Specifies the number of studies evaluated orselected
Steps, and targets of constructing a good review article are listed in Table 3 . To write a good review article the items in Table 3 should be implemented step by step. [ 11 – 13 ]
Steps of a systematic review
Formulation of researchable questions | Select answerable questions |
Disclosure of studies | Databases, and key words |
Evaluation of its quality | Quality criteria during selection of studies |
Synthesis | Methods interpretation, and synthesis of outcomes |
It might be helpful to divide the research question into components. The most prevalently used format for questions related to the treatment is PICO (P - Patient, Problem or Population; I-Intervention; C-appropriate Comparisons, and O-Outcome measures) procedure. For example In female patients (P) with stress urinary incontinence, comparisons (C) between transobturator, and retropubic midurethral tension-free band surgery (I) as for patients’ satisfaction (O).
In a systematic review on a focused question, methods of investigation used should be clearly specified.
Ideally, research methods, investigated databases, and key words should be described in the final report. Different databases are used dependent on the topic analyzed. In most of the clinical topics, Medline should be surveyed. However searching through Embase and CINAHL can be also appropriate.
While determining appropriate terms for surveying, PICO elements of the issue to be sought may guide the process. Since in general we are interested in more than one outcome, P, and I can be key elements. In this case we should think about synonyms of P, and I elements, and combine them with a conjunction AND.
One method which might alleviate the workload of surveying process is “methodological filter” which aims to find the best investigation method for each research question. A good example of this method can be found in PubMed interface of Medline. The Clinical Queries tool offers empirically developed filters for five different inquiries as guidelines for etiology, diagnosis, treatment, prognosis or clinical prediction.
As an indispensable component of the review process is to discriminate good, and bad quality researches from each other, and the outcomes should be based on better qualified researches, as far as possible. To achieve this goal you should know the best possible evidence for each type of question The first component of the quality is its general planning/design of the study. General planning/design of a cohort study, a case series or normal study demonstrates variations.
A hierarchy of evidence for different research questions is presented in Table 4 . However this hierarchy is only a first step. After you find good quality research articles, you won’t need to read all the rest of other articles which saves you tons of time. [ 14 ]
Determination of levels of evidence based on the type of the research question
I | Systematic review of Level II studies | Systematic review of Level II studies | Systematic review of Level II studies | Systematic review of Level II studies |
II | Randomized controlled study | Crross-sectional study in consecutive patients | Initial cohort study | Prospective cohort study |
III | One of the following: Non-randomized experimental study (ie. controlled pre-, and post-test intervention study) Comparative studies with concurrent control groups (observational study) (ie. cohort study, case-control study) | One of the following: Cross-sectional study in non-consecutive case series; diagnostic case-control study | One of the following: Untreated control group patients in a randomized controlled study, integrated cohort study | One of the following: Retrospective cohort study, case-control study (Note: these are most prevalently used types of etiological studies; for other alternatives, and interventional studies see Level III |
IV | Case series | Case series | Case series or cohort studies with patients at different stages of their disease states |
Rarely all researches arrive at the same conclusion. In this case a solution should be found. However it is risky to make a decision based on the votes of absolute majority. Indeed, a well-performed large scale study, and a weakly designed one are weighed on the same scale. Therefore, ideally a meta-analysis should be performed to solve apparent differences. Ideally, first of all, one should be focused on the largest, and higher quality study, then other studies should be compared with this basic study.
In conclusion, during writing process of a review article, the procedures to be achieved can be indicated as follows: 1) Get rid of fixed ideas, and obsessions from your head, and view the subject from a large perspective. 2) Research articles in the literature should be approached with a methodological, and critical attitude and 3) finally data should be explained in an attractive way.
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His hit single, “Houdini,” is the latest example of Eminem, for more than a decade now, successfully defying every aging critic and each disillusioned fan imploring him to grow up
This past month in hip-hop, in the immediate aftermath of Drake vs. Kendrick Lamar , there has been a minor surge of disagreeable releases met with widespread mockery. You had Drake once again employing his signature patois to spoof “Hey There Delilah,” of all songs, with “Wah Gwan Delilah,” a confoundingly goofy ode to Toronto from local parody rapper Snowd4y. You also had J. Cole launching into a characteristically mortifying series of sex raps, e.g., She gon’ chew on this stick like it’s Wrigley’s, in his guest verse on “Grippy” by Cash Cobain.
And then you had “Houdini,” the lead single from Eminem’s forthcoming album, The Death of Slim Shady (Coup de Grâce). “Houdini” is a song with plenty of controversial elements—from its pointedly retro beat to its mildly provocative punchline about Megan Thee Stallion —drawing all sorts of ridicule of Eminem on podcasts and TikTok. But now it’s the no. 2 song in the country. So what do those people know, really?
Eminem has long been a figure proudly out of step with the general direction of hip-hop. He’s a self-contained and self-sustaining musical continuity, and “Houdini” is a self-conscious throwback to his 2000s heyday. The song and its music video both extensively evoke “Without Me,” the biggest single from the biggest album of his career, The Eminem Show . The music video stages a generational clash of two versions of Eminem. There’s the supposedly washed-up Marshall Mathers, a bearded brunette who’s a bit too old to still be running around in superhero spandex, as “Rap Boy.” And then there’s the classic Eminem, the trailer park heartthrob with a bleached blond buzz cut wrapped in a bandana, wearing his signature white tee with baggy sweatpants, emerging dumbstruck (and rather impressively de-aged) from “a portal from 2002.” This Eminem is irritated by the sight of so many yuppies immersed in smartphones and VR. He doesn’t get emojis. He flips off his own daughters on FaceTime. He says he’ll “hit an 8-year-old in the face with a participation trophy , ” and so he does. Slim Shady must, but ultimately can’t, be stopped! Guess who’s back—back again—once more proving the latest reports of his irrelevance to be greatly exaggerated.
