**An Integrated Survey Designs in Collecting Data towards a Sustainable Knowledge and Information** # Siraj Osman Omer # Experimental Design and Analysis Unit, # Agricultural Research Corporation P.O. Box 126, Wad Medani, Sudan, E-mail: **Abstract** Surveys are essential for collecting data that contribute to innovation and sustainability within the framework of statistical data-based knowledge. There is a need for integrated survey design to learn and apply how to measure, monitor, and compare the performance of country’s sustainability. The objective of the paper was to develop integrated survey designs within the framework of sustainable information and statistical system towards scientific management, sustainable knowledge, and data analysis in a sustainable manner. This study approach is an integrated survey design for enhancing the potential of sustainable information system (SIS) by combining it with the statistical information system (STIS). Moreover, this methodology is framework of integrated survey design in SIS development. The outcome of the connectional of integrated design surveys framework will be implemented in the statistical information system (STIS), ensuring that the resulting statistics are effective enough to support a sustainable information system (SIS). Overall, integrated survey design will support the development of economic and social innovation policies. The proposed integrated survey design is considered as necessary tools to collect data from different sources across federal organizations, institutions, and the private sector in a single location through the Sustainable Information System. The study suggests the creation of a platform for integrated survey design on sustainability knowledge, innovation, and statistical information. The future study will address survey design software and big data in a machine learning approach. Key words: Sustainable knowledge, Survey Design, Innovation Statistics, Metadata **1. Introduction** Sustainability is defined as the ability of any business or institution to respond to their short-term and future risks to avoid loss. Furthermore, sustainability means achieving sustainable economic growth by innovative ways (Adams et al, 2016). A lot of work has been done on sustainable management but current practice in knowledge management is organizing, creating, sharing, and managing information (Alshubaily and Altameem, 2017). Within conception of sustainability and big data availability, therefore, its necessary to develop an integrated survey design as well. In our world today, an integrated survey design and data analytics offer a less expensive and time for producing a variety of data collection and statistical information in every field (Amaya et al., 2020). There has been no comprehensive review of recent literature in the contexts of using survey designs for the collection of sustainable knowledge and information. There are a few related studies, such as Singh et al. (2018) addressed the role of big data analysis in building sustainable capacity, considering internal processes that constitute sustainable capacity. Recent previous studies of integrated survey design focused only on sampling issues ranging from concepts and theoretical works mixed methodologies, including quantitative and qualitative. For example, Wiśniowski et al. (2020) studied an integrating probability and nonprobability samples for survey inference Davern et al. (2019) have created improved survey data, products using linked administrative survey data. For the evaluation and promotion of the macroeconomic growth system, the data and information are requirements for measuring the parameter of economic growth (Harsanto and Permana, 2019). Therefore, the economic growth parameters linked to scientific and technological innovation have been increased to meet statistical analysis. However, the integrated survey design will help in sustainable development of the growth parameters through the multiplier effect of data analysis efficiency (Wang et al., 2018). Recent survey designs have been applied to collect multifunctional data and for development and implementation of policies and strategies based on science and technology (S\&T) indicators in the national development process for understanding sustainable practice, information and acquired knowledge (Roslan et al. 2011). The advantage of a sustainable information system (SIS) in the context of statistical information it will supports reflection on the formulation of knowledge mining, knowledge analysis and knowledge implementation. A survey is the comprehensive systematic method to collect data from different aspects. Surveys are primarily used to gather quantitative information about the perceptions and opinions of a sample of individuals that adequately represent the study of the target population, such as: - Method: Clarifying the purpose, evaluating resources, and deciding methods. - Design: Writing the questionnaire, testing/revising the questionnaire, and preparing the sample. - Tools: Collects, processes, analyzes, interprets, and disseminates information. - A survey made it possible to improve the product or process, then marketing can be considered as an aspect of the economic impact of any project that will lead to: - Collect information, but it is not available through other sources. - Selection of the sample: in this case, a non-biased representation of the population of interest. Standardize the measure, gather information from each respondent. - Analytical requirements: using survey data to supplement existing data fro secondary sources (Omer, 2012). In the United Kingdom, the Community Innovation Survey (CIS) supplements other innovation indicators by providing a regular overview of innovation inputs and outputs and their innovation efforts across industries and enterprises (Battisti and Stoneman, 2010). A survey design is the core of collecting data about knowledge and technology transfer activities between universities and companies for determining the innovation performance of the institutions (Minguillo et al., 2015). An integrated survey designs are an essential tool for monitoring the economic development based on descriptive statistics, statistical estimations, and numeric indicators (Holdsworth et al., 