Mohammad T Islam (BSc, MBA, MEd)

What Makes English Academic?

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The concept of academic English is central to success in higher education. Unlike everyday communication, academic English is characterised not by the ideas themselves but by the way those ideas are presented and expressed (Wilson, n.d.). It provides a structured, evidence-based, and formal mode of communication that aligns with the conventions of the academy. This essay explores the defining features of academic English, focusing on the presentation of ideas, expression of arguments, and the use of appropriate style and vocabulary. Examples will illustrate how academic English functions in practice, drawing on textbooks, scholarly journals, and guidance from reputable educational sources. Presenting Ideas in Academic English One of the hallmarks of academic English is the logical organisation of ideas. According to Wilson (n.d.), ideas should follow a structured order, beginning with a plan, grouping related points, and supporting each paragraph with a topic sentence that introduces its focus. This emphasis on logical flow ensures that arguments are easy to follow and coherent. For example, in a research paper examining the effectiveness of speed cameras in preventing road traffic accidents published in the British Medical Journal (BMJ, 2005), the authors structured their argument carefully: presenting a clear topic sentence, supporting it with evidence from randomised controlled trials, and offering cautious alternatives where evidence was insufficient. This systematic ordering allowed the audience to follow a complex debate with clarity. This approach reflects Swales’ (1990) influential concept of the “moves” in academic discourse, where writing progresses through identifiable stages such as establishing territory, identifying a niche, and occupying that niche. Without such structure, ideas risk becoming fragmented and less persuasive. Using Evidence Academic English is distinguished by its reliance on evidence rather than personal opinion. Students are expected to draw upon reliable sources, compare viewpoints, and acknowledge areas of agreement and disagreement (Wilson, n.d.). This reflects the broader principle of knowledge construction in academia, which values argumentation based on verifiable data. Cottrell (2019) emphasises that critical analysis in academic writing requires synthesising material from multiple authors rather than relying on a single source. For example, when discussing climate change, an academic essay would not simply state that “climate change is bad,” but would cite scientific consensus from the Intergovernmental Panel on Climate Change (IPCC, 2021) alongside counter-arguments from economists concerned about adaptation costs. This balanced approach demonstrates both breadth and depth of understanding. Importantly, evidence must be referenced appropriately, using recognised citation styles such as the Harvard system, which reinforces transparency and academic integrity (Pears and Shields, 2019). Objectivity in Academic English Another crucial feature of academic English is its objectivity. Writers are advised to avoid emotional or subjective statements, instead presenting measured suggestions (Wilson, n.d.). For instance, rather than asserting, “Speed cameras are absolutely essential to saving lives,” an academic author might write, “Evidence suggests that speed cameras may contribute to a reduction in traffic-related fatalities, though further research is required.” Such cautious phrasing avoids overstatement and acknowledges uncertainty. Hyland (2005) notes that hedging—the use of cautious language like may, could, or suggests—is a defining feature of academic discourse. Hedging allows scholars to present claims without overstating certainty, reflecting the tentative and evolving nature of knowledge. This contrasts with journalistic writing, which often seeks definitive statements to capture attention. Formal Language Academic writing avoids colloquial expressions and employs formal, complete sentences without abbreviations or contractions (Wilson, n.d.). For example, rather than writing “don’t”, an academic text would use “do not.” Similarly, slang terms like “kids” would be replaced with “children.” Bailey (2018) stresses that academic writing is also characterised by impersonal constructions. Passive voice, while often discouraged in other contexts, can be useful in academic English because it removes personal bias. For instance, “The experiment was conducted to test the hypothesis” focuses on the process rather than the researcher. Specialist Vocabulary The use of specialist vocabulary is another defining trait. Wilson (n.d.) recommends students familiarise themselves with technical terms used in their discipline, observing how they appear in books and journal articles. For example, in medicine, terms like randomised controlled trial or systematic review are standard and must be employed accurately. Misuse of such terminology can undermine credibility. Hyland and Tse (2007) argue that mastering discipline-specific lexis is part of developing academic identity, signalling belonging within a scholarly community. This reflects the idea of “discourse communities” (Swales, 1990), where specialised language marks membership and expertise. Expected Words and Phrases Academic writing also employs signposting language that guides readers through arguments. Common phrases such as “on the one hand”, “in contrast”, and “therefore” provide clear cues about logical relationships between ideas. These markers are not optional but expected by academic readers (Wilson, n.d.). Additionally, academic writing often uses cautious phrases like “it appears that” or “the evidence suggests.” This reflects the epistemological stance of academia, where knowledge is provisional and open to challenge (Hyland, 2005). By adopting such language, students align themselves with academic conventions and demonstrate awareness of scholarly norms. Practical Steps for Students Developing academic English is a process that requires practice and support. Wilson (n.d.) suggests students can improve by attending writing workshops, enrolling in short courses, and engaging actively with academic reading. Lea and Street’s (1998) academic literacies model highlights that learning academic writing is not just a matter of mastering technical skills but involves adapting to new cultural and epistemological practices. For example, a student transitioning from high school to university may initially struggle with avoiding personal opinions or providing sufficient evidence. Over time, however, they can acquire the conventions of academic discourse through feedback and reflection. In summary, academic English is distinguished by its structured presentation of ideas, reliance on evidence, objectivity, formal style, specialist vocabulary, and use of expected signposting language. These features are not merely stylistic but reflect the values of the academic community: precision, rigour, and openness to dialogue. Mastering academic English equips students to participate effectively in scholarly conversations, construct persuasive arguments, and succeed in their studies. Ultimately, as Wilson (n.d.) reminds us, becoming proficient in academic writing takes practice, but it is … Read more