Disagreements about the musical worthiness of post-peak Eminem have in recent years become so heated, loaded, and polarizing that it’s now a minor culture-war conflict. Eminem’s the ultimate white rapper, with all the good grief that’s always entailed; there’s some sense that he’s perennially overrated by white fans who, on some level, see him as more accessible or relatable than Black rappers of a similar caliber—or higher. (I don’t agree—I think he’s properly rated as one of the most tremendously talented and rightly influential rappers of his generation—but that’s the argument you’ll hear.) His late-career success story is a peculiar one. Hip-hop is still a relatively young musical tradition, and many of its elder statesmen—Jay-Z, Kanye West, André 3000—become conspicuously more bougie and tasteful (in the scariest of scare quotes, especially as far as Kanye is concerned) in middle age. Eminem is the glaring counterexample: Here you have an artist who’s won great acclaim and amassed an even greater fortune in the course of a quarter century yet still specializes in low-brow punchlines and put-downs over no-frills production. Honestly—cards on the table—I relate to his detractors more, yet, on the merits, I’d say his stalwarts have the stronger argument here: Eminem sounds quaint to some because Eminem, shamelessly and so rather admirably, does something that no one else is really doing at this level, at this point, with this sort of consistency and self-assurance. He’s rapping for the love of the game, for fans who aren’t necessarily scrambling to keep up with the trap zeitgeist or the innovations of younger rappers.
Strangely, though, Eminem has been marketing The Death of Slim Shady , with its overwrought title, as the turning of a page. He ran a mock obituary for his alter ego in his hometown newspaper, the Detroit Free Press : “His complex and tortured existence has come to a close.” I’m inclined to view this setup as yet another one of his characteristically carnivalesque and ultimately meaningless gimmicks, because otherwise, it’s not immediately obvious to me what the “death” of Slim Shady is even meant to evoke on a metaphorical or metanarrative level. Classically, Slim Shady represented the bleakest thoughts and rudest impulses of Marshall Mathers, tormented by his alter ego as he struggled to become a good and sober father. Slim was a somewhat goofy caricature, sure, but nonetheless a powerful construct; Slim Shady and Marshall Mathers, together, formed a multifaceted persona who could prove at once trashy and poignant on earlier albums. Late-career Eminem is hardly so anguished. Ever since “Rap God”—the tongue-twisting, spitfire single released in 2013—and The Marshall Mathers LP 2, he’s seemed mostly concerned with preserving his chops and proving he still belongs in the pantheon, on the merits of his technique, after all these years. His skill as a lyricist now came with only a fraction of the rage and a mere echo of the old torments. You’d be forgiven for assuming that Slim Shady had already been killed off of this particular sitcom years ago.
Eminem, Slim Shady, Marshall—whatever you want to call him—for more than a decade now he’s successfully defied every aging critic and each disillusioned fan imploring him to grow up, to adapt to both the prevailing morality and popular drum patterns of contemporary hip-hop. “Houdini,” with its many anachronisms, is the strongest rap debut on the Hot 100 since “Not Like Us” and, if you can believe it, Eminem’s strongest single since 2013’s “The Monster”—a song featuring Rihanna with more than 1 billion streams on Spotify. Even his creative lulls would be other rappers’ commercial peaks. Revival, Kamikaze, Music to Be Murdered By —these are each chart-topping albums whose low-key success suggests some underrated wisdom in the rapper’s arrested development, even if the marketing for The Death of Slim Shady also suggests a need for some sort of reset at this stage. Clearly something is working here. Still, I’d love to see Eminem outdo not only his critics but also himself on his new album—to somehow emerge from the pocket dimension in which he’s flourished for more than a decade and reintegrate into the hip-hop zeitgeist, somehow, if only briefly, on his own terms. “Houdini” doesn’t give me much hope, but its success does give him the best opening he’s had in years. Either way, kill Slim Shady or don’t; you’re never killing Eminem.
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Petesy and Chuck discuss the UFC’s debut in Saudi Arabia, which saw Robert Whittaker dominate Ikram Aliskerov
Plus, the biggest news from this week’s Nintendo Direct, gaming’s stacked release schedule for 2024, and what the Switch 2’s launch lineup could look like
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The Football Fill-In is in Germany, and this time, we are breaking down the England vs. Denmark game
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This paper reviews the literature on agility and its relationship with organisational performance using a sample of 249 recent empirical studies from 1998 to February 2024. We find support for a relatively strong and consistent contribution of different aspects of agility to organisational performance. Our analysis highlights numerous salient issues in this literature in terms of the theoretical background, research design, and contextual factors in agility-performance research. On this basis, we propose relevant recommendations for future research to address these issues, specifically focusing on the role of the board of directors and their leadership in fostering organisational agility.
Avoid common mistakes on your manuscript.
Since the “Manifesto for Agile Software Development” was declared in 2001 (Highsmith 2011 ), the Agility concept and methodologies have migrated from a narrow area of the IT industry to a wide range of organisational applications. Agility has often been associated with startups and small and medium-sized companies but has recently been extended to large corporations. Due to the volatile, uncertain, complex, and ambiguous (VUCA) business environment combined with intense competition and threats from new startup radical growth, large firms are forced to change their status quo and their heavy and inflexible business and management models to quickly adapt to the rapidly changing environment. As such, embracing agility and leading with agility have become new norms and are essential for business survival (Rigby et al. 2016 ).
In recent years, the world economy has gone through unprecedented crises due to the COVID-19 pandemic, the tech and trade war between the US and China, the Ukraine-Russia war, and the most recent Gaza Strip conflict triggering the Red Sea marine crisis; this has intensified the need for organisations to develop more agile business models to weather environmental turbulence and economic downturns (McKinsey & Company 2020 ). Complexity and unpredictability are dominating rules, challenging traditional management methods that rely on well-order planning. As such, in today's business world, being agile is no longer optional—it is essential for a company to stay alive (Harraf et al. 2015 ).
