Information Technology Adoption Process within Indonesian SMEs: An Empirical Study

IT adoption within SMEs has been covered extensively within literature, most of which have considered IT adoption from narrow perspective such as drivers and barriers of IT adoption. IT adoption is better defined as a process which involves organisation and its components, stakeholders external to the organisation, and interactions within organisation and between organisation and its stakeholders. This paper uses multi perspective in IT adoption to build model of IT adoption. A field study involving 35 Indonesian SMEs was conducted in the form of semi structured interviews. The result from this field study were analysed and used to refine the proposed model

An exploration of IoT platform development

IoT (Internet of Things) platforms are key enablers for smart city initiatives, targeting the improvement of citizens\u27 quality of life and economic growth. As IoT platforms are dynamic, proactive, and heterogeneous socio-technical artefacts, systematic approaches are required for their development. Limited surveys have exclusively explored how IoT platforms are developed and maintained from the perspective of information system development process lifecycle. In this paper, we present a detailed analysis of 63 approaches. This is accomplished by proposing an evaluation framework as a cornerstone to highlight the characteristics, strengths, and weaknesses of these approaches. The survey results not only provide insights of empirical findings, recommendations, and mechanisms for the development of quality aware IoT platforms, but also identify important issues and gaps that need to be addressed

Exploring Qualitative Research Using LLMs

The advent of AI driven large language models (LLMs) have stirred discussions about their role in qualitative research. Some view these as tools to enrich human understanding, while others perceive them as threats to the core values of the discipline. This study aimed to compare and contrast the comprehension capabilities of humans and LLMs. We conducted an experiment with small sample of Alexa app reviews, initially classified by a human analyst. LLMs were then asked to classify these reviews and provide the reasoning behind each classification. We compared the results with human classification and reasoning. The research indicated a significant alignment between human and ChatGPT 3.5 classifications in one third of cases, and a slightly lower alignment with GPT4 in over a quarter of cases. The two AI models showed a higher alignment, observed in more than half of the instances. However, a consensus across all three methods was seen only in about one fifth of the classifications. In the comparison of human and LLMs reasoning, it appears that human analysts lean heavily on their individual experiences. As expected, LLMs, on the other hand, base their reasoning on the specific word choices found in app reviews and the functional components of the app itself. Our results highlight the potential for effective human LLM collaboration, suggesting a synergistic rather than competitive relationship. Researchers must continuously evaluate LLMs role in their work, thereby fostering a future where AI and humans jointly enrich qualitative research

ELICA: An Automated Tool for Dynamic Extraction of Requirements Relevant Information

Requirements elicitation requires extensive knowledge and deep understanding of the problem domain where the final system will be situated. However, in many software development projects, analysts are required to elicit the requirements from an unfamiliar domain, which often causes communication barriers between analysts and stakeholders. In this paper, we propose a requirements ELICitation Aid tool (ELICA) to help analysts better understand the target application domain by dynamic extraction and labeling of requirements-relevant knowledge. To extract the relevant terms, we leverage the flexibility and power of Weighted Finite State Transducers (WFSTs) in dynamic modeling of natural language processing tasks. In addition to the information conveyed through text, ELICA captures and processes non-linguistic information about the intention of speakers such as their confidence level, analytical tone, and emotions. The extracted information is made available to the analysts as a set of labeled snippets with highlighted relevant terms which can also be exported as an artifact of the Requirements Engineering (RE) process. The application and usefulness of ELICA are demonstrated through a case study. This study shows how pre-existing relevant information about the application domain and the information captured during an elicitation meeting, such as the conversation and stakeholders' intentions, can be captured and used to support analysts achieving their tasks.Comment: 2018 IEEE 26th International Requirements Engineering Conference Workshop

Challenges and Solutions in AI for All

Artificial Intelligence (AI)'s pervasive presence and variety necessitate diversity and inclusivity (D&I) principles in its design for fairness, trust, and transparency. Yet, these considerations are often overlooked, leading to issues of bias, discrimination, and perceived untrustworthiness. In response, we conducted a Systematic Review to unearth challenges and solutions relating to D&I in AI. Our rigorous search yielded 48 research articles published between 2017 and 2022. Open coding of these papers revealed 55 unique challenges and 33 solutions for D&I in AI, as well as 24 unique challenges and 23 solutions for enhancing such practices using AI. This study, by offering a deeper understanding of these issues, will enlighten researchers and practitioners seeking to integrate these principles into future AI systems.Comment: 39 pages, 10 figures, 10 table