The Ten Bad Listening Habits and Their Implications for Effective Learning

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Listening is one of the most fundamental yet overlooked skills in communication. Scholars argue that while individuals spend approximately 45% of their communication time listening, they often do so ineffectively (Brownell, 2012). Poor listening habits hinder academic learning, workplace collaboration, and interpersonal relationships. Ralph Nichols, often referred to as the “Father of Listening Research”, identified ten bad listening habits that remain highly relevant today (Nichols, 1960; Wolvin & Coakley, 1996). This article explores these habits, analyses their implications, and suggests strategies to cultivate effective listening. 1.0 Calling the Subject Dull One of the most common poor listening habits is dismissing a subject as boring. According to Nichols (1960), ineffective listeners disengage when the topic seems uninteresting, while effective listeners search for useful information. For instance, in a lecture on statistics, a disengaged student may “switch off,” missing crucial content, whereas an engaged student will filter for applications relevant to their research. As Cottrell (2019) notes, successful learners approach all material with the mindset that it may contain valuable insights. 2.0 Criticising the Speaker Another poor habit is focusing on the speaker’s mannerisms rather than the message. Nichols (1960) emphasises that effective listeners quickly move past a speaker’s flaws to focus on the content. Research supports this: listeners who engage in “message-focused listening” rather than “form-focused listening” retain more information (Imhof & Janusik, 2006). For example, dismissing a professor’s lecture because of their monotone voice risks losing critical academic material. Effective listening requires separating style from substance. 3.0 Getting Overstimulated Listeners often overreact emotionally to specific points, which blocks further understanding. Nichols (1960) argued that listeners must withhold evaluation until comprehension is complete. This resonates with active listening theory, which stresses delaying judgement (Rogers & Farson, 1987). For instance, a manager hearing criticism may fixate defensively on one comment, failing to grasp the broader feedback. Training in emotional regulation can help listeners remain open to the entire message. 4.0 Listening Only for Facts Poor listeners tend to focus narrowly on facts rather than main ideas. Nichols (1960) found that such listeners often misinterpret or forget information. Effective listeners extract key themes and use them as anchors for facts (Brownell, 2012). For example, during a legal briefing, a lawyer who listens for the overarching principle rather than isolated statistics is better positioned to recall and apply knowledge in context. 5.0 Trying to Outline Everything Some listeners rigidly attempt to outline every detail, which may not align with the speaker’s delivery style. As Nichols (1960) suggested, good listeners are flexible and adapt note-taking to the presentation. This aligns with modern note-making strategies such as mind mapping (Buzan, 2018), which allow learners to organise information non-linearly, capturing both structure and nuance. 6.0 Faking Attention “Pretend listening” involves appearing attentive without genuine engagement. Nichols (1960) stressed that listening is an active and energy-consuming process. Physiological signs such as increased heart rate and mental focus indicate authentic attention. In workplaces, faked attention undermines trust. Research by Bodie et al. (2015) shows that active listening behaviours, including nodding and paraphrasing, significantly improve communication effectiveness. 7.0 Tolerating Distraction Ineffective listeners allow external or internal distractions to interfere. Nichols (1960) highlighted that good listeners develop the ability to filter distractions. For example, students distracted by mobile phones during lectures exhibit reduced comprehension (Junco, 2012). Practical strategies such as mindfulness techniques and creating a focused environment can significantly reduce susceptibility to distractions. 8.0 Choosing Only What’s Easy Poor listeners avoid difficult material, preferring simple or entertaining content. Nichols (1960) argued that this avoidance weakens intellectual growth. Similarly, deep learning theory emphasises the importance of engaging with challenging texts for critical thinking (Marton & Säljö, 1976). For instance, a medical student who avoids complex anatomy lectures is likely to face difficulties in professional practice. Thus, resilience in listening to demanding content is a hallmark of academic excellence. 9.0 Letting Emotion-Laden Words Interfere Emotionally charged words often cause listeners to “tune out.” Nichols (1960) referred to this as letting symbols override meaning. Research supports this: emotionally provocative language can impair rational processing (Krauss & Chiu, 1998). In political debates, terms like “immigration” or “feminism” may trigger strong reactions. Effective listeners acknowledge their emotions while remaining focused on the substance of the message. 10.0 Wasting the Differential Between Speech and Thought Speed A final poor habit involves misusing the gap between speech speed (100–125 words per minute) and thought speed (400–500 words per minute). Nichols (1960) suggested that good listeners exploit this gap by anticipating, identifying evidence, and summarising. For example, a student in a lecture can use spare mental capacity to summarise key points rather than daydreaming. Research on metacognition supports this technique, showing that self-monitoring enhances comprehension (Flavell, 1979). Overcoming Bad Listening Habits Overcoming these habits requires deliberate practice. Scholars recommend strategies such as: Active listening training, including paraphrasing and summarising (Brownell, 2012). Developing critical thinking to distinguish main ideas from supporting details (Cottrell, 2019). Practising mindfulness to manage distractions (Shapiro et al., 2006). Using structured approaches such as SQ3R for reading and adaptation for listening contexts. Listening is not merely a passive act but a dynamic and effortful process. Nichols’ identification of ten bad listening habits highlights how individuals often undermine their own comprehension and communication. From dismissing content as dull to misusing mental capacity, these habits impede learning and relationships. By cultivating active, flexible, and reflective listening practices, learners and professionals alike can enhance their effectiveness in both academic and real-world settings. Ultimately, effective listening is a learned skill that requires self-awareness, discipline, and the replacement of poor habits with constructive ones. References Bodie, G., Vickery, A., Cannava, K. & Jones, S. (2015). The role of “active listening” in informal helping conversations: Implications for research and practice. International Journal of Listening, 29(3), pp.99–117. Brownell, J. (2012). Listening: Attitudes, Principles, and Skills. 5th ed. Pearson. Buzan, T. (2018). Mind Map Mastery. Watkins Publishing. Cottrell, S. (2019). The Study Skills Handbook. 5th ed. Red Globe Press. Flavell, J. (1979). Metacognition and cognitive monitoring. American Psychologist, 34(10), pp.906–911. Imhof, M. & … Read more

Survey, Question, Read, Recall and Review (SQ3R): An Effective Reading Strategy

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Survey, Question, Read, Recall and Review (SQ3R): An Effective Reading Strategy Effective reading and study strategies are fundamental to academic success. One of the most influential methods is the SQ3R system, an acronym for Survey, Question, Read, Recall, and Review. Developed by Francis P. Robinson (1946) in his book Effective Study, the system provides a structured approach to reading comprehension and retention. Its effectiveness lies in transforming passive reading into an active learning process (Cottrell, 2019). This article critically discusses the SQ3R system, explores its application in different learning contexts, and analyses its relevance in the digital learning environment. Origins and Importance of SQ3R The SQ3R method was designed during the Second World War to help army personnel study efficiently (Robinson, 1946). Since then, it has become a staple in study skills programmes worldwide, emphasising critical thinking and information retention. According to Weinstein and Mayer (1986), learning strategies such as SQ3R enhance cognitive engagement and enable learners to construct meaningful connections between new knowledge and prior understanding. In an era of information overload, SQ3R offers a systematic framework for processing large amounts of material, particularly useful in higher education where students must engage with complex academic texts (Nist & Simpson, 2000). Step 1: Survey The first stage, Survey, involves scanning the material to gain an overview. This includes reading titles, subheadings, introductions, summaries, and visual aids (SQ3R document; Cottrell, 2019). The purpose is to establish a mental map of the text’s structure, enabling learners to set goals and expectations. For example, when approaching a chapter in a psychology textbook, a student may skim the headings on cognitive development and glance at figures or diagrams. This primes the mind to expect information about stages of development, key theorists, and empirical studies. Research by Rayner et al. (2012) demonstrates that previewing texts increases comprehension by providing a framework for active reading. Step 2: Question In the Question stage, learners transform headings and subheadings into questions. This step encourages active engagement with the material. Instead of passively reading, the student anticipates answers, fostering curiosity and deeper understanding (Nist & Holschuh, 2012). For instance, a heading such as “Piaget’s Theory of Cognitive Development” may lead to questions such as: What are Piaget’s stages? or How do they explain child learning?. This aligns with constructivist learning theory, which suggests that learning occurs when individuals actively construct knowledge (Bruner, 1961). Step 3: Read The Read stage involves carefully engaging with the text to answer the formulated questions. Reading becomes purposeful and focused, reducing distractions and improving retention. Empirical studies support this approach. McNamara (2009) found that students using structured reading strategies like SQ3R demonstrated improved comprehension, particularly when dealing with challenging texts. By continuously checking whether their questions are answered, learners also develop critical literacy skills, essential in academic research. Step 4: Recall Once a section is read, the Recall stage requires learners to summarise the main points from memory. This reinforces active retrieval, a process that has been shown to strengthen memory consolidation (Roediger & Butler, 2011). For example, after reading about Vygotsky’s Zone of Proximal Development, a student may close the book and attempt to explain the concept in their own words. This mirrors the testing effect, where retrieving knowledge enhances long-term retention (Karpicke & Blunt, 2011). Step 5: Review Finally, Review involves revisiting questions, notes, and summaries to ensure understanding is consolidated. This step fosters distributed practice, a highly effective learning strategy (Cepeda et al., 2006). Reviewing not only improves memory but also refines organisational skills, as learners restructure notes and highlight connections across chapters. As the SQ3R document emphasises, the Review phase ensures that “the information you gain from reading is important” rather than superficial (SQ3R document). In practice, this might mean revisiting a week’s lecture readings before an exam, thereby reinforcing knowledge systematically. Applications in Academic and Professional Settings The SQ3R system is widely used in academic contexts, from secondary education to postgraduate research. For instance, medical students often face extensive reading lists; applying SQ3R helps them prioritise key learning outcomes, particularly when studying clinical case studies (Brown et al., 2014). In professional environments, SQ3R is equally valuable. Business leaders analysing industry reports can apply the method to extract strategic insights efficiently. Similarly, in law, where practitioners must interpret dense legal texts, SQ3R provides a structured approach to identifying relevant arguments and precedents (Cottrell, 2019). Critiques and Limitations Despite its strengths, SQ3R is not without criticism. Some researchers argue it is time-consuming, making it less appealing in fast-paced environments (Pressley & Afflerbach, 1995). Additionally, learners with low motivation may struggle to sustain the questioning and recalling processes. Furthermore, in digital learning environments, where texts are hyperlinked and non-linear, SQ3R may require adaptation. However, studies suggest that combining SQ3R with digital annotation tools enhances its relevance (Mangen et al., 2013). SQ3R in the Digital Age The rise of e-learning and digital platforms has changed reading behaviours. Modern students often skim articles online rather than engage in deep reading. Applying SQ3R in digital contexts means using tools such as highlighting software, online flashcards, and summarisation apps to support each stage. For example, surveying may involve scrolling through abstracts and graphical abstracts, while recall may be enhanced using apps like Quizlet. As Carr (2010) notes in The Shallows, digital reading risks reducing comprehension. Therefore, structured methods like SQ3R are increasingly important in maintaining deep reading practices in an era of distraction. The SQ3R study system remains one of the most effective strategies for reading comprehension, retention, and academic success. By guiding learners through surveying, questioning, reading, recalling, and reviewing, it transforms passive reading into active learning. While challenges exist, particularly in adapting to digital platforms, the method’s core principles remain relevant. Ultimately, SQ3R embodies the philosophy that learning requires active effort and reflection. Whether applied by students, professionals, or lifelong learners, it continues to be a cornerstone of effective study skills in the 21st century. References Bruner, J. (1961). The Act of Discovery. Harvard University Press. Brown, C., Roediger, H. & McDaniel, M. … Read more

