Future Information Systems Research Articles

Implementasi Sistem Informasi Pengembangan Kompetensi ASN Masa Depan (SI ABANG TAMPAN) pada Badan Kepegawaian dan Pengembangan Sumber Daya Manusia Kabupaten Bogor

This study aims to analyze the implementation of the Future ASN Competency Development Information System (SIABANG TAMPAN) at the Bogor Regency Human Resources Development and Personnel Agency (BPKSDM). This research uses a descriptive method with a qualitative approach. Data collection was conducted by structured observation, documentation, literature study, and in-depth interviews. The conclusion of this study is that the implementation of the SIABANG TAMPAN application at the BKPSDM of Bogor Regency has gone well, but still needs improvement. The disposition factor has gone quite well, and has a positive influence on the application of SIABANG TAMPAN. Likewise, 3 other factors, namely communication, resources and bureaucratic structure contributed significantly to the successful implementation of SIABANG TAMPAN in Bogor Regency. However, there are several aspects that certainly need to be developed and improved in order to implement SIABANG TAMPAN. These aspects are the information clarity aspect which is a sub-factor of communication factors, the facility aspect in resource factors and the SOP aspect in bureaucratic structure factors.

Orchestrating Digital Resilience: A Clinical IS Study of an Everything-as-a-Service Technology Strategy

ABSTRACT Organisations must build digital resilience to effectively safeguard their business operations against environmental, economic, or socio-political disruptions. One approach involves adaptation – using Information Technology (IT) to design business operations that can withstand and recover from such disruptions. A second approach involves mitigation – using IT to advance environmental, social, or economic objectives that reduce the root causes and likelihood of future disruptions. However, both approaches to building digital resilience are challenging to implement. Our clinical Information Systems (IS) research article examines and reflects on how Beta Bank, an Australian fintech firm, developed an Everything-as-a-Service (EaaS) technology strategy and achieved digital resilience throughout its business operations. We explain how Beta Bank strategically leveraged EaaS to adapt to the significant disruptions caused by the Australian wildfire disaster and the global COVID pandemic of 2019–2021. We then unpack how Beta Bank further leveraged EaaS to enhance its digital resilience via a mitigation approach. Our work delineates a typology of four EaaS pathways for pursuing digital resilience, discusses the scholarly implications of EaaS as a future IS research area, and provides actionable managerial guidance through an EaaS roadmap, a strategic planning tool for those aiming to implement and benefit from this technology strategy.

Large Language Models for Conducting Advanced Text Analytics Information Systems Research

The exponential growth of digital content has generated massive textual datasets, necessitating the use of advanced analytical approaches. Large Language Models (LLMs) have emerged as tools that are capable of processing and extracting insights from massive unstructured textual datasets. However, how to leverage LLMs for text analytics Information Systems (IS) research is currently unclear. To assist the IS community in understanding how to operationalize LLMs, we propose a Text Analytics for Information Systems Research (TAISR) framework. Our proposed framework provides detailed recommendations grounded in IS and LLM literature on how to conduct meaningful text analytics IS research for design science, behavioral, and econometric streams. We conducted three business intelligence case studies using our TAISR framework to demonstrate its application in several IS research contexts. We also outline the potential challenges and limitations of adopting LLMs for IS. By offering a systematic approach and evidence of its utility, our TAISR framework contributes to future IS research streams looking to incorporate powerful LLMs for text analytics.

Searching for people in the workplace: aims, behaviour, and challenges

Introduction. People have been shown to play an important role in solving search tasks in professional settings. Searching for a person covers a large part of the information retrieval carried out in the workplace. However, it can be challenging to identify relevant people through a query. Thus, to support workplace users, knowledge of the characteristics of people searches is needed to identify which steps can be taken to ease the retrieval process. Method. This study examined the characteristics of people searching in a biotechnology company based on a mixed-methods process consisting of search log data from an in-house enterprise system, a questionnaire survey, and semi-structured interviews with users of the enterprise system. Results. The findings showed that people search accounted for 59.5% of all queries in the search log. Log data and questionnaire data were analysed to determine how the queries were distributed across organisational areas and information sources. The interview data provided insight into how people search was used for locating people and expertise and as part of exploratory, complex subject searches. Challenges in searching for people were also discussed in the interviews. The findings were used to discuss the implications for future workplace information systems.