2018). Thus, integrated survey designs can lead to econometric results showing that institutions significantly improve, and achieve the success and sustainable growth of economics (Foray and Werther, 2020). Survey design is an essential tool for collecting and analyzing data for all activities that contribute to the creation of innovation and knowledge (Gallant, 2013). **2. The Justification and Objective of the Study** The justification of this study goes to the importance of applying statistical tools in sustainable knowledge management and driven data collection for statistical analysis to measure a performance indicator and the actual assessment of economic data and utilization of the results in the right way (Omer, 2012). Due to increasing world-wide information, existence of big data and machine learning tools, it is necessary for a better build an integrated survey design for knowledge management and to evaluate the impact of technological developments in the economy and the application of organizational knowledge to grow (Donoho, 2017). Creating an integrated survey design will provide us with structured and sustainable data that leads to possibility of access the knowledge and rich information in the process of decision making and will pave the establishment of integrated survey systems through modern technological systems (Holdsworth et al., 2018) An integrated survey designs is a new opportunity for developing real-time data collection tools using web survey, computer-assisted interviewing, computer-administered survey modes, and computerized sample management systems. Integrated survey design has significant role to collect data from multiple sources and leverage more information to better achieve the objectives of the decision-making process (Kaisera et al., 2020). The objective of the paper was to develop integrated survey designs within the framework of sustainable information and statistical system towards scientific management of sustainable knowledge, information, data management and data analysis in a sustainable manner. This means to draw up an integrated survey design to promote collection of data and information with a view to access a sustainable knowledge management to enhance social and economic sustainability. The remainder of this paper is organized as follows: examines integrated survey designs for the respective, introduces the Sustainable Information System (SIS), the role of statistical information in SIS and their statistics innovation. Discussion metadata approach and finally concludes and summarizes our results. **3. Methodology** In this study, the research methodology is based on framework approach. The framework approach is gaining as a means of collection of data to be used to manage the sustainable information system by combine it with statistical information system (STIS) and undertake analysis systematically. This paper builds on the new approach of a comprehensive integrated survey design, synthesis of the available literature on knowledge management and survey design in a sustainable information system. The author's perspective in this paper to create a survey design to collect data towards a sustainable knowledge and information. Therefore, the methodology will focus on three main topics. Frist: the conceptual framework for survey design. Fig. 1 shows a schematic representation of this integrated survey design framework. In this section, we will focus on the actual framework of survey design, research survey and innovation survey. Secondly: Sustainable information system (SIS), in this section we define the role of statistical information in SIS. Fig. 2 shows the statistical information system (STIS), innovation, and survey organization. Third: statistical information System (STIS) and its components such as statistics Innovation, data quality and metadata by providing illustrations supporting for ideas Fig 3 shows the Metadata components. **3.1. Approach: The conceptual framework for surveys designs** This conceptual framework is a characterization of processes surveys designs which have been identified and assessed through three main components of survey designs are survey research, sustainable information, and survey methods to product sustainable knowledge system as part of the statistical information system (STIS). This framework also defines survey designs profile, survey distribution methods, innovation survey link between the sustainable information and survey application, which could be adopted by the survey designs for collecting data and managing information. Figure1: Illustrated an Integrated survey design in relation to the sustainable information system (SIS) addressing the integration of research, method, and innovation survey in SIS. Figure 1 demonstrates the link between survey research and survey method. The survey design also considers innovation survey for collecting and processing data that need to be included in the overall framework. Survey method and information management are required for operational data, data exchange and database administration. A specialized area of survey design is the role of the data management of sustainable information or more master data. In general, this conceptual framework is intended to provide insights into data collection and data management of statistical information in sustainable developments. **3.1.1 Survey Design** In this paper, the survey design will consider based approach to data collocation, managing data, statistical information from research survey uses of an appropriate survey design that the specific research question should determine the best design for a study or project (Flynn, et al., 2017). Survey design may be implemented to collect data from the bottom, middle and top scales of the SIS. The survey design must organize through innovation survey on how to collect information, information creation, acquisition and sharing knowledge aspects of sustainability (Lim et al., 2017). Innovation survey is a methodology for making sustainable knowledge more effectively for management about how the data were utilized for in new ways to gain more knowledge and applying them in research and development. (Matyas and Kamar Gianni, 2019). The most used frameworks for making statistical information from data based on survey