Sentence Building: An Essential Component of Academic Success in Higher Education

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Sentence building is one of the fundamental skills in academic writing. A well-constructed sentence enhances clarity, coherence, and precision, while poorly constructed sentences can obscure meaning and weaken arguments. In higher education, students are expected to demonstrate mastery in constructing sentences that convey complex ideas with accuracy (Cottrell, 2013). Developing sentence-building skills requires an understanding of grammar, syntax, vocabulary, sentence variety, and cohesion. It also involves critical thinking to ensure that meaning is expressed clearly and effectively in academic contexts (Moon, 2004). This article explores the importance of sentence building, common challenges, strategies for improvement, and practical examples from research and academic writing practice. 1.0 Importance of Sentence Building in Academic Writing Sentences are the basic units of thought in written communication. In university assignments, they serve as vehicles for arguments, evidence, and analysis. As Rahman (2025) notes, many undergraduate students struggle with sentence construction errors, including incorrect verb usage, subject–verb disagreement, and misplaced modifiers. Such errors undermine the credibility of academic writing. Moreover, Lahoual and Hdouch (2025) emphasise that complex sentence construction is associated with higher levels of writing proficiency. For example, a student who writes: “The theory is important. It is used in business.” demonstrates simplicity but lacks sophistication. Whereas: “The importance of this theory lies in its application across diverse business contexts, particularly in shaping strategic decision-making,” shows complexity and academic maturity. 2.0 Challenges in Sentence Building Grammatical Errors Research by Rahman (2025) in Bangladesh reveals that engineering students frequently commit errors in verb forms, prepositions, and sentence structure, suggesting a lack of foundational grammar knowledge. Similarly, Chuanpipatpong (2025) found that Thai EFL students often struggled with syntax and word order, impacting the quality of their argumentative essays. Overuse of Simple Sentences According to Maghfirah et al. (2025), many learners over-rely on simple sentences, leading to repetitive and monotonous writing. Academic writing often demands a balance of simple, compound, and complex structures to maintain reader engagement and convey nuanced meaning. Influence of First Language (L1) Rafieyan (2025) observed that students’ first language strongly influences sentence formation in a second language (L2). For instance, learners may transfer structures directly from their native language, resulting in awkward or ungrammatical constructions in English. 3.0 Strategies for Effective Sentence Building Mastery of Grammar and Syntax Grammar is the foundation of sentence building. As Nuryani and Rohmat (2025) note, words in sentences influence one another, and understanding grammatical relationships ensures accuracy. For example, subject–verb agreement (“The students are working” vs “The students is working”) is essential to maintain precision. Sentence Variety Effective academic writing requires varying sentence types: Simple sentences convey direct information. Compound sentences join related ideas: “The results were significant, and they confirmed the hypothesis.” Complex sentences introduce depth: “Although the results were significant, further research is needed to confirm the hypothesis.” By combining sentence types, writers improve the rhythm and readability of their work (Williams, 2025). Use of Transition Words and Cohesion Transitions such as however, therefore, in contrast, and furthermore link ideas, ensuring cohesion. Sukmawati (2025) found that students using digital writing assistants such as ChatGPT or Grammarly improved cohesion by generating clearer sentence structures and logical connectors. Feedback and Peer Review Lahoual and Hdouch (2025) highlight the value of peer feedback in improving complex sentence construction. When students review one another’s work, they learn alternative ways of phrasing and structuring ideas, enhancing their own writing. 4.0 Role of Technology in Sentence Building Digital tools have transformed how students build sentences. According to Anwar et al. (2025), applications such as Grammarly refine sentence accuracy, suggest alternatives, and encourage variety. Similarly, Maghfirah et al. (2025) found that QuillBot helps students balance precision and personal voice by rephrasing sentences while preserving meaning. However, as Pedersen (2025) warns, over-reliance on AI can reduce students’ ability to think critically about sentence construction. Tools should be seen as support systems, not replacements for independent skills. 5.0 Practical Applications in Higher Education Academic Essays In essays, sentence building reflects the ability to construct arguments logically. For example, instead of writing: “The study is about climate change. It is dangerous.” a stronger version would be: “This study critically examines climate change as a global threat, highlighting its profound environmental, economic, and social implications.” Reports and Dissertations Dissertation writing often requires precision in presenting data. Pourgholamali (2025) argues that maintaining sentence order and semantic fidelity is vital in research summaries, especially in fields such as medicine or engineering, where misrepresentation can distort findings. Language Learning Research by Reducto and Idul (2025) shows that individualised feedback and technology-enhanced instruction improve students’ sentence construction in EFL contexts, helping them transition from simple to more sophisticated academic writing. 6.0 Case Studies Morocco: Lahoual and Hdouch (2025) found that EFL students improved their use of complex sentences through structured peer feedback, which encouraged active reflection on syntax. Bangladesh: Rahman (2025) showed that targeted grammar teaching reduced common errors in undergraduate engineering students’ academic writing. Indonesia: Sukmawati (2025) demonstrated that students using ChatGPT as a writing assistant produced more coherent and structurally varied assignments, improving clarity. These case studies underline that effective sentence building combines traditional grammar instruction, practice, and digital innovation. 7.0 Developing Critical Thinking through Sentence Building Sentence construction is not just about grammar but also about critical thinking. Martinez (2025) points out that higher-order thinking is reflected in how sentences are structured to present nuanced arguments. For example: Descriptive: “The data shows a decline in sales.” Critical: “While the data indicates a decline in sales, this trend may reflect seasonal variation rather than long-term market instability.” Here, sentence building demonstrates the writer’s ability to interpret, evaluate, and synthesise information. Sentence building is an essential component of academic success in higher education. It requires mastery of grammar, syntax, sentence variety, and cohesion, supported by feedback and responsible use of technology. Research confirms that students who develop these skills produce more sophisticated, persuasive, and academically credible writing. Ultimately, effective sentence building is not just about forming grammatically correct statements but about crafting sentences that convey critical thought, originality, and … Read more