Sociotechnical perspectives of digital technologies in sustainable mining

This paper adopts an interpretive case study approach to understand the role of digital technologies in addressing seemingly contradictory sustainability goals in mining. The sociotechnical model of information systems was used as a framework to guide the analysis of twenty-five in-depth interviews with globally dispersed digital technology experts working collaboratively at an industry-leading hi-tech mining solutions company. The sociotechnical-led thematic analysis findings highlight the trade-offs experts face in balancing narrow technological imperatives and economic outcomes with broader sustainability goals. The analysis moves beyond the technological and economic to a harmonious perspective of social, human, environmental, and technological interactions. A visual thematic map is presented to aid practitioners in designing and optimally implementing digital technologies to simultaneously address the United Nations Sustainable Development Goals while prioritising business sustainability. We conclude by drawing from the proposed sociotechnical perspectives approach for digital sustainability to provide scholars with possible pathways for future responsible information systems research.

Understanding Farmers' Data Collection Practices on Small-to-Medium Farms for the Design of Future Farm Management Information Systems

Farm Management Information Systems (FMIS) integrate data from a variety of sources, including sensors, for the purpose of enabling farmers to interpret past activity and predict future performance. FMIS is traditionally designed for and used by large farms, given their capital and need for automation and scale-up. This paper examines the current data collection practices on small and medium farms so that FMIS systems can be better designed to their needs. Our empirical research comprises interviews conducted during 10 farm visits. Our semi-structured interviews incorporated questions about daily activities, points of decision-making, data sharing, and incentives for data collection. We analyzed the interviews by focusing on possible obstacles to adopting expanding digital data collection practices and how expanded data collection might help fulfill farmers' goals and motivations. We found that farmers use their own bespoke data collection techniques instead of or in parallel to more formalized methods and often hold key observations and hypotheses in their heads rather than committing them to any data collection system at all. Key barriers to FMIS adoption include technology skepticism, technical hurdles, lack of support, and self-doubt in technical skills. Based on this empirical work and analysis, we recommend that FMIS systems can best address the needs of small and medium farms by 1) accounting for the farmers' different approaches to memorizing vs. storing data, 2) integrating rather than trying to replace existing practices, and 3) considering the economic and political motivations driving farm decision-making and practices.

Examining information systems use to facilitate the workplace accommodation process.

The workplace accommodation process is often affected by ineffective and inefficient communications and information exchanges among disabled employees and other stakeholders. Information systems (IS) can play a key role in facilitating a more effective and efficient accommodation process since IS has been shown to facilitate business processes and effect positive organizational changes. Since there is little to no research that exists on IS use to facilitate the workplace accommodation process, this paper, as a critical first step, examines how IS have been used in the accommodation process. Thirty-six interviews were conducted with disabled employees from various organizations. Open, axial, and selective coding were part of the analysis. Fuzzy set qualitative comparative analysis was used to identify different levels of IS use based on participants' descriptions. An IS used in the workplace accommodation process consists of electronic request form, accommodation checklist, special budget, specific role, ancillary service, formal policy and procedure. There are different levels of IS use in the current accommodation process. The high-level IS use often results in a better accommodation performance than the low-level IS use, including high efficiency, high effectiveness, and low emotional tolls. Nevertheless, the high-level IS use often uses a specific, inflexible template as well as disregards human elements in the accommodation process. This work provides implications that future IS design should raise awareness of disability and accommodation, account for individual differences, involve multiple stakeholder inputs, as well as address the fundamental social issues in the accommodation process.

Role of Machine Learning in Cybersecurity: Techniques and Challenges

Abstract: Machine learning (ML) plays a crucial role in cybersecurity across various aspects of threat detection, prevention, and response. Here are specific roles and applications of machine learning in cybersecurity. Machine Learning (ML) represents a pivotal technology for current and future information systems, and many domains already leverage the capabilities of ML. However, deployment of ML in cybersecurity is still at an early stage, revealing a significant discrepancy between research and practice. Such a discrepancy has its root cause in the current state of the art, which does not allow us to identify the role of ML in cybersecurity. The full potential of ML will never be unleashed unless its pros and cons are understood by a broad audience. In the computer world, data science is the force behind the recent dramatic changes in cybersecurity's operations and technologies. The secret to making a security system automated and intelligent is to extract patterns or insights related to security incidents from cybersecurity data and construct appropriate data-driven models. Data science, also known as diverse scientific approaches, machine learning techniques, processes, and systems, is the study of actual occurrences via the use of data. Recent breakthroughs in Machine Learning (ML) methods promise new solutions to each of these infamous diversification and asymmetric information problems throughout the constantly increasing vulnerability reporting data-bases. Due to their varied methodologies, those procedures themselves display varying levels of performance. The authors provide a method for cognitive cybersecurity that enhances human cognitive capacity in two ways. To create trustworthy data sets, initially reconcile competing vulnerability reports and then pre-process advanced embedded indicators