The recent focus on organisational agility in both research and practice can also be tied to some common practices applied in both small and large companies. One example is the use of cross-functional teams with procedures such as SCRUM to work in harmony with customers and deliver what they expect in a timely and cost-efficient manner (Handscomb et al. 2019 ). Teams with members from different functions and disciplines work together to put customers first and respond swiftly to their requests, reducing the waiting time visible in hierarchal organisations. However, further research evidence is needed to examine whether and to what extent it is sufficient for such a practice to build organisational agility. This highlights the need for comprehensive literature reviews with scientific research insights to guide industry practitioners in the application of agile practices.
Organisational agility is often defined as the dynamic capability of an organisation to act and react to uncertainties and the ability to explore and exploit opportunities in the business environment (Overby et al. 2006 ; Roberts and Grover 2012 ). Since 2019, the number of publications on organisational agility in literature has increased notably. However, as this literature evolves, agility is conceptualised inconsistently. This is particularly problematic given that agility is a multidimensional concept that includes but is not limited to various aspects, such as manufacturing agility, strategic agility, supply chain agility, IT agility, marketing agility, and workforce agility (Walter 2021 ). Such disagreement among researchers regarding how agility should be defined and constructed has posed significant challenges for researchers and practitioners in this area moving forwards, making it difficult to build the literature upon previous findings, to generalise those findings in different contexts and to apply this concept in reality (Walter 2021 ). Thus, a comprehensive understanding of agility as an overarching concept, its antecedents, and its effects on organisational outcomes is needed (Walter 2021 ).
Agility is often considered beneficial to organisational performance. With a dynamic ability to weather rapid changes and turbulence, an agile organisation is believed to be in a better position to produce outcomes. However, some evidence shows that the organisational benefits of agility are dependent on a range of factors, including the types of agility and outcomes as the focus of interest and the conditions for agility to contribute to organisational outcomes (Wieland and Wallenburg 2012 ). For instance, agility is found to increase firm financial performance (Rafi et al. 2021 ) or boost innovation (Del Giudice et al. 2021 ). However, in their study, Chakravarty et al. ( 2013 ) found that only entrepreneurial agility—the proactive ability to anticipate and exploit market opportunities and challenges—can help achieve better financial performance, while such effects from reactive types of agility are not significant. Additionally, while researchers have devoted much attention to some aspects of agility, such as supply chain agility or strategic agility, other aspects of agility, such as workforce agility and marketing agility, are still underresearched (Ajgaonkar et al 2022 ; Gomes et al. 2020 ). This demonstrates the need for a comprehensive and systematic review of whether, how, and what agility can contribute to organisational outcomes.
Recent literature reviews in this area have elucidated how agility is measured, what contributes to agility and the impact of agility on organisational outcomes. However, these reviews either adopted a narrow focus on one aspect of agility, such as marketing agility, supply chain agility, or IT agility (Kalaignanam et al. 2021 ; Patel and Sambasivan 2021 ; Tallon et al. 2019 ), or failed to provide an in-depth analysis that focused exclusively on the contribution of agility to business outcomes (Walter 2021 ).
The lack of a consensus on the concept, measurements and association of organisational agility with critical business outcomes indicates the need for a systematic review of the literature to, first, bring together all different types of agility and examine their impact on different organisational outcomes; second, identify the intervening factors that affect this relationship; and third, provide implications for future research in this area. This paper addresses the abovementioned objectives with an overarching research question: What is the current status of the literature on organisational agility and organisational outcomes? Then, this question is broken down into five broad subquestions as follows:
How are organisational agility and organisational performance defined and measured?
What is the relationship between organisational agility and organisational outcomes?
Which theories are used to examine the relationship between organisational agility and organisational outcomes?
What are some possible mediators or moderators that affect the relationship between organisational agility and organisational outcomes?
What are the implications for future research on this topic?
To comprehensively review the literature on agility and organisational performance, this paper adopts the strategy of a systematic literature review to examine 249 empirical studies in this area from 1998 to February 2024. This paper makes two significant contributions to the literature in this field. First, it seeks to provide a comprehensive summary and a conceptual map of whether and how organisational agility affects organisational performance based on 26 years of empirical evidence on this topic. Second, it aims to identify the gaps in knowledge and propose possible directions for future research and practices in this area. The paper starts with an introduction of the research design, followed by a description of the research findings, and ends with a discussion and recommendations for future research.
This paper adopts the widely used systematic review methodology in literature review studies to collect and analyse data because it is comprehensive, transparent, evidence-based, and unbiased (Khan et al. 2003 ; Snyder 2019 ; Tranfield et al. 2003 ). Figure 1 explains the strategy and steps taken to conduct this literature review.
Systematic literature review strategy and procedure
Following Xiao and Watson ( 2019 ), Diaz Tautiva et al. ( 2024 ), Tranfield et al. ( 2003 ), the paper utilises a systematic strategy and review steps through the three main phases of (i) planning, (ii) data collection, and (iii) data extraction, synthesis, and reporting to ensure the replicability and transparency of the methodology and findings. In the planning phase, we formed the review framework by carefully crafting the research objectives and referring to existing systematic review frameworks. Through this process, we were able to determine the search criteria and the framework for data extraction and classification, as indicated in Fig. 1 .
The review framework is based on dimensions of agility, variable measurement, theoretical background, methodology, findings, and intervening factors, followed by a synthesis of a conceptual map (Walter 2021 ; Bhattacharjee and Sarkar 2022 ; Patel and Sambasivan 2021 ). This framework is well aligned with our research questions and objectives and is often used in other literature review papers (Walter 2021 ; Bhattacharjee and Sarkar 2022 ; Patel and Sambasivan 2021 ). By using this framework, we can then move to the next step, which involves identifying the knowledge gaps in the literature and proposing some directions for future research in the field.