English for Academic Purposes (EAP): Concepts, Practices, and Challenges

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English for Academic Purposes (EAP) has developed as a significant field within applied linguistics and English language teaching. It is primarily concerned with preparing learners to use English effectively in academic contexts such as universities, research environments, and professional scholarly communication. EAP differs from General English in that it emphasises academic discourse, disciplinary-specific communication, and the development of critical literacy skills required for higher education. As globalisation and internationalisation of education have increased, EAP has become essential for students navigating English-medium institutions worldwide (Hyland, 2006). This article explores the concepts, practices, and challenges of EAP, highlighting key pedagogical frameworks, contemporary debates, and practical examples, supported by scholarly literature and empirical research. 1.0 Defining English for Academic Purposes EAP is defined as the branch of English for Specific Purposes (ESP) that focuses on the language, skills, and practices necessary for academic study and scholarly communication (Jordan, 1997). While ESP caters to professional or vocational fields (e.g., English for Medical Purposes, English for Business), EAP specifically targets learners engaged in academic study. According to Hyland and Hamp-Lyons (2002), EAP encompasses not just language structures but also the academic literacies students require to participate effectively in higher education. A key feature of EAP is its needs-based orientation. Unlike general English courses, EAP begins with a detailed needs analysis to identify the specific linguistic and cognitive requirements of learners within their disciplines (Dudley-Evans & St John, 1998). For example, science students may need to focus on reading research articles and writing laboratory reports, whereas humanities students may require training in essay writing and critical analysis. 2.0 Pedagogical Approaches in EAP 2.1 Skills-based Approach The skills-based approach emphasises developing the four core skills—reading, writing, listening, and speaking—for academic contexts. This includes critical reading, note-taking, academic writing, and seminar participation (Minarsih, Tyas & Herlina, 2025). 2.2 Genre-based Approach The genre approach, heavily influenced by systemic functional linguistics, examines the textual and rhetorical structures of academic discourse. Hyland (2004) argues that genre pedagogy empowers learners by exposing them to authentic models of academic texts, such as dissertations, journal articles, and conference abstracts. 2.3 Corpus-based Approach Corpus linguistics has enabled teachers to use large databases of academic texts to analyse recurrent patterns in academic writing. For instance, research shows that signalling nouns, hedging, and nominalisation are key features of dissertation writing (Yang, 2025). 2.4 Online and Blended Learning The rise of digital education has transformed EAP delivery. Bao (2025) highlights that EAP MOOCs and online writing programmes have expanded access, though challenges remain in maintaining engagement and providing individualised feedback. 3.0 Critical Literacy and EAP EAP is not limited to teaching grammar or vocabulary but emphasises critical literacy—the ability to question sources, evaluate arguments, and construct academic knowledge (Nguyen & Trinh, 2025). Critical reading strategies are central to EAP courses, equipping students to navigate dense, discipline-specific materials. For example, a student in engineering must critically interpret research data, while a literature student may analyse theoretical frameworks. Wong (2025) provides a critical EAP perspective, noting that EAP is inherently linked to issues of identity, power, and resistance. By teaching academic English, institutions may implicitly promote certain cultural or ideological values, raising concerns about linguistic imperialism. Thus, educators must balance language training with respect for students’ linguistic and cultural backgrounds. 4.0 The Role of EAP Educators EAP practitioners occupy a unique position within universities. They are often seen as support staff rather than disciplinary experts, which may undermine their professional identity. Taylorson, Mavor and Miles (2025) argue that applying a psychological lens reveals the identity challenges faced by EAP educators, who negotiate between language pedagogy and disciplinary authority. Professional development and collaboration with subject specialists can strengthen EAP provision. For example, joint modules between EAP lecturers and faculty members in engineering or business can provide students with both linguistic scaffolding and disciplinary knowledge. 5.0 EAP Across Disciplines A major challenge in EAP is addressing disciplinary variation. Research shows that science textbooks employ different grammatical and lexical structures compared to humanities materials (Yang, Lan & Huang, 2025). Similarly, English for Medical Purposes requires specialised vocabulary, diagnostic communication skills, and familiarity with medical discourse (Aarvidurai, Anandhan & Thiyagarajan, 2025). Therefore, effective EAP must be discipline-sensitive, recognising that academic communication is not homogeneous but shaped by the epistemological norms of different fields. 6.0 Technological Integration in EAP The integration of digital tools has reshaped EAP teaching. Leva et al. (2025) note that translation students increasingly rely on university-provided tools for academic tasks. Similarly, Shalhoub and Kunt (2025) highlight that online distance learning (ODL) during COVID-19 accelerated the adoption of platforms such as Moodle and Zoom for EAP instruction. While these tools increase accessibility, they also pose challenges in terms of digital literacy and the risk of over-reliance on AI-based translation or writing tools, which may undermine the development of independent academic skills. 7.0 Challenges in EAP 7.1 Cultural Diversity – Students from varied linguistic and cultural backgrounds face different barriers to academic integration. 7.2 Assessment – Measuring EAP outcomes remains complex. Traditional exams may not capture skills such as critical thinking or collaborative learning. 7.3 AI and Academic Integrity – With tools like ChatGPT being widely used, concerns have arisen regarding plagiarism and authenticity of student work (Prykhodchenko et al., 2025). 7.4 Resource Inequality – Access to high-quality EAP instruction is uneven globally, disadvantaging students in less-resourced institutions. EAP plays a vital role in supporting students’ academic success by equipping them with the necessary linguistic, cognitive, and critical skills to thrive in higher education. Its needs-based, discipline-sensitive, and literacy-oriented approaches ensure that it remains dynamic and adaptable to global academic trends. However, challenges such as professional recognition, digital integration, and academic integrity require continuous attention. Ultimately, EAP is not merely about mastering English; it is about gaining access to academic communities of practice, shaping professional identities, and engaging critically with knowledge production in a globalised world. References Aarvidurai, E., Anandhan, H., & Thiyagarajan, T.K. (2025). Education as panacea: Paradigm shift in assessing the English for medical purposes needs and learning preferences of … Read more

Business Planning and Strategy: Transforming Opportunities into Profitable Ventures