Role of Machine Learning in Cybersecurity: Techniques and Challenges

Abstract: Machine learning (ML) plays a crucial role in cybersecurity across various aspects of threat detection, prevention, and response. Here are specific roles and applications of machine learning in cybersecurity. Machine Learning (ML) represents a pivotal technology for current and future information systems, and many domains already leverage the capabilities of ML. However, deployment of ML in cybersecurity is still at an early stage, revealing a significant discrepancy between research and practice. Such a discrepancy has its root cause in the current state of the art, which does not allow us to identify the role of ML in cybersecurity. The full potential of ML will never be unleashed unless its pros and cons are understood by a broad audience. In the computer world, data science is the force behind the recent dramatic changes in cybersecurity's operations and technologies. The secret to making a security system automated and intelligent is to extract patterns or insights related to security incidents from cybersecurity data and construct appropriate data-driven models. Data science, also known as diverse scientific approaches, machine learning techniques, processes, and systems, is the study of actual occurrences via the use of data. Recent breakthroughs in Machine Learning (ML) methods promise new solutions to each of these infamous diversification and asymmetric information problems throughout the constantly increasing vulnerability reporting data-bases. Due to their varied methodologies, those procedures themselves display varying levels of performance. The authors provide a method for cognitive cybersecurity that enhances human cognitive capacity in two ways. To create trustworthy data sets, initially reconcile competing vulnerability reports and then pre-process advanced embedded indicators

User Engagement and Beyond: A Conceptual Framework for Engagement in Information Systems Research

Humans are considered “engaged” once they invest personal resources such as energy, time, or attention beyond a required level. This engagement state occurs when feeling connected to another actor or an object. In information systems (IS) research, engagement is recognized in the concept of ‘user engagement’, which has for many years successfully been employed to understand interaction patterns and user reactions. However, as IS artifacts evolve, we observe contemporary phenomena that are no longer covered by the existing concept: For one, IS have developed from passive resources (such as websites) to human-like actors (such as conversational, nowadays often large language model (LLM)-based, agents) so that a pure ‘user engagement’ perspective does not capture new affordances for engagement with active counterparts. Secondly, IS increasingly act as intermediaries into other engagement objects (e.g., organizations) that are the ultimate target of engagement. Thus, IS research may benefit from a broader perspective on engagement. Our work systematically draws on a structured literature review across adjacent academic disciplines and an in-depth qualitative analysis to develop a more comprehensive conceptual framework for engagement. With this framework, we contribute a refined and broadened conceptual base for engagement and discuss how it can inform future IS research.

From Methodological Symmetry to Gaia: Latour’s Legacy and Untapped Potential for IS Research

A defining concern for information systems (IS) is how to theorize its primary object of study, the digital artifact. Historically, approaches in IS have oscillated between technological determinist and social determinist ones. Bruno Latour’s works contributed to recalibrating a more symmetrical treatment of the two, and he played a distinctive role in stimulating the evolution of empirically committed, process-oriented theorizing in the IS field. We commemorate Latour’s work by revisiting his early influence on IS research and discuss some of the direct and indirect influences on contemporary IS, such as infrastructure studies and sociomateriality. With the interest in agentic technologies via AI, Latour’s perspectives on technological agency are more relevant than ever. In addition, we explore ideas that are relevant to IS but have yet to be taken up, thus representing untapped theoretical and methodological potential for future IS research—for example, approaching data as circulating reference or how his work could contribute to sustainability discussions in IS.