Using the predetermined search criteria identified in the planning phase, we first conducted a general search on Web of Science, one of the largest coverage databases, and obtained a sample of 8107 papers. We used the filter function to include 1165 peer-reviewed articles that had full texts available, were written in English, and were published in the fields of business, economics, and management. Then, we screened the titles and abstracts and adopted further exclusion criteria, as shown in Fig. 1 . The final sample consists of 249 English peer-reviewed empirical articles on agility and organisational outcomes, with agility being one of the main variables of interest in studies that test the firm-level impact of agility in the business, economics, and management fields.
Three groups of coders performed the data extraction and grouping based on the predetermined criteria mentioned above. Discussion and moderation were conducted before each group carried out their tasks. The data were extracted into an Excel file and categorised into the following columns: article title, authors, year, journal, theories, sample size, sample type (cross-sectional or panel), independent variables, moderators and contextual variables, mediators, dependent variables, control variables, analytical approach, and findings.
3.1 descriptive analysis.
Table 1 summarises some key features of our data. In this dataset, agility is either the primary independent variable or a mediator that links inputs to outcomes. We also included other recent literature reviews and conceptual papers in this field to support our data analysis. Thus, our final data consist of 249 empirical studies, 39 literature reviews and conceptual studies, and seven other relevant studies in this area.
Figure 2 presents the distribution of 249 empirical studies on agility and outcomes from 1998 to February 2024, with a sharp increase in the number of publications in recent years since 2017. This indicates researchers’ growing interest in this area and reflects a timely research response to recent environmental and societal changes (Joyce 2021 ).
Publications by year from 1998 to February 2024
Table 2 provides an overview of different subtopics in agility and organisational outcomes research and shows that supply chain agility, organisational agility, and strategic agility are the most researched topics in this area. Other aspects of agility run from manufacturing/operational to marketing, business process, customer, workforce, IT and digital, market capitalising, project management, leadership, intellectual, R&D, social media, and value creation.
Table 3 elucidates how different types of agility are measured in the literature. There is no consensus on how agility should be defined and measured. As the most researched type of agility, supply chain agility has been captured based on one or multiple dimensions, such as customers, products, delivery, responsiveness to the environment, competitors, and partners (Mandal 2018 ; Charles et al. 2010 ), collaborative planning (Braunscheidel and Suresh 2009 ; Chiang et al. 2012 ), procurement/sourcing and distribution/logistics (Swafford et al. 2006 ). Other approaches to measuring agility focus more on organisational capabilities such as alertness, accessibility, decisiveness, swiftness, and flexibility (Gligor and Holcomb 2012 ) or internal processes such as network collaboration, information integration, process integration, customer demand responsiveness (Mirghafoori et al. 2017 ) or information sharing (Whitten et al. 2012 ).
Organisational agility has also been measured in different ways. While some pioneering studies consider organisational agility to be flexible (Sharifi and Zhang 1999 ), others reveal that organisational agility should be a broader concept (Vokurka and Fliedner 1998 ). Such a concept can be similar to organisational ambidexterity (Overby et al. 2006 ; Roberts and Grover 2012 ), can feature dynamic capability (Teece et al. 1997 ), or can represent an overall organisational framework (Doz and Kosonen 2008 ; Dyer and Shafer 1998 ). The three most popular dimensions of organisational agility—customers, operation and partnership—are drawn from the work of Tallon and Pinsonneault ( 2011 ). Other approaches capture the sensing capability and response capability of organisations (Overby et al. 2006 ) or have different focuses, including but not limited to internal capabilities (Sharifi and Zhang 1999 ), people (Pramono et al. 2021 ), business processes (Vaculík et al. 2018 ), or products and costs (Zheng et al. 2023 ).
Strategic agility is commonly measured based on strategic sensitivity, resource fluidity, leadership unity, or a combination of technology capability, collaborative innovation, organisational learning, and internal alignment (Clauss et al. 2021 ; Doz and Kosonen 2008 ). Another approach involves adopting the three key dimensions of agility from Tallon and Pinsonneault ( 2011 ) from a strategic perspective. Some other measurement approaches are presented in Table 3 .
Manufacturing agility has been examined as a system leveraged by a range of capabilities, including responsiveness, competency, flexibility and speed (Cao and Dowlatshahi 2005 ; Sharifi and Zhang 1999 ), or as an organisational competency (Jacobs et al. 2011 ). Some of the less popular types of agility, such as customer agility, are measured as customers’ sensing capabilities and customers’ response capabilities (Clauss et al. 2021 ; Doz and Kosonen 2008 ). Intellectual agility is captured as the level of business-related skills, the frequency of skills and knowledge updates, the perception of work tasks as a challenge or an opportunity to practice skills, and the willingness to apply alternative solutions when solving problems (Chen and Chiang 2011 ; Felipe et al. 2016 ; Sambamurthy et al. 2003 ).
Overall, the literature on agility offers a wide range of approaches to measuring organisational agility and other dimensions of agility. While traditional approaches such as those of Sharifi and Zhang ( 1999 ), Overby et al. ( 2006 ), or Tallon and Pinsonneault ( 2011 ) are widely used, the literature continues to evolve with newer and more innovative approaches to measure agility and its dimensions. On the one hand, it motivates researchers in this field to develop better and more comprehensive ways to capture agility. On the other hand, the lack of consistency in measuring agility makes it difficult for researchers to synthesise how agility and its dimensions are constructed and what organisations should focus on to be more agile. Thus, there is a lack of informed guidance for practitioners to build agility in their organisations.