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An effective business planning and strategy is central to converting promising opportunities into profitable ventures. It serves as a structured document that outlines a company’s vision, mission, objectives, competitive positioning, operations, and financial forecasts (Barringer & Ireland, 2016). However, business planning goes beyond documentation. It is deeply rooted in strategic management, where firms align their internal resources with external market opportunities (Hitt et al., 2008). In today’s fast-paced and digitally transformed environment, methods such as the lean start-up approach (Ries, 2011) and the integration of analytics (Ezeife et al., 2024) play a crucial role in ensuring that plans are adaptable, evidence-based, and profit-oriented. This article explores how business planning and strategy enable organisations to transform opportunities into sustainable profits, supported by theoretical frameworks, practical examples, and current research. 1.0 The Role of Business Planning in Opportunity Transformation A business plan functions as both a roadmap and a communication tool. It clarifies strategic direction, reduces uncertainty, and attracts investors, lenders, and partners (Barringer & Ireland, 2016). According to Borcosi (2025), structured planning is associated with higher levels of employee training, efficiency, and operational profitability. Furthermore, Rezvani et al. (2025) emphasise that planning facilitates entrepreneurial entry by systematically identifying opportunities and mitigating risks. For example, Dropbox successfully validated its idea by releasing a demo video before developing the full product (Ries, 2011). This demonstrates how structured yet flexible planning can reduce upfront costs while gauging market demand. 2.0 Strategic Management as a Profitability Driver Strategic management ensures that firms deploy resources to capture opportunities effectively. Hitt et al. (2008) argue that long-term profitability depends on the alignment of resources, competitive advantage, and environmental scanning. In line with this, Ezeife et al. (2024) propose integrating predictive analytics into strategic decision-making, which boosts profitability and enhances business longevity. Similarly, Jadhav et al. (2025) highlight the emergence of green and sustainable business models, where strategic planning incorporates environmental and social value creation alongside profit objectives. Firms that integrate sustainability into planning, such as Unilever’s Sustainable Living Plan, gain both competitive advantage and customer loyalty. 3.0 The Lean Start-up and Iterative Planning The lean start-up methodology challenges the traditional approach of producing detailed long-term plans upfront. Ries (2011) stresses that businesses should experiment, measure, and learn through minimum viable products (MVPs). This iterative process enables firms to validate assumptions early and pivot when necessary. Khan et al. (2025) demonstrate how firms use machine learning and optimisation models to cut costs and increase profitability in logistics. By combining lean principles with advanced analytics, businesses achieve agility while maximising financial outcomes. 4.0 Digital Transformation and Data-Driven Strategy Digitalisation has fundamentally transformed business planning and execution. According to Vanani et al. (2024), digital transformation technologies—including AI, big data, and advanced analytics—improve decision-making, reduce risks, and enhance profitability. Borcosi (2025) further observes that digital management practices adopted by SMEs allow them to scale operations globally. Human Resource (HR) practices are also key. Ojakorotu (2025) notes that HR analytics informs workforce strategies, aligning talent management with profitability objectives. This data-driven alignment ensures that the human capital dimension supports broader strategic goals. 5.0 Financial Planning and Risk Management Effective planning also requires financial forecasting and risk analysis. According to Mansour and Vadell (2025), green finance and energy transition frameworks demonstrate how integrating financial planning with sustainable strategies reduces risks while ensuring long-term viability. In hospitality, Golja et al. (2024) show how investment models in Croatian hotels emphasise detailed planning to balance customer experience with profitability metrics. Similarly, Shelby (2025) stresses how ownership transitions planned under employee ownership models can sustain profitability while building long-term resilience. 6.0 Case Studies in Strategic Planning Success Several industries provide examples of how planning and strategy transform opportunities: Tourism and Hospitality: Zhang et al. (2025) analyse immigrant entrepreneurs in Tasmania who used detailed market analysis and business planning to create viable ventures in tourism despite initial barriers. Food and Beverage: Burbar et al. (2025) illustrate how Hamburger Restaurants in Palestine leveraged entrepreneurial planning to thrive in challenging environments. Technology Start-ups: Dropbox’s MVP strategy (Ries, 2011) remains a benchmark for cost-effective opportunity validation. Sustainable Enterprises: Jadhav et al. (2025) highlight green models where sustainability integration enhances long-term competitiveness. These examples show that regardless of industry, the core elements of planning, adaptability, and strategic alignment underpin success. 7.0 Challenges in Transforming Opportunities into Profitable Ventures Despite its advantages, business planning faces challenges: Uncertainty in dynamic markets: Predicting customer preferences in rapidly evolving industries like technology remains difficult (Vanani et al., 2024). Overplanning and rigidity: Excessive focus on detailed long-term projections may hinder responsiveness (Ries, 2011). Digital adoption barriers: SMEs often struggle to access resources for advanced digital transformation (Borcosi, 2025). Sustainability trade-offs: Balancing short-term profit with long-term environmental goals is complex (Mansour & Vadell, 2025). Addressing these challenges requires adaptive strategy frameworks, continuous learning, and flexible financing. Business planning and strategy remain indispensable tools for converting opportunities into profitable ventures. They combine vision setting, strategic resource allocation, financial forecasting, and digital adoption with lean experimentation and sustainability considerations. Evidence from multiple industries confirms that structured yet adaptive planning enhances profitability, resilience, and long-term growth. As businesses face increasing uncertainty, adopting data-driven, iterative, and sustainability-focused approaches will be crucial for success in transforming opportunities into lasting profitability. References Barringer, B. R. & Ireland, R. D. (2016). Entrepreneurship: Successfully Launching New Ventures. 5th ed. Harlow: Pearson. Borcosi, B. C. (2025). The trend in management–digital management and how it is implemented by SMEs. Annals – Economy Series, University of Targu Jiu. Available at: https://www.utgjiu.ro/revista/ec/pdf/2025-01/12_Borcosi.pdf. Burbar, M. Y., Jaber, R. Y. & Shkukani, S. J. (2025). Entrepreneurship, start-ups, and business success: Hunger’s Hamburger Restaurant in Palestine. Springer. Ezeife, E., Eyeregba, M. E. & Mokogwu, C. (2024). Integrating predictive analytics into strategic decision-making. World Journal of Management. Available at: https://www.researchgate.net/publication/386336187. Golja, T., Kukurin, Ž. & Prevolšek, D. (2024). Razvojne strategije u hotelijerstvu. CroRIS. Available at: https://www.croris.hr/crosbi/publikacija/resolve/croris/857999. Hitt, M. A., Ireland, R. D. & Hoskisson, R. E. (2008). Strategic Management: Competitiveness and Globalisation. 8th ed. Mason, OH: South-Western. Jadhav, H. L., Pandey, P. P. & … Read more