designs. Planning a survey design project describes the steps that begins with the statement of the survey objectives and ends with the interpretation of the survey results. The survey was designed specifically (Table1) to: - Table1: Survey designs profile | **No** | **Project** | **Descriptions** | | ------ | ---------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------ | | **1** | Survey Goals | Creating, visualization and verifying the [survey goals](https://explorable.com/defining-survey-goals) | | **2** | Sampling | [Determining the sample from population](https://explorable.com/methods-of-survey-sampling) | | **3** | Survey Methodology | Specifying a [survey method](https://explorable.com/selecting-the-survey-method) | | **4** | Questionnaire Design | [Creating a questionnaire or survey](https://explorable.com/questionnaire-layout) | | **5** | Pilot Survey | Conducting a [pilot survey](https://explorable.com/pilot-survey) | | **6** | Revision of Survey | Revising the questionnaire | | **7** | Execute Survey | Executing the full survey | | **8** | Data analysis | Analyzing and [interpreting](https://explorable.com/conclusion-of-a-survey) the data gathered | | **9** | Data [Communicating](https://explorable.com/presenting-survey-results) | [communicating](https://explorable.com/presenting-survey-results) The results | The survey design approach will make it possible to understand every detail of the research survey and methodology from the statistical information system, and this approach also contributes to creating a framework for sustainable knowledge (Lohr, 2017). **3.1.2 Survey Research** Survey research is a means to collect information and data from a wide range of people and from all form’s environments (Cirera and Muzi, 2020). Thus, survey research can consider a suitable parameter for survey research management (Marandel et al., 2018). Survey research is part of the statistical activities, which includes a survey and recognition of the statistical needs of the various subdivisions, i.e., designing, formulate and implement the necessary statistical information establishing a system of statistics and promoting sustainable knowledge system (Lohr, 2017). The main activities in survey research are as follows: - Conduct surveys and recognize statistical needs. - Present the best solutions for promoting the quality of statistical production. - Researching the use of new statistical sampling methodologies. - Research on using new statistical sampling methods. - Conduct, operations, and data quality The survey uses a combination of methods of data collection to provide maximum flexibility for the respondent via questionnaires or Computer-assisted methods (Ottawa, 2014). Therefore, surveys will provide an efficient and flexible means of collecting for a wide range of information from large numbers of respondents. The most recent survey distribution methods are forward to replace legacy methods to modern methods, due to changes in technology and consumer behavior that allow for faster, better results and more opportunities to reach audience target (Datta et al., 2020). Survey distribution methods are given Table 2. These methods been shown in market research professionally because of advantage of these new methods. Table2: Survey distribution methods

Classical methods

(Legacy Survey Distribution Methods)

New methods

(Survey Distribution Methods)
Telephone Surveys Random Device Engagement
In-Person Interviews Assisted Crowd Sourcing
Google Search Ads Share your survey on your website or blog
Panel Sampling Hire a market research agency
----------------- Send surveys via email
**3.1.3 Innovation Survey** An innovation is a new or improved product or process that differs significantly from previous products or processes and that has been made available to potential users. Innovation surveys is smart way to collect data on a range of activities for example, in European countries, regular innovation surveys are conducted with fully developed innovation survey systems to measure high-tech innovation activities (Arundel et al., 2019). Furthermore, different types of innovation surveys might rely on different techniques. Therefore, having on the creation, statistical system will make innovation survey is applicable and provides a wide range of information related to innovation activity among enterprises which includes information on innovation activities and the impact of innovation on businesses (Eder, 2018). The innovation modes of survey administration will be considered as survey distribution methods, survey of internet or survey of electronic commerce and technology that provides relevancy, accuracy, completeness, consistency, timeliness survey information. Therefore, we need for innovation survey for developing successful, sustainability development, innovative organizational strategies, to meet regulatory standards and access to formal sources of knowledge as drivers of sustainable information system for measuring and estimations of social and economics indictors (Maruf, 2017). **3.2. Sustainable Information system (SIS)** In this study, management of sustainability knowledge can be obtained through external knowledge sources based on statistical survey methods (Harsanto and Permana, 2019). In regard of integrated survey designs we need sustainability information by identifying a minimum set of core data; into the national sustainable information system (SIS) and constantly derived and streamed from statistical information system (STIS) through governance and statistical capacity (Mikalauskiene and atkociuniene, 2019). Moreover, in respect of information creation from sustainable information system has significant impact of data management and utilization of statistics for measuring indicators on social and economics sustainability (Lim et al., 2017). Therefore, application of organizational knowledge within the SIS will developing surveying activities in sustainability information to produce surveys in many countries, implemented by community innovation survey (Abbas and Sağsan, 2019). The building of a survey design needs to populate statistics, information and to construct indicators that requires concepts with definitions acceptable to the international community that govern both the measurement and