Table 4 provides an overview of the various aspects of organisational outcomes and the ways in which they are measured. The literature indicates a wide range of organisational outcomes examined in the context of agility. Some popular approaches to measuring organisational outcomes include the use of a self-reported overall organisational performance indicator, the construction of a composite variable with multiple dimensions, or the use of multiple separate indicators to capture different aspects of performance, including but not limited to financial performance (accounting and market indicators), nonfinancial performance, environmental performance, operational performance and beyond (Kurniawan et al. 2021a , b ). Other aspects of organisational outcomes examined in the agility and organisational outcomes literature include supply chain performance, innovation, competitiveness, customer service performance, digital and technology performance, manufacturing and operation, sustainability, international performance, employees, marketing, and organisational capabilities.
The literature offers a diverse set of organisational outcomes in conjunction with agility. This allows researchers and practitioners to look at how agility affects organisations in different angles and layers from financial performance to organisational survival, operation, sustainability, capabilities, employee performance and well-being. However, methodologically, the literature reveals some flaws in measuring and constructing organisational performance. While the predominant use of composite variables helps capture an overall indicator of organisational performance, which eases the analysis process (Panda 2021 ), this approach lacks consideration of the separate impact of each aspect of performance, making it challenging to interpret the results and apply the findings to practice.
The literature also reveals that organisational outcomes are often measured as construct variables through reflective/self-report survey questions (Altay et al. 2018 ; Goncalves et al. 2020 ), which raises some concerns about data reliability and validity. Some other studies use quantitative variables based on secondary data (Gligor and Bozkurt 2021 ; Pereira et al. 2021 ) or examine both qualitative and quantitative performance variables. However, further tests should be adopted to ensure the consistency and congruence of these methods (Feizabadi et al. 2019 ; Gligor et al. 2020a , b ).
Table 5 provides a summary of relevant theories in this area of research. Despite the wide range of theories available in this domain, the use of theories in empirical research in this sample is still inadequate. Out of 249 empirical studies, 141 (56.6%) adopt single or multiple theoretical approaches to build their argument of the contribution of agility to organisational outcomes. However, 109 (43.8%) studies in the dataset did not explicitly utilise relevant theories to support their hypothesis development. Given this lack of solid theoretical frameworks, these studies cannot develop a logical and established view of how or why agility improves organisational outcomes, which might threaten the rigour of their research design and the strength of their argument.
Furthermore, Table 5 highlights a wide range of theories incorporated in this research domain, with the dynamic capabilities perspective and the resource-based view being the most widely used theoretical background. These two theoretical frameworks are often combined to provide a comprehensive understanding of the relationship between agility and outcomes (Jabarzadeh et al. 2022 ; Mikalef and Pateli 2017 ). The dynamic capabilities perspective emphasises the importance of perceiving and seizing valuable growth opportunities and the ability to transform the organisation to fit with these opportunities (Teece et al. 1997 ). However, the dynamic capabilities perspective is criticised for its limited explanation of how and to what extent organisations should achieve the abovementioned purposes (Ambler and Wilson 2006). The resource-based view focuses on analysing the internal resources of the enterprise as well as linking internal resources with the external environment to foster innovation and create competitive advantage (Sambamurthy et al. 2003 ). However, similar to dynamic capabilities theory, the resource-based view still has limited practicality (El Shafeey and Trott 2014 ). Therefore, future studies on firm performance and agility should be based on a multitheoretical approach to obtain a more comprehensive view of this relationship (Doz and Kosonen 2008 ; Dyer and Shafer 1998 ).
Figure 3 summarises the findings of the relationship between agility and performance. Evidence from the current literature elucidates the positive impact of agility on organisational performance, with 219 (87.9%) studies confirming the positive impact of different forms of agility on organisational outcomes. Twenty-seven studies reported mixed effects between agility and organisational outcomes, 2 studies found no significant relationship between agility and organisational outcomes, and 1 study showed a negative impact of organisational agility on the continuity of innovation projects in organisations.
The impact of agility on organisational performance
Overall, relatively strong and consistent results support the contribution of organisational agility to organisational outcomes, including overall organisational performance (Stei et al. 2024 ), financial and nonfinancial performance (i.e., Rafi et al. 2021 ), innovation (i.e., Goncalves et al. 2020 ), sustainability ( i.e., Lopez-Gamero et al. 2023 ), competitiveness (i.e., Mikalef and Pateli 2017 ), digital and technology transformation ( i.e., Ly 2023 ), international performance ( i.e., Nemkova 2017 ), and employee job performance ( i.e., Chung et al. 2014 ).
However, some studies still report mixed effects of organisational agility on organisational outcomes. Several factors contribute to this mixed effect. First, it depends on the type of inputs and outcomes in the models where organisational agility serves as a mediator or a main independent variable. For example, even though organisational agility is found to enhance radical innovation, it does not help incremental innovation, even under technological turbulence, according to a study conducted by Puriwat and Hoonsopon ( 2021 ). Organisational agility has been shown to translate firm knowledge management into competitive advantage. However, by taking a closer look at different forms of knowledge management, Corte-Real et al. ( 2017 ) found that organisational agility serves as a mediator only for the relationship between exogenous knowledge management and firm competitiveness but not for that between endogenous knowledge management or knowledge sharing partners. Another study confirmed that knowledge management improves organisational agility, which in turn strengthens firm competitive advantage, but a similar positive mediating effect is not found for knowledgement and firm innovation (Salimi and Nazarian 2022 ).
Second, the impact of organisational agility on organisational outcomes is dependent on its dimensions . For instance, between the two types of organisational agility, entrepreneurial agility improves firm financial performance, while adaptive agility does not (Chakravarty et al. 2013 ). Additionally, El Idrissi et al. ( 2023 ) found that among the three dimensions of organisational agility—customer agility, operational agility, and partnering agility—only the first two help organisations to be more prepared for crises.