Entrepreneurship: Opportunity Recognition and Idea Generation

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Opportunity recognition is central to entrepreneurship, as it involves identifying gaps in markets, unmet consumer needs, or emerging trends and translating them into viable ventures (Montiel-Campos, 2023). Without this skill, entrepreneurial activity would lack direction and purpose. The process is often described as entrepreneurial alertness—the ability to scan environments, connect disparate pieces of information, and envision new opportunities (Ardichvili et al., 2003). Famous ventures such as Airbnb and Uber demonstrate the power of opportunity recognition, having emerged from inefficiencies in hospitality and transport sectors. This article examines opportunity recognition and idea generation, highlighting theoretical foundations, influencing factors, and practical examples. 1.0 The Nature of Opportunity Recognition Scholars conceptualise entrepreneurial opportunities as situations where new goods, services, or methods of production can be introduced and sold at greater than their cost (Shane & Venkataraman, 2000). Recognising such opportunities requires not only creativity but also contextual awareness. Baron (2006) argues that entrepreneurs employ pattern recognition, “connecting the dots” between emerging technologies, consumer frustrations, and industry shifts. For example, Spotify recognised the opportunity in the frustration caused by music piracy and declining CD sales, introducing a legal, convenient streaming service. This illustrates that opportunities often arise at the intersection of consumer pain points and technological change. 2.0 Entrepreneurial Alertness A central concept in opportunity recognition is entrepreneurial alertness—the readiness to notice new opportunities without deliberate search (Tang et al., 2012). It involves three dimensions: Scanning and searching for information – keeping track of market trends. Association and connection – combining unrelated information to form new insights. Evaluation and judgment – deciding whether the opportunity is viable. Elon Musk, for example, displayed entrepreneurial alertness by perceiving opportunities in both the electric vehicle and space exploration industries, where traditional players saw insurmountable risks. 3.0 Influencing Factors in Opportunity Recognition Research shows that opportunity recognition is shaped by several factors: 3.1 Prior Knowledge Individuals draw on prior knowledge when recognising opportunities. Shane (2000) found that entrepreneurs with specialised industry knowledge are better positioned to perceive new possibilities. For instance, Jeff Bezos’ experience in finance and e-commerce enabled him to conceptualise Amazon. 3.2 Social Networks Social capital is crucial in opportunity recognition. Aldrich and Zimmer (1986) argue that opportunities are often discovered through networks, where information flows across weak ties. LinkedIn itself emerged as an entrepreneurial opportunity through recognising the value of professional networking online. 3.3 Cognitive Styles Gómez-Gras and Mira-Solves (2010) highlight that entrepreneurs with intuitive and innovative cognitive styles are more likely to identify opportunities than those with purely analytical approaches. Pattern recognition and creative problem-solving play significant roles in bridging market gaps. 3.4 Environmental Dynamism Puhakka (2002) emphasises that opportunities emerge more frequently in dynamic environments. For example, the COVID-19 pandemic accelerated recognition of opportunities in remote work platforms such as Zoom, which saw exponential growth. 4.0 The Role of Idea Generation While recognising opportunities is crucial, idea generation translates recognition into actionable business concepts. Idea generation is both a creative and systematic process involving brainstorming, experimentation, and iteration (Gielnik et al., 2012). These factors below are critical for idea generation: 4.1 Creativity and Innovation Creativity is central to idea generation. Entrepreneurs must generate multiple solutions before selecting viable ones (Hansen et al., 2011). For example, the founders of Airbnb initially experimented with different concepts before settling on peer-to-peer lodging. 4.2 Organisational Learning Lumpkin and Lichtenstein (2005) stress that organisational learning enhances opportunity recognition and idea generation. Firms that foster learning cultures, such as Google, allow employees to explore side projects—leading to products like Gmail. 4.3 Diversity of Information Research shows that diverse information sources foster novel idea generation. Gielnik et al. (2012) found that individuals exposed to multiple industries and knowledge bases generated more innovative business ideas. 5.0 Models of Opportunity Recognition Several models attempt to explain how entrepreneurs identify and develop opportunities: Discovery Theory – Opportunities exist independently and are discovered through search (Shane & Venkataraman, 2000). Creation Theory – Opportunities are constructed through entrepreneur–environment interaction (Alvarez & Barney, 2007). Pattern Recognition Model – Entrepreneurs identify patterns across information and events to form opportunities (Baron, 2006). For example, Tesla aligns with creation theory, as opportunities in electric mobility were not obvious until entrepreneurs like Musk actively shaped consumer and regulatory environments. 6.0 Challenges in Opportunity Recognition Despite its importance, entrepreneurs face barriers in recognising and acting upon opportunities: Cognitive biases: Overconfidence or confirmation bias can lead to pursuing poor ideas (Mitchell et al., 2007). Resource constraints: Recognised opportunities may not be pursued without adequate funding or networks. Market uncertainty: Opportunities that appear attractive may collapse due to unforeseen changes. The case of Segway illustrates these challenges: despite innovative technology, the product failed due to misjudged consumer demand and pricing. 7.0 Practical Approaches to Enhancing Opportunity Recognition 7.1 Entrepreneurial Education Education plays a vital role in developing entrepreneurial alertness. Heinonen et al. (2011) highlight the importance of creativity training and experiential learning in fostering opportunity recognition skills. Business schools now use simulations and incubators to expose students to real-world opportunities. 7.2 Use of Technology Digital tools enhance opportunity recognition by providing access to big data, social media trends, and predictive analytics (George et al., 2016). For example, start-ups use platforms like Google Trends to anticipate consumer interests. 7.3 Collaboration and Networking Engaging in entrepreneurial ecosystems helps entrepreneurs share ideas and gain exposure to opportunities. Silicon Valley remains a prime example where dense networks and knowledge spillovers fuel opportunity recognition and innovation (Spiegler & Halberstadt, 2018). 8.0 Real-World Examples Zoom: Recognised an opportunity in seamless remote communication long before the pandemic but capitalised massively when environmental shifts increased demand. Airbnb: Saw potential in underutilised housing and consumer demand for cheaper, authentic travel experiences. Tesla: Recognised environmental and regulatory trends favouring clean energy, turning electric vehicles into a mass-market product. These examples reinforce that opportunity recognition and idea generation are not isolated skills but interconnected with market dynamics, social networks, and creative cognition. Opportunity recognition and idea generation lie at the core of entrepreneurship. Entrepreneurs rely on alertness, prior knowledge, networks, and creativity to … Read more

The Entrepreneurial Mindset and Characteristics

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The concept of the entrepreneurial mindset has gained significant attention in both academic research and practice, as it encapsulates the cognitive, behavioural, and emotional orientations that enable individuals to identify opportunities, take risks, and create value in uncertain environments. Unlike traditional employees who may seek stability, entrepreneurs thrive in ambiguity and dynamic contexts, often turning volatility into opportunity (Bailetti, 2012). This article explores the entrepreneurial mindset, its core characteristics, and its importance in today’s rapidly evolving business landscape. 1.0 Defining the Entrepreneurial Mindset The entrepreneurial mindset refers to a way of thinking and acting that allows individuals to identify opportunities, innovate, and persevere in the face of challenges. Ratten (2010) describes it as being characterised by creativity, resilience, adaptability, and risk-taking. More recently, Pidduck et al. (2023) conceptualise it as a blend of dispositional beliefs, opportunity beliefs, and entrepreneurial behaviours, suggesting that mindset is not only about traits but also about learned patterns of thought and action. Educational institutions have increasingly recognised the importance of cultivating an entrepreneurial mindset among students, even those not intending to start businesses, because it enhances problem-solving skills, critical thinking, and innovation capacity (Li et al., 2016). For example, the UK has embedded entrepreneurship education into curricula to foster employability and adaptability in graduates (Fayolle & Loi, 2021). 2.0 Key Characteristics of the Entrepreneurial Mindset 2.1 Creativity and Innovation Orientation Entrepreneurs are often described as creative problem-solvers who challenge the status quo and generate novel ideas. Creativity provides the foundation for opportunity recognition, while innovation translates those ideas into viable solutions (Tidd & Bessant, 2020). For instance, Steve Jobs exemplified creativity by integrating design, technology, and user experience, leading to revolutionary products such as the iPhone. Research confirms that creativity and openness to experience are positively linked with entrepreneurial intentions (Al-Ghazali et al., 2022). 2.2 Resilience and Persistence Resilience refers to the ability to recover from setbacks and maintain motivation in the face of adversity. Entrepreneurs frequently experience failure, yet those with a resilient mindset use failure as a learning opportunity (Kuratko et al., 2021). Elon Musk provides a powerful example of resilience, overcoming multiple failures in ventures such as SpaceX before achieving success. According to Ratten (2023), resilience is one of the most critical characteristics in navigating the uncertainties of entrepreneurship. 2.3 Adaptability and Flexibility The entrepreneurial environment is marked by rapid change. Entrepreneurs must continuously adapt their strategies, business models, and products to evolving market needs. Adaptability is seen in companies such as Netflix, which transitioned from DVD rentals to online streaming, later moving into original content production to sustain its competitive advantage. Research highlights that adaptability is associated with opportunity exploitation and firm survival (Naumann, 2017). 2.4 Risk-Taking Propensity A willingness to take calculated risks is a hallmark of entrepreneurship. While entrepreneurs do not engage in reckless behaviour, they are more comfortable with uncertainty and more inclined to pursue high-risk, high-reward opportunities compared to non-entrepreneurs (Mathisen & Arnulf, 2014). For example, Richard Branson consistently pursued ventures in diverse industries, from airlines to space tourism, driven by a risk-tolerant mindset. 2.5 Self-Efficacy Self-efficacy—the belief in one’s capability to perform tasks and achieve goals—is strongly linked to entrepreneurial success (Bandura, 1997; Carlsson et al., 2013). Entrepreneurs with high self-efficacy are more likely to pursue opportunities, persist in adversity, and influence others. Studies show that self-efficacy predicts entrepreneurial intentions across cultures (Kwapisz et al., 2022). 2.6 Proactiveness Entrepreneurs are typically proactive, anticipating future trends and acting before competitors. Proactiveness involves scanning environments for signals and positioning ventures strategically (Daspit et al., 2023). For example, Jeff Bezos anticipated the potential of e-commerce long before it became mainstream, positioning Amazon as a global leader. 2.7 Opportunity Recognition At the core of entrepreneurship lies the ability to identify and evaluate opportunities. Ardichvili et al. (2003) argue that opportunity recognition is influenced by prior knowledge, social networks, and alertness. Entrepreneurs often see connections where others see chaos, turning market gaps into viable ventures. For instance, Airbnb recognised the underutilisation of private homes as a lodging resource, creating an entirely new market. 3.0 Entrepreneurial Mindset in Practice The entrepreneurial mindset is not confined to business founders. Corporate managers, freelancers, and even public sector leaders benefit from entrepreneurial thinking. This has led to the concept of intrapreneurship, where employees apply entrepreneurial characteristics within established organisations to drive innovation (Hisrich & Kearney, 2014). Google’s “20% time” initiative, which allowed employees to pursue personal projects, gave rise to products like Gmail—demonstrating intrapreneurial creativity and autonomy. Moreover, studies show that cultivating an entrepreneurial mindset enhances employability and career adaptability. For example, Ferreira & Gonçalves (2023) found that individuals with entrepreneurial traits such as initiative and resilience are better positioned to thrive in start-ups and fast-paced industries. 4.0 Educational and Policy Implications Given its importance, fostering an entrepreneurial mindset has become a policy and educational priority. Programmes such as the European Commission’s “EntreComp” framework highlight the need for teaching entrepreneurial competences across education levels. Fayolle & Loi (2021) argue that experiential learning, such as business simulations, start-up incubators, and mentoring, is more effective than traditional lectures in developing entrepreneurial characteristics. Governments and institutions can also play a role by creating ecosystems that support entrepreneurial development. Start-up hubs like Silicon Valley thrive not only because of individual entrepreneurs but also due to supportive networks of universities, investors, and policies that encourage risk-taking and innovation (Celestin & Vanitha, 2018). 5.0 Critiques and Limitations of the Entrepreneurial Mindset Concept While the entrepreneurial mindset is widely celebrated, scholars caution against oversimplification. Some argue that mindset is context-dependent and cannot be reduced to a fixed set of traits (Belousova & Hattenberg, 2021). Others highlight the potential downsides: excessive risk-taking may lead to financial instability, while relentless proactiveness can cause burnout (Kuratko et al., 2021). Additionally, cultural differences influence how entrepreneurial traits are expressed. For example, risk-taking may be more valued in Western contexts but less accepted in collectivist cultures where failure carries higher social costs (Srivastava, 2025). Therefore, a nuanced understanding of entrepreneurial mindset must consider individual, cultural, and … Read more