2 Requirements Elicitation: A Survey of Techniques, Approaches, and Tools

Abstract: Requirements elicitation is the process of seeking, uncovering, acquiring, and elaborating requirements for computer based systems. It is generally understood that requirements are elicited rather than just captured or collected. This implies there are discovery, emergence, and development elements to the elicitation process. Requirements elicitation is a complex process involving many activities with a variety of available techniques, approaches, and tools for performing them. The relative strengths and weaknesses of these determine when each is appropriate depending on the context and situation. The objectives of this chapter are to present a comprehensive survey of important aspects of the techniques, approaches, and tools for requirements elicitation, and examine the current issues, trends, and challenges faced by researchers and practitioners in this field

The Innovation-to-Occupations Ontology: Linking Business Transformation Initiatives to Occupations and Skills

The fast adoption of new technologies forces companies to continuously adapt their operations making it harder to predict workforce requirements. Several recent studies have attempted to predict the emergence of new roles and skills in the labour market from online job ads. This paper aims to present a novel ontology linking business transformation initiatives to occupations and an approach to automatically populating it by leveraging embeddings extracted from job ads and Wikipedia pages on business transformation and emerging technologies topics. To our knowledge, no previous research explicitly links business transformation initiatives, like the adoption of new technologies or the entry into new markets, to the roles needed. Our approach successfully matches occupations to transformation initiatives under ten different scenarios, five linked to technology adoption and five related to business. This framework presents an innovative approach to guide enterprises and educational institutions on the workforce requirements for specific business transformation initiatives.Comment: 14 pages, 3 figures, Camera-ready version in ACIS 202

Uncovering Barriers in Forecasting Uncertain Product Demand in the Supply Chain

This paper aims to provide insights into the barriers of forecasting uncertain product demand in supply chain by focusing on the relative importance of the barriers for businesses, particularly the forecast practitioners and prospective forecast implementers. A exploratory, qualitative approach was adopted within an Australian electrical manufacture. Data was gathered through semi-structured interviews with 20 participants from different departments, including forecasting practitioners, supplier and customer of the Australian electronics manufacturer. Thematic analysis was conducted to confirm some of the existing barriers reported in the literature and identify emerging barriers from practice in industry. The study reveals that there are more barriers to choosing the right forecasting system or method and the main reason for poor forecast performance is intertwined between cultural, communication, product, market, environmental and technological themes. These themes lend empirical insights into the barriers still faced in many organisation today. The identification of end to end barriers in forecasting uncertain product demand of the electrical manufacturing industry have not previously been studied in great depth. This paper sheds insight, provides new knowledge and contributes to academic thinking

Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering

Responsible AI is widely considered as one of the greatest scientific challenges of our time and is key to increase the adoption of AI. Recently, a number of AI ethics principles frameworks have been published. However, without further guidance on best practices, practitioners are left with nothing much beyond truisms. Also, significant efforts have been placed at algorithm-level rather than system-level, mainly focusing on a subset of mathematics-amenable ethical principles, such as fairness. Nevertheless, ethical issues can arise at any step of the development lifecycle, cutting across many AI and non-AI components of systems beyond AI algorithms and models. To operationalize responsible AI from a system perspective, in this paper, we present a Responsible AI Pattern Catalogue based on the results of a Multivocal Literature Review (MLR). Rather than staying at the principle or algorithm level, we focus on patterns that AI system stakeholders can undertake in practice to ensure that the developed AI systems are responsible throughout the entire governance and engineering lifecycle. The Responsible AI Pattern Catalogue classifies the patterns into three groups: multi-level governance patterns, trustworthy process patterns, and responsible-AI-by-design product patterns. These patterns provide systematic and actionable guidance for stakeholders to implement responsible AI

Two Sides of the Same Coin: Software Developers' Perceptions of Task Switching and Task Interruption

In the constantly evolving world of software development, switching back and forth between tasks has become the norm. While task switching often allows developers to perform tasks effectively and may increase creativity via the flexible pathway, there are also consequences to frequent task-switching. For high-momentum tasks like software development, "flow", the highly productive state of concentration, is paramount. Each switch distracts the developers' flow, requiring them to switch mental state and an additional immersion period to get back into the flow. However, the wasted time due to time fragmentation caused by task switching is largely invisible and unnoticed by developers and managers. We conducted a survey with 141 software developers to investigate their perceptions of differences between task switching and task interruption and to explore whether they perceive task switchings as disruptive as interruptions. We found that practitioners perceive considerable similarities between the disruptiveness of task switching (either planned or unplanned) and random interruptions. The high level of cognitive cost and low performance are the main consequences of task switching articulated by our respondents. Our findings broaden the understanding of flow change among software practitioners in terms of the characteristics and categories of disruptive switches as well as the consequences of interruptions caused by daily stand-up meetings