Third, the mixed effect of organisational agility on organisational outcomes is found under different contextual factors . For instance, the dynamics of the business environment facilitate the positive effect of organisational agility on firm financial performance but not on environmental performance or social performance (Khan 2023 ). Under a low to moderate level of industry competition, organisational agility positively mediates the impact of operational cooperation on the mass customisation of products and services. However, when competition is too intense, this mediating effect becomes negative (Sheng et al. 2021 ). Vaculík et al. ( 2018 ) found that under disruptive organisational changes, firms need to trade off short-term benefits for long-term performance. In such a situation, being more agile causes firms to abandon their current innovation projects and leads to greater possibilities of innovation project termination.
Supply chain agility has been found to improve organisational financial performance (DeGroote and Marx 2013 , Wamba and Akter 2019 ; Zhu and Gao 2021 ), competitive advantage (Alfalla-Luque et al. 2018 ; Chen 2019 ), commercial performance (Sturm et al. 2021 ), customer service (Avelar 2018 ), customer satisfaction (Gligor et al. 2020a , b ), supply chain performance (Baah et al. 2021 ; Wang and Ali 2021 ), and supply chain resilience (Naimi et al. 2020 ). However, in some specific situations, such as uncertain environmental conditions and supply chain disruptions, only supply chain flexibility—one of the three dimensions of supply chain agility—increases organisational performance, while the impacts of the other two dimensions (velocity and visibility) are not statistically significant (Juan et al. 2021 ). Another study showed that supply chain agility has no significant impact on performance (Wieland and Wallenburg 2012 ).
Strategic agility has been found to directly improve overall performance (Chan and Muthuveloo 2021 ; Kurniawan et al. 2020 ), project performance (Haider and Kayani 2021 ), technological performance (Pereira et al. 2021 ), competitive advantage (Hemmati et al. 2016 ), and innovation (Clauss et al. 2021 ). However, Reed ( 2021 ) shows that under environmental turbulence, firms that are more strategically agile experience lower financial performance.
Manufacturing agility and operational agility have been proven to increase competitiveness (Vázquez‐Bustelo et al. 2007 ), manufacturing performance (Awan et al. 2021 ), and market share (Ettlie 1998 ). However, Jacobs et al. ( 2011 ) found that the relationship between manufacturing and firm financial performance is not significant.
Strong evidence supports the contribution of other forms of agility to organisational outcomes (Abrishamkar et al. 2021 ; Asseraf et al. 2019b; Gupta et al. 2019 ; Ju et al. 2020 ; Roberts and Grover 2012 ). However, the positive contributions of these forms vary under certain conditions. Onngam and Charoensukmongkol ( 2023 ) highlighted that firms benefit more from social media agility when the organisational size is smaller and the dynamism of the business environment is lower. Sharif et al. ( 2022 ) found that market capitalising agility only mediates the relationship between knowledge coupling and firm innovation during business downsizing. Khan ( 2020 ) and Zhou et al. ( 2019 ) noted that marketing agility improves firm financial performance. However, when the market is turbulent, this positive effect becomes nonsignificant; when the complexity of marketing is heightened, higher marketing agility reduces marketing adaptation ability. Ngo and Vu ( 2021 , 2020 ) examined two dimensions of customer agility and found that while sensing capability helps organisations achieve superior financial performance, response capability does not.
Overall, the literature on the organisational impact of agility provides strong evidence to support such a positive and significant effect. However, in some cases, how and whether agility leads to higher outcomes is notably dependent on (i) certain environmental factors, (ii) different dimensions of agility and (iii) the types of organisational outcomes.
Table 6 presents the use of intervening factors in agility and performance research. Agility is often treated as an important mediator linking organisational inputs to outcomes. This is reflected in 61.8% of the research in the dataset incorporating agility as a mediator in their models. For instance, organisational agility is considered a positive explanatory factor for the impact of technological capability and IT (Govuzela and Mafini 2019 ), corporate network management (Kurniawan et al. 2021a , b ), knowledge and intellectual resources management (Cegarra-Navarro et al. 2016 ), leadership capability (Oliveira et al. 2012b , a ), risk management culture (Liu et al. 2018 ), organisational learning culture (Pantouvakis and Bouranta 2017 ), strategic alignment (Hazen et al. 2017 ), promotion information analysis capability (Shuradze et al. 2018 ), organisational ambidexterity (Del Giudice et al. 2021 ), and dispute management (Yaseen et al. 2021 ) on organisational performance. This indicates the importance of conducting agility-performance research in an organisation's internal and external context to understand how agility plays out with other factors to predict organisational outcomes.
Table 7 presents the types of intervening factors examined in the literature on agility and organisational outcomes. The literature highlights that the organisational impact of agility is subjected to a wide range of moderating factors . As aforementioned, organisational agility tends to exert its strengths under adverse environmental conditions, such as volatile and complex environments (Clauss et al. 2021 ), high competitive pressure (Ahammad et al. 2021 ), and high demand for major technological change in the industry (Ashrafi et al. 2019 ). Additionally, the impact of agility on organisational outcomes depends on external factors such as customer loyalty (Gligor et al. 2020b , a ) and industry type (Lee et al. 2016 ) or internal factors such as firm age (Reed 2021 ), the adaptability of products and marketing (Asseraf et al. 2019a), the nature of work (Chung et al. 2014 ), information technology systems agility (Tallon and Pinsonneault 2011 ), and startup innovation sensitivity (Tsou and Cheng 2018 ).