ANALYZING CONSTRAINTS FROM EUCS EVALUATION RESULTS: ANTICIPATING POTENTIAL FLAWS IN THE FUTURE DEVELOPMENT OF INFORMATION SYSTEMS

This research aims to analyze the constraints that arise from the EUCS (End-User Computing Satisfaction) evaluation results and to propose measures to anticipate potential shortcomings in future information system development. In the context of ongoing digital transformation, information system plays an increasingly important role in supporting organizational success. However, constraints from EUCS evaluation results are often overlooked, which can negatively impact the quality of information system development. Through an in-depth analysis of the identified constraints, this research uses the narrative review method to identify constraints that could potentially recur in the future. Furthermore, this research proposes steps that can be taken to anticipate potential shortcomings in future information system development. The author found the obstacles that tend to recur are the completeness of information (content), information that is not up to date (accuracy), monotonous appearance (format), the use of systems that are not easy to learn (ease of use), and long response time (timeliness). So, in the future, this can be a concern when designing an information system

Tourism Competitiveness Evaluation Model of Urban Historical and Cultural Districts Based on Multi-Source Data and the AHP Method: A Case Study in Suzhou Ancient City

Urban historical and cultural districts, serving as multi-functional compounds integrating cultural preservation, consumer experience, and economic growth, are increasingly becoming the preferred choice for in-depth tourism under the trend of historical heritage protection and consumption upgrading. Due to the complexity of the construction purpose, inherent functions, and operational management of historical districts, scientifically and rationally evaluating them poses a challenge. This paper attempts to construct an evaluation method for the tourism competitiveness of urban historical and cultural districts based on multi-source data and the Analytic Hierarchy Process (AHP) method. First, based on the model of destination competitiveness and combined with literature research and open-ended expert interviews, an evaluation framework for the tourism competitiveness of urban historical and cultural districts is established, using the AHP method to calculate the specific weights of each evaluation indicator. Then, the corresponding data sources for each indicator and the data processing and calculation methods are further clarified. To verify the effectiveness of the proposed evaluation model, this paper selects three key historical and cultural districts in Suzhou City, calculates the tourism competitiveness of each district based on the proposed model, and collects tourist satisfaction surveys from the three districts for cross-validation with the evaluation results. The experimental results show that the evaluation model is reliably effective in assessing the cultural, commercial, and tourism service aspects of historical districts, thereby providing a theoretical basis for future tourism decision-making information systems and practical applications of historical districts.

Circular economy oriented future building information processing: PSO for CNN approach

With the acceleration of urbanization, resource depletion is becoming increasingly serious. The innovative strategy of resource utilization is of great significance to the development of new green building. Therefore, this paper combines artificial intelligence technology and deeply excavates the basic information of the old building images collected by the survey. It determines the specific future building construction information from the direction of sustainable development. Based on the traditional convolutional neural network U-Net model, this paper improves its shortcomings, proposes an improved IU-Net model and optimizes the full convolutional neural network structure based on Particle Swarm Optimization (PSO). This paper analyzes and designs the future building information system platform oriented to circular economy, and it compares the improved IU-Net model with other models through experiments to obtain the accuracy and other indicators. The experimental results indicate that the system using the IU-Net model of full convolutional neural network based on PSO has higher prediction accuracy. It has better functionality for the analysis and decision-making of future building.

The Integration of Artificial Intelligence Into Database Systems(AI-DB Integration Review)

In recent times, it can be deduced that a distributed workforce is the future of work, and the future is now. Therefore, it is essential to know that AI-DB integration is not only for the effective application of Artificial intelligence technology, and the development of database technology, but also for the next generation of computing, which will support future Intelligent Information Systems, and allow work to be more effective and productive. Hence, AI-DB Integration will contribute generally to the infrastructure of science and technology, businesses, and humanitarian applications of computers. With all the potential contributions in play, AI-DB Integration is considerably more important than might be assumed from its contribution to the enhancement of AI and DB technologies alone. In this review, different concepts were discussed by emphasizing some key areas like the design of Intelligent Database Interfaces (IDIs), Learnable databases, and Smart Query. The three fundamental areas geared us to investigate how AI enhances database efficiency by optimizing query performance, automating routine management tasks, and fortifying data security. Also, the paper presents short-term and long-term application areas where AI and databases have converged, providing a comprehensive overview of progress, challenges, and opportunities. The outcome of this review expresses some authors' or experts’ opinions on the need for and importance of AI-DB integration and on the future generation of computing.