the interpretation of the data (Jensen et al., 2012). SIS is a framework of understanding sustainability knowledge through organizing knowledge and analyzing information, positive value creation, as sustainable development that is linked to STIS (Acquier et al., 2019). Therefore, we consider SIS as part of a statistical information system (STIS) which serves its purposes by providing its users with sustainable information system. Thus, statistical information describes and understand the neutral of sustainable knowledge and its subsystems and components (Wang, 2019). **3.2.1 Role of Statistics in the Sustainable Information System (SIS)** The traditional role of a statistician in science and technology was to act as a consultant, or to train some workers in certain tools such as statistical analysis, the use of statistical application software and the design of experiments, etc. Statisticians must play a part in all scientific and technological innovations (Donoho, 2017). There is an extensive statistical method for the development of sustainable information system. - To which statistics, science can influence the innovation system. - To a large extent, the approaches adopted by various statistical inferences in the direction of innovation systems depending on statistical application. These approaches have ranged from descriptive statistics, statistical inference, data mining, predictive modeling, and their model to a much more simulation models employed in sustainable knowledge. For example, simulation models can be used in the development of systems operating policies and in research aimed at developing systems knowledge and decision-makers using information obtained from the survey design (Sargent et al., 2013). Statistical innovation has adopted that fall in between advances models, software, Bayesian statistics and other advanced statistical methods. Thus, from the standpoint of a statistical innovation process (Sivaparthipan et al., 2013). There are several statistical approaches to directing and shaping the innovation-led development processes **3.3. Statistical Information System (STIS)** Statistical Information System (STIS) is developing system to support statistical activities such as assembling the information, storing the information and statistical evaluation, and to improve the efficiency of metadata collection, validation, processing, storage, and dissemination, and to improve quality, statistical publication, and to enhance the accessibility, and to visibility of the statistical system outputs (Sivaparthipa et al., 2018). Therefore, the STIS includes various steps such as aggregating the information, storing the information, statistical evaluation and processing the evaluated documentation. Figure 2 presented the comprehensive framework of the (STIS) scheme. Fig 2: Statistical information system (STIS), innovation, and survey organization Figure 2 shows the overview of the statistical information system (STIS), innovation, and survey organization interaction. STIS is considered here as a guide for planning, monitoring, and evaluation of insights, decisions of the system. Its presents types of formal and informal combinations of the various stages of the data, research, and their relationship to aspects of evidence (Alshubaily and Altameem, 2017). **3.3.1. Statistics Innovation** Innovation and statistics have appeared in the last 30 years for industrial surveys, such as that of McCraw-Hill in the US (Azimi et al., 2020). Notable statistical innovations were motivated by the so-called lot-consistency study that is used to demonstrate that the manufacturing process is reproducible. As far as the need for innovation is concerned, the term statistical means modern statistical scientific method, smart data collection tools and the making of statistical inferences. Statistical innovation is the art of statistics used in scientific and technological activities (Heyse et al., 2016). In this definition, data collection is taken to encompass both the design and execution of data collection activities as well as data analytics. The art of statistics innovation additionally includes activities that attempt to: - Designing statistics, surveys that recognize the many constraints brought about by the sustainability of their society and its innovation system. - Make changes in statistical modelling (when the model is taken to include not only content but also statistical methods and procedures of evaluation. As a result, the implementation of the survey will increase operational research and continually innovate for new discoveries method such as used by artificial intelligence and machine learning that will enhance our enduring knowledge and create innovation development of industries surveys (Heyse, et al., 2016). **3.3.2. Data Quality** The quality is a developing of data, analysis, and indicators of the globalization of sustainable development that leads to better understanding of the emerging global economy (Roger et al., 2012). This means improving the methodology for smart data collection tools using integrated survey design. Data quality includes research on survey design or quality, including studies on sampling error control, coverage, responses, data coherence with associated surveys (Lohr, 2017). Regarding the use of integrated survey design for collecting information on a sustained basis will make it to be more effective and productive. The quality of survey information should include reliability, quality indicators and confidentiality (Lyberg, 2012). Furthermore, quality should be achieved at three different levels:- - Product quality: The survey results given to the researcher should satisfy the type of quality standards. - Process quality: Quality control can be used and to be sure that the process by which the survey is conducted is of high quality, as in high standards for hiring and supervising interviewers. - Organization quality: The management of the survey organization should be of high quality, such as having strong leadership, good customer relations, and high staff satisfaction. According, data quality include cost and time, because are two factors that need to be accounted for to achieve high quality in the survey. The creation of a high-quality survey management may benefit many survey