Third, the literature also elucidates the mediators through which agility contributes to organisational outcomes. These include but are not limited to the following: new technology acceptance (Chung et al. 2014 ), business model innovation (Mihardjo and Rukmana 2019 ), entrepreneurship and innovative behaviour development (Pramono et al. 2021 ), networking structure (Yang and Liu 2012 ) and market and social media analytics capability (Yang and Liu 2012 ). Similarly, supply chain agility is said to improve organisational performance through competitiveness (Sheel and Nath 2019 ), risk management (Okoumba et al. 2020 ), collaboration and re-engineering capabilities (Abeysekara et al. 2019 ), effectiveness, cost reduction (Gligor et al. 2015 ), and customer value and customer service (Um 2017 ).
The above analysis and the aspects that are mentioned in Sect. 3.5 stress the importance of studying the relationship between agility and firm performance in the context of both contextual factors and mediators. This highlights the need for future research to continue searching for factors that affect the contribution of agility to firm performance. Such comprehensive models will enhance our understanding of the relationship between agility and organisational performance and, as such, will significantly contribute to further developing this research area.
Table 8 presents a summary of popular research methodologies used in agility–organisational outcome research, with several notable findings as follows:
First, most studies in the sample use quantitative methods to examine the effect of agility on firm performance. Qualitative and mixed methods, although considered insightful and comprehensive (Truscott et al. 2010 ), have not been adequately utilised in this literature. Overall, the quantitative approach is appropriate for testing the causal effect between Agility (X) and OP (Y) in one or multiple regression models. However, the over-emphasis on causality testing without a proper investigation of the underlying reasons and insights using qualitative techniques might lead to imprecise findings and conclusions, which may create confusion and misunderstanding when applied to practice (Heyvaert et al. 2013 ).
Second, the research on agility and organisational performance mainly uses primary data from surveys and questionnaires to individuals and organisations at a specific timeframe. This approach is appropriate because, given the complexity of measuring agility, it is challenging and impractical for researchers to use proxy and secondary data for measurement. However, using a one-time survey has disadvantages in terms of reliability and generalisability, as the information collected only reflects the impact of agility on organisational performance at a specific time point. This reduces the generalisability of research findings to other contexts at different time points (Bartram 2019 ; Wooldridge 2010 ).
Third, the most popular analytical tool used in this literature is structural equation modelling (SEM)/PLS-SEM (Mikalef and Pateli 2017 ; Ramos et al. 2021 ), which includes bootstrapping techniques (Felipe et al. 2020 ; Gligor et al. 2019 ), followed by multiregression approaches for cross-sectional or panel data (Chen et al. 2014 ; Pereira et al. 2021 ). It is appropriate to use SEM for complex models with multilevel causal relationships. This method facilitates the examination of models with different pathways, including models with mediators and moderators, and provides suitable treatments for latent variables (Bollen 2014 ; Kline 2015 ).
Notably, there are two widely used methods in SEM: covariance-based SEM (CB-SEM) and partial least squares-based SEM (PLS-SEM). CB-SEM is often used in confirmatory research and factor-based models, while PLS-SEM is used in exploratory research and composite-based models (Dash and Paul 2021 ; Rigdon et al. 2017 ). However, the use of PLS-SEM is still debatable in the literature. PLS-SEM is criticised for its limited ability to examine complex and multidirectional causal relationships in SEM and its unproven assumptions (Antonakis et al. 2010 ). This leads to inconsistency in analytical findings and the ability to appraise model fit, especially for models based on small sample sizes (McIntosh et al. 2014 ; Rönkkö et al. 2016 ). Recent research in this area has emphasised that researchers must prioritise understanding their research question, the nature of the variables used, and the purpose of their research to consider the appropriate analytical method (Sarstedt et al. 2016 ).
Using a dataset of 249 empirical studies from 1998 to 2024, this literature review paper has highlighted that agility is an essential predictor of organisational outcomes. Details about agility, firm performance, and the intervening factors of this causal relationship are summarised in Fig. 4 . The findings of this paper support our understanding of the relationship between agility and organisational performance and provide valuable implications for future research in this field, as indicated below.
A summary concept map of the agility and organisational outcomes relationship
The literature shows that organisational agility is a matter of becoming rather than being (Alzoubi, et al. 2011 ; Harraf et al 2015 ). As analysed earlier, the literature on agility and firm performance has not provided a solid answer as to how and to what extent agility and its dimensions should be measured. For instance, Table 2 indicates that organisational agility can be measured with multiple instruments, including a firm’s internal capability, external partnership management, its proactiveness to sensing new opportunities, and its responsiveness to changes in the environment. This provides opportunities for future research to explore more extensive approaches to measuring agility based on the literature and explore how organisational agility and its dimensions could be improved (i.e., Ajgaonkar et al. 2022 ).
Our analysis indicates that there is a wide range of theories available in the literature that provide explanations and justifications for the contribution of agility to organisational performance, with dynamic capability theory and resource-based theory being the two most widely used theories. The literature also highlights the growing use of multitheoretical approaches for a more extensive understanding of this relationship. Future research could explore new theories and simultaneously continue to incorporate multiple theories to examine the relationship between agility and firm performance.
Our analysis indicates that organisational agility, supply chain agility, strategic agility, and manufacturing/operational agility are the most popular topics in the agility-firm performance literature, while the organisational impact of other types of agility, for instance, workforce agility, intellectual agility, leadership agility, and project management agility, are not thoroughly examined. This provides opportunities for future research to investigate these dimensions and their impact on organisational outcomes.
Another promising pathway moving forwards is leadership agility. While top managers and corporate boards are considered crucial for creating and promoting organisational agility, research on this topic is still scarce in terms of both quantity and quality (Lehn 2018 ). The existing corporate governance literature has emphasised the unparalleled contribution of boards of directors to organisational survival with their ability to link firms to external resources during economic uncertainties, crises, or bankruptcy (Haleblian and Finkelstein 1993 ; Hillman et al. 2009 ). To do so, boards needs to build their dynamic capabilities to create, strengthen, and adjust their internal resources to adapt to the external environment (Barreto 2010 ; Helfat et al. 2009 ). However, except for the work of Desai ( 2016 ) that examines the impact of board size and ownership structure on organisational flexibility and the work of Hoppmann et al. ( 2019 ) on the influence of the board on strategic flexibility, this area of research is still in its infancy. This gap in knowledge encourages future research to examine (i) the processes that allow boards to fulfil their role of facilitating changes and building agility capability in their organisation, (ii) the attributes and characteristics of boards that allow them to be more agile, and (iii) whether such agility can contribute to organisational agility, which translates to organisational outcomes.
