitoring data quality and aligning it with business goals, is one of the causes of internal organizational changes, such as increased productivity. It has external impacts like increasing customer satisfaction and creating new business opportunities. Therefore, organizations need to use, implement, and evaluate data governance in their business to maintain competitive advantage and comply with laws and regulations. The present study tried to provide an applicable approach to evaluate data governance in organizations. For this purpose, the dimensions of data governance and different criteria for their evaluation were determined using the literature review and the opinion of experts. Then, a hierarchical structure was proposed to evaluate data governance. This structure considers data governance from the three dimensions of data quality, internal organizational effects and external organizational effects of data governance. In the following, for each of these dimensions and depending on their nature, different criteria were introduced and explained. Then the evaluation method was developed based on the obtained structure and using a hybrid AHP and TOPSIS approach. In the next step, the proposed approach was used in a case study to evaluate data governance in five organizations active in the food industry. This study, while showing the implementation steps of the proposed approach, specifies its applicability and generalizability in other organizations. In addition, the results of this evaluation can help organizations to improve the state of data governance and while ensuring customer satisfaction and compliance with laws in the field of data, provide a platform for organizational excellence and new business opportunities.