Role of Management Information Systems (MIS) at the Operational Level: Enhancing Efficiency and Accuracy

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Management Information Systems (MIS) are essential for supporting organisational decision-making across all levels of management. At the operational level, MIS plays a pivotal role in automating routine processes, reducing human error, and improving efficiency in areas such as payroll, inventory management, and sales reporting (Zorina & Zorin, 2025). By streamlining these activities, organisations free employees to focus on higher-value tasks, ultimately improving productivity. This article examines operational-level MIS, focusing on its applications in payroll, sales reporting, and inventory management. It also explores Tesco’s implementation of MIS and evaluates the challenges and future directions of operational-level systems. Understanding Operational-Level MIS At the operational level, MIS is concerned with structured, repetitive tasks that underpin day-to-day business activities. These tasks typically involve transaction processing systems (TPS), which record and manage transactions such as payroll entries, point-of-sale (POS) data, and inventory updates (Laudon & Laudon, 2022). The benefits of operational-level MIS include: Accuracy – automation reduces manual input errors. Efficiency – tasks such as payroll calculation are completed more quickly. Cost Savings – less reliance on manual labour. Timeliness – managers receive near real-time updates on key operational metrics. Payroll Systems One of the most common applications of MIS at the operational level is in payroll management. Payroll systems automate salary calculations, tax deductions, and benefits management, ensuring compliance with employment regulations and consistency in employee compensation (Whiteley, 2017). For example, large retailers such as Tesco employ payroll systems that integrate with timekeeping and attendance records, ensuring accurate wage calculations for thousands of employees (Meng, 2024). This eliminates errors associated with manual data entry and reduces disputes between staff and employers. Sales Reporting Sales reporting systems form another critical application of operational-level MIS. Through POS systems, retailers automatically record every transaction, which is then aggregated into daily, weekly, and monthly sales reports (Saani, 2019). These reports enable managers to identify top-selling products, measure employee performance, and detect anomalies such as theft or errors. In practice, Tesco has integrated sales reporting into its ERP and MIS platforms, providing real-time visibility into store-level and company-wide performance (Kukreja & Gupta, 2016). This allows Tesco to detect patterns such as regional variations in demand, enabling more precise marketing campaigns. Inventory Management Perhaps the most significant contribution of operational-level MIS lies in inventory management. Inventory systems automate stock tracking, replenishment, and forecasting to avoid overstocking or stockouts. Advanced systems employ techniques such as Just-in-Time (JIT) and Economic Order Quantity (EOQ) to optimise stock levels (Meng, 2024). Tesco is a prime example of MIS in inventory management. The company uses automated systems to track product sales in real time, triggering reordering processes to avoid shortages (Potter & Disney, 2010). This system is supported by data-driven supply chain models, helping Tesco mitigate the bullwhip effect and maintain customer satisfaction. Tesco: A Case Study in Operational-Level MIS Tesco’s integration of MIS across its operations demonstrates how automation enhances efficiency: Payroll – Tesco’s payroll system integrates employee attendance data to automate wage processing, ensuring compliance with wage laws (Kukreja & Gupta, 2016). Sales Reporting – POS systems at Tesco stores feed into a central MIS, generating daily sales summaries that inform short-term marketing and staffing decisions (Farooq, 2021). Inventory Management – Tesco leverages advanced replenishment systems, reducing stockouts and waste by ensuring products are restocked based on real-time demand (Potter & Disney, 2010). By combining these systems, Tesco has achieved operational resilience, improved accuracy, and reduced costs, positioning itself as a leader in retail efficiency. Challenges in Operational-Level MIS Despite its benefits, operational-level MIS faces several challenges: Data Quality – inaccurate input can still compromise the system’s output (Ring & Tigert, 2001). Integration Issues – linking payroll, sales, and inventory systems across multiple locations is complex. Cybersecurity Risks – sensitive payroll and sales data must be protected against breaches (Woods, 2022). Cost – the initial investment in MIS infrastructure can be significant, particularly for small businesses (Mukhopadhyay, 2009). User Resistance – staff may resist system adoption if adequate training is not provided. For Tesco, managing such challenges involves continuous investment in IT infrastructure, employee training, and robust data governance strategies (Rahman et al., 2025). Future Directions of Operational-Level MIS The future of operational MIS will be shaped by: Artificial Intelligence (AI) – enabling predictive payroll analytics (e.g., turnover forecasting) and inventory forecasting (Stone & Hollier, 2000). Cloud Computing – offering scalable and cost-effective MIS solutions for global retailers (Luftman & Kempaiah, 2008).  IoT Integration – using smart shelves and sensors to update inventory systems in real time (Richards, 2017).  Blockchain – providing secure payroll processing and transparent supply chains (Whiteley, 2017). Retailers like Tesco are already exploring these innovations, integrating AI-driven demand forecasting and IoT-based stock management into their MIS to further enhance efficiency and reduce costs. Operational-level MIS plays a vital role in automating routine processes such as payroll, sales reporting, and inventory management, enabling organisations to reduce errors, improve efficiency, and focus on higher-value tasks. Tesco serves as a leading case study, demonstrating how MIS can transform retail operations through automation and data-driven decision-making. While challenges remain, the integration of emerging technologies such as AI, IoT, and blockchain promises to further advance operational-level MIS. For businesses operating in highly competitive environments, mastering these systems is no longer optional but a necessity for survival and growth. References Farooq, M.U. & Comanoiu, A.M.B. (2021) Strategic Synergy: Leveraging Big Data and Information Systems for Competitive Advantage in the Digital Age. York St John University. [Available at: https://ray.yorksj.ac.uk/id/eprint/11156/]. Kukreja, G. & Gupta, S. (2016) Tesco Accounting Misstatements: Myopic Ideologies Overshadowing Larger Organisational Interests. SDMIMD Journal of Management. [Available at: https://www.researchgate.net/publication/309519208]. Laudon, K.C. & Laudon, J.P. (2022) Management Information Systems: Managing the Digital Firm. 17th ed. Pearson: Harlow. Luftman, J. & Kempaiah, R. (2008) Key Issues for IT Executives 2007. MIS Quarterly Executive, 7(2), pp. 91–99. Meng, S. (2024) Operation Management of Grocery Retailer in the UK: A Case of Tesco. Pacific International Journal. [Available at: https://rclss.com/pij/article/view/586]. Mukhopadhyay, J. (2009) Supply Chain Management: A Comparative Study Between Large Organised Food and Grocery Retailers in India. … Read more