designs; As a result, the costs of producing and maintaining high-quality standards are beneficial to an investigative organization in attracting more surveys (Deriu et al., 2020). **3.3.3. Metadata** Figure 3: Metadata components The new approach of survey development called a Metadata survey design. The benefits of using a Metadata approach are to less unnecessary work, better, faster data documentation, and measurable of key survey components, increase data potential, greater research integrity and increasingly being used electronically. Most of Metadata components include data collection, data delivery and data analysis and codebooks such as specifications, protocols, and other documents and finally Metadata exchange. Metadata can be operated by many different Driven Components for example Questionnaires: Cloud Foundry API(CAPI), Audio Computer-Assisted. Self-Interview (ACASI), Personal Desktop App (PDA) and Website Metadata (WEB) (Myers, et al. 2019). Metadata is a tool for integration heterogeneous data sets and its support, indemnification, description and location of network, electronic recourse, which represent high level information describing, the content, extents, quality, structure, and accessibility of a specific data set (Jeremy, 2009). The Metadata describes three components include content representation (basic details about, who, where and key linkage), database description (data set. Model, platform, and purpose, etc.), and database covering and availability (spatial, temporal, limitations, access, etc. (Joesph, 2009). The Metadata survey design will produce an adequate and efficient return of precision for all important survey estimates by - Review of innovation system - Problem identification - Development of survey programmer - Identify science and technology indicators. - Data collection and analysis **4. Discussions** A major contribution of the paper has been to make integrated survey design more efficient for data collection and information. This study clarified that how integrated survey design is work and the role of applied statistics in knowledge management, and more importantly the use of statistical information system (STIS) in the acquisition of sustainable information. There has been increasing interest in survey design in managing appropriate knowledge management. In recent years, There is an emerging in the use of statistical techniques in all fields, especially data sciences as well survey design Integrated survey design to promote sustainable knowledge management and provide an innovative effort to enhance statistical information that impacts all areas of the national sustainable information system (SIS) (Cirera and Muzi, 2020). Therefore, it is especially important to establish SIS and disseminate findings from key surveys based on integrated and sustainable knowledge. The SIS is useful and realistic across science and technology sectors for example the agricultural activities, healthcare, business, investment, marketing, economic, financial, environmental, and industrial production (Datta et al., 2020). The advantages of used integrated survey design will produce data collection based on innovation survey methods. Therefore, scientific approach such as innovation statistics, statistical thinking, and statistical engineering is necessary to improve the STIS. For example, statistical engineering and survey designs are unique from machine learning (ML) as innovation statistics methods because of ML provides methods to increase survey operations by providing complementary approach through integrated survey design (Jensen et al., 2012). Integrated survey design outcome has significant impact on statistical innovation and actual practice using data sciences for action. Furthermore, we must consider the SIS as important for yield a developments sustainability indicator (Abbas and Sağsan, 2019). Nowadays, Technologies speed up survey research, such as telephones and computer assisted telephone interviewing (CATI) software. There are many facilities used for Survey Research in computer hardware technology, Web browser capabilities, iPads/tablets, iPhones/smartphones, and other devices that transforming the scope of survey research. ISD allows to collect information more precisely because of use modern surveys technologies and innovations to collect data on a timely and accurate basis (Dziallas and Blind, 2018). Concurrently, the need for integrated survey designs towards a sustainable knowledge and information towards issues such as sustainability science, equitable economic growth, and development (Myers, et al. 2019). **Conclusion** This paper examined, integrated survey designs from the perspective of enduring sustainable information system and statistical information system (STIS) needed for survey research, survey method, innovation survey, data quality and the role of statistics in sustainable information system. An integrated survey design is useful for collecting rich data from different resources and provides statistical information for the interpretation of results that will contribute to the economic development of the knowledge system and sustainable development indicators. It also provides an overview of innovation statistics and survey metadata, which are among the new technologies being developed. Overall, integrated survey design is essential tool for collecting sustainable information that supports the development of economic and social innovation policies. This paper mainly raises the awareness of the need for integrated survey design at all levels of data collection. The study suggests the creation of a platform for integrated survey design on sustainability knowledge, innovation, and statistics. The future study will address survey design software and big data in a machine learning approach. **References** Alshubaily, N.F., & Altameem, A.A. (2017). The Role of strategic information systems (SIS) in supporting and achieving the competitive advantages (CA): An empirical study on saudi banking sector. *International Journal of Advanced Computer Science and Applications (IJACSA),* 8 (7), 128. Adams, R., Jeanrenaud, S., Bessant, J., Denyer, D., & Overy, P. (2016).  Sustainability-oriented Innovation: *A Systematic Review. 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