Breakdowns in team resilience during aircraft landing due to mental model disconnects as identified through machine learning

Landing system in aviation is a representative sociotechnical system; such systems often require seamless teamwork among experts. Resilience is critical for the ability of a team to adapt to sudden changes, overcome challenges, and return to normalcy. However, team heterogeneity can result in cognitive misalignment, known as mental model disconnects (MMDs). MMDs can cause interaction conflcits, which compromise team resilience. This study aimed to use machine learning to identify breakdowns in team resilience during aircraft landing. The results showed that the MMDs in the terminal area had the greatest influence on missed approaches; in particular, the late intervention of air traffic controllers’ (ATCOs) in an improper landing state was the major breakdown identified. This result highlights the importance of ATCO initiative in preventing landing incidents. The mechanism was determined to be ineffective information dissemination in nonroutine scenarios, which unravels the necessity for improving future information systems.

Understanding Spatial Cognition for Designing Pedestrian Wayfinding Systems

Individuals react differently when moving in urban spaces, as their senses are distinctively activated. Their interpretations and how the built environment is perceived result in different meanings. When studying public spaces, it is of utmost importance to understand how residents and visitors construct images of the city, by perceiving and appropriating urban spaces. In particular, this is decisive when designing Pedestrian Wayfinding Systems (PWS) in urban areas.
 Based on spatial cognition studies, this paper identifies and explores the main aspects influencing the design of wayfinding systems. The study is structured in three main parts. First, the article reviews literature on spatial cognition and human behaviors. Second, it analyzes some reference cases considered good practices for PWS, their implementation in contemporary public spaces, and their visual and aesthetic forms of expression. Third, it develops a practical guide for designing a PWS focused on end-users. More specifically, the research aims to support the municipality of Porto in defining the tender specifications for the development of a PWS, to be included in the future Integrated Signage and Information System.
 Ultimately, this study proposes recommendations to improve pedestrian navigation in the urban context and help decision-makers, designers, and urban planners potentially create better wayfinding systems in the future.

PENGEMBANGAN SISTEM INFORMASI MONITORING PRAKERIN BERBASIS ANDROID DI SMKN 2 BARRU

This study aims: (1) to produce an Android-based internship monitoring information system, and (2) to determine the performance of an Android-based internship monitoring information system. This study used the Research and Development research method which was carried out at SMKN 2 Barru. The research data was obtained by giving a questionnaire to 30 respondents divided into 20 productive teachers and 10 industrial partners. The data analysis technique used is percentage descriptive analysis. The results of the analysis found an Android-based internship monitoring information system. The test results using the Black Box method on the system show that the system has good functionality, all functions can work properly and have met the requirement specifications. The test results show that the information system has been acceptable to users and can be used as a medium to find information about internship activities. For future Android-based apprentice monitoring information system developers, it is expected that all SMK in Barru Regency can use it. Recommendations for researchers who are interested in studying and studying more broadly should use different development and application methods.

A novel autonomous vehicle interface for older adults with cognitive impairment

The population of older Americans with cognitive impairments, especially memory loss, is growing. Autonomous vehicles (AVs) have the potential to improve the mobility of older adults with cognitive impairment; however, there are still concerns regarding AVs’ usability and accessibility in this population. Study objectives were to (1) better understand the needs and requirements of older adults with mild and moderate cognitive impairments regarding AVs, and (2) create a prototype for a holistic, user-friendly interface for AV interactions. An initial (Generation 1) prototype was designed based on the literature and usability principles. Based on the findings of phone interviews and focus group meetings with older adults and caregivers (n = 23), an enhanced interface (Generation 2) was developed. This generation 2 prototype has the potential to reduce the mental workload and anxiety of older adults in their interactions with AVs and can inform the design of future in-vehicle information systems for older adults.

Physicians' Perspectives on EHR Usability: Results from Four Large Cross-Sectional Surveys from 2010 to 2021.

Usability and user experience are central quality attributes of electronic health record (EHR) systems. Usability evaluation studies typically focus on short-term use and situational usability, although feedback collected during operational use provides input for future information systems development. An abundance of studies report on physicians' dissatisfaction with the usability of their EHR systems and many show an association between poor usability and physician burnout. However, there is a scarcity of large long-term monitoring studies assessing end users' experiences with EHRs. We report on the results from four large (n=3,929-4,628) national cross-sectional usability surveys conducted among Finnish physicians in 2010, 2014, 2017, and 2021. The main finding was that the perceptions of physicians working in public health centres had changed for the better but those working in public hospitals reported similar or even more negative experiences in 2021 than in 2010-17; they rated only system responsiveness to inputs as having improved. Based on this finding, systematic research-based monitoring of EHR development from the end users' perspectives should be continued.