Business Analytics: An Overview of Key Concepts and Applications

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In today’s data-driven economy, organisations are increasingly reliant on Business Analytics (BA) to make informed decisions, enhance efficiency, and gain competitive advantage. BA can be defined as the systematic exploration of data through statistical and computational methods to generate insights for decision-making (Evans, 2016). It is generally categorised into three types: descriptive analytics, predictive analytics, and prescriptive analytics, each serving a distinct but complementary role. This article explores the foundations of BA, its categories, tools, and techniques, as well as its applications across industries such as healthcare, retail, and aviation. It also considers the challenges of implementation and outlines future directions shaped by big data, machine learning (ML), and artificial intelligence (AI). The Core Types of Business Analytics Descriptive Analytics Descriptive analytics summarises past data to reveal patterns and trends. It involves reporting, visualisation, and statistical summaries, helping organisations understand what has already happened (Evans, 2016). For instance, a retailer may use sales dashboards to identify the best-performing product lines or seasonal demand fluctuations (Iyer & Iyer, 2025). Predictive Analytics Predictive analytics employs statistical modelling, regression analysis, and machine learning to forecast future outcomes (Vyas & Patel, 2025). Airlines such as British Airways use predictive analytics to forecast customer demand, optimise ticket pricing, and manage route profitability (Habib et al., 2025). Prescriptive Analytics Prescriptive analytics goes beyond forecasting by recommending optimal actions. It combines predictive models with optimisation algorithms to suggest strategies for maximising performance. For example, logistics firms use prescriptive analytics to determine the most efficient delivery routes, balancing costs and customer service (Joshua, 2025). Tools and Techniques in Business Analytics BA leverages a range of tools and methods, including: Data Mining – uncovering hidden patterns in large datasets. Statistical Modelling – using regression, correlation, and hypothesis testing for structured analysis (Wiranegara, 2025). Machine Learning (ML) – applying algorithms such as decision trees, clustering, and neural networks for classification and prediction (Cheriyan, 2025). Optimisation – solving mathematical models to determine the most efficient allocation of resources (Modalavalasa & Kali, 2025). Visualisation Tools – dashboards and BI platforms such as Tableau and Power BI, which present data in accessible forms (Jain, 2025). For instance, Microsoft uses Power BI dashboards to integrate sales, marketing, and customer service data, enhancing real-time decision-making (Jain, 2025). Applications Across Industries Retail and E-commerce Retailers like Tesco employ BA to track consumer purchasing patterns, manage supply chains, and personalise promotions (Rahman et al., 2025). Amazon applies predictive analytics to suggest products, improving cross-selling and upselling. Healthcare In healthcare, BA supports predictive modelling for disease outbreaks and patient risk analysis. Hospitals use prescriptive analytics to optimise resource allocation, such as ICU bed management during peak demand (Wu et al., 2025). Finance and Banking Financial institutions utilise BA for fraud detection, credit scoring, and risk management. Predictive models help banks anticipate loan defaults, while prescriptive models assist in portfolio optimisation (Thiyagarajan, 2025). Aviation Airlines like British Airways and Lufthansa use BA to optimise fuel consumption, flight schedules, and ticket pricing, significantly improving profitability and customer satisfaction (Habib et al., 2025). Marketing BA enables customer segmentation, campaign analysis, and sentiment analysis. Firms can track social media trends and customer feedback to shape marketing strategies (Ahmed, 2025). The Role of Big Data and AI in Business Analytics The rise of big data has revolutionised BA by enabling organisations to analyse massive, unstructured datasets from IoT devices, social media, and digital transactions (Modalavalasa & Kali, 2025). Artificial Intelligence (AI) enhances predictive and prescriptive analytics by enabling self-learning models capable of improving accuracy over time (Sabapathy, 2025). For example, Netflix uses AI-driven analytics to recommend content to users, driving engagement and retention. Similarly, in manufacturing, predictive maintenance powered by BA minimises downtime by forecasting equipment failures. Challenges in Business Analytics Despite its transformative potential, BA faces key challenges: Data Quality – poor data integrity can undermine analytical accuracy (Pham, 2025). Integration Issues – combining structured and unstructured data from multiple sources is complex. Privacy Concerns – stricter regulations such as GDPR necessitate ethical and compliant use of customer data (Khalique, 2025). Skills Gap – organisations face shortages of professionals proficient in both analytics tools and business strategy (Cheriyan, 2025). Cost and Complexity – BA infrastructure requires significant investment in technology and skilled staff (Drvarek, 2025). Future Directions The future of BA is shaped by emerging technologies and methodologies: AI-Powered Analytics – deeper integration of machine learning for autonomous decision-making (Iyer & Iyer, 2025). Real-Time Analytics – increasing demand for streaming analytics in sectors like finance and e-commerce (Wu et al., 2025). Explainable AI (XAI) – ensuring that advanced predictive models remain transparent and interpretable (Sabapathy, 2025). Cloud-Based Analytics – scalable, flexible solutions accessible to firms of all sizes (Mamun, 2025). Industry-Specific Analytics – tailored solutions for healthcare, retail, and manufacturing. For example, smart cities are applying BA integrated with IoT to improve traffic management, energy consumption, and environmental monitoring (Sudha et al., 2026). Business Analytics is a critical enabler of informed decision-making, innovation, and sustainable growth in modern organisations. By integrating descriptive, predictive, and prescriptive analytics, firms can harness data to understand historical performance, anticipate future challenges, and take proactive measures. From retail to healthcare and aviation, BA has transformed operational efficiency and customer experience. While challenges such as data quality, privacy, and skills gaps remain, the integration of AI, big data, and cloud computing promises to make BA more powerful, accessible, and impactful. For students and practitioners alike, understanding BA is essential for navigating the digital economy and leveraging data as a strategic asset. References Ahmed, S. (2025) Evolution and Importance of Business Analytics in Modern Business. ResearchGate. [Available at: https://www.researchgate.net/publication/379975634]. Cheriyan, V. (2025) Navigating the Analytics Landscape: A Comprehensive Guide for Recent Graduates. Journal of Computer Science and Technology. [Available at: https://al-kindipublishers.org/index.php/jcsts/article/view/9845]. Drvarek, S. (2025) Konceptualni model poslovne analitike korištenjem podatkovne analitike. Repozitorij Unin. [Available at: https://repozitorij.unin.hr/en/islandora/object/unin:7720]. Evans, J.R. (2016) Business Analytics: Methods, Models, and Decisions. 2nd ed. Pearson, Harlow. Habib, M.A., Mahmood, A., Ahmad, M. & Baig, S.A. (2025) Barriers to Adoption Industry 4.0 in Textile Sector. IEEE … Read more

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