Our literature review also indicates that agility can contribute to a wide range of organisational outcomes. However, there is still a lack of evidence on how agility affects outcomes in an orderly way running from the individual level to the group level to organisational level outcomes and how and whether the impact of agility on organisational outcomes might be different in the short, medium, and long term. Thus, it is strongly recommended that future research explore these possibilities to provide a more comprehensive and structured view of agility and outcome relationships.
Our review indicates that many aspects of organisational performance benefit from agility. However, these benefits are likely to be dependent on a wide range of factors. This encourages future research to continue searching for intervening factors that have meaningful impacts on the agility–performance relationship. For instance, how and whether agility impacts organisational outcomes might depend on various factors: the type of organisation – small and medium-sized enterprises, public sector organisations, multinational enterprises, nonprofit organisations or domestic vs. international organisations; different stages of the organisational life cycle; and different types of organisational structure and culture (Harraf et al 2015 ).
Additionally, different types of agility may interact, and such interactions might affect organisational outcomes in different ways. This warrants further investigation to examine the effects of different types of agility on firm performance both separately and interactively (Gunasekaran et al 2019 ), for instance, the interactive effects of workforce agility and manufacturing agility on organisational performance.
Our review shows that quantitative research is a primary approach in agility-firm performance research. However, the overreliance on causality might prevent researchers from understanding the underlying reasons why agility can translate to organisational outcomes and the dynamics behind this causal relationship. As such, future research should use a mixed method with both qualitative and quantitative approaches to first understand the organisational impact of agility at the surface level and, second, reveal the processes, dynamics, blockages, enablers and other organisational factors that explain the relationship between agility and organisational outcomes.
Additionally, our review indicates that there is still a lack of comparative research in this area. This provides some pathways for future research to investigate the effect of agility on firm performance in comparative settings. For instance, is the impact of agility on organisational outcomes different across different national cultures and institutional contexts?
Finally, our review highlighted the need for panel and time series data to examine the short-term, medium-term, and long-term effects of agility on organisational performance. We strongly recommend that future research develop more extensive datasets covering multiple periods to ensure that robust and rigorous studies are added to this literature.
The resulting concept model of this paper with antecedents, mediators, moderators, organisational outcomes and types of agility has multiple implications for industry practitioners.
First , organisational agility is constructed from several subcomponents corresponding to multiple business functions, such as the supply chain, strategy, manufacturing, marketing, workforce, IT and leadership. For an entire organisation to be agile, each and every function should be agile.
Organisations can utilise different avenues and practices to build capabilities that contribute to agility.
Second , agility promotes corporate outcomes through its impact on mediating actions. To realise the potential of agility, organisations should account for those mediating steps and outcomes in their implementation.
Finally , a strong finding of this literature review is the way in which the relationship between agility and outcomes is contextualised. As such, organisations should pay attention to both internal and external environments as contingent factors on agility and outcomes. For instance, agility seems to have the greatest impact in complex and volatile environments, so organisations should carefully consider the implementation of agility if they operate in relatively stable industries. Additionally, while startups in high-tech industries are initially agile, established businesses in stable industries are generally not agile. As such, for such businesses to achieve agility, they should consider factors such as firm size, IT infrastructure and their customer base.
By answering the research question “ What is the current status of the literature on organisational agility and organisational outcomes?” in the above analysis, this study has provided a comprehensive picture of the current literature on the relationship between several aspects of agility and firm performance, with the former either as independent or as mediator variables. The review covers theories, measurements, relationship structure, methodology, and concepts of agility. Following Walter's ( 2021 ) systematic review of agility, our study has extended the scope of investigation and focuses specifically on the relationship between the two most important concepts of agility and performance that play a minor role in Walter’s OA conceptual map. Additionally, the paper has mapped out the organisational agility–performance relationship with antecedents, mediators and moderators, each with a specific list of dimensions for measurement, as sketched out in the subresearch questions. This conceptual map can guide future studies in establishing well-rooted research models.
With a limited number of empirical studies (249), a sharp increase since 2017, a few with archival data (while a majority with data from questionnaires and interviews), and a significant proportion of research without theories as background, agility performance appears to be an emerging research field in its immature phase. This point is strengthened by the fact that the reviewed articles are not in top theoretical management journals such as the Journal of Management and the Academy of Management Journal. Furthermore, theories of this relationship have not been explicitly developed to support quantitative studies for hypothesis testing. By highlighting this gap, this study opens a new road for researchers to establish theories for the agility–performance relation beyond what is currently borrowed from the strategic management field.
Our paper has several limitations. Our attempt to provide a comprehensive overview of agility and performance prevents us from examining this relationship in a specific country or industry context. In addition, although our dataset covers a long time frame from 1998 to February 2024, some of the most recent research may not be included in our review. Nevertheless, we believe that our findings underline both the importance of organisational agility and the worth viewing it in conjunction with other organisational aspects in predicting organisational performance. Furthermore, we hope that this study will inspire future investigations to move further in this literature.
In conclusion, organisational agility and its association with organisational performance have emerged as attractive research topics since 2017. Even though quantitative empirical studies account for most publications, a significant number of them lack a background theory and a consensus on measuring agility and its subcategories. This is detrimental to the value of the findings and intensifies the need for future studies to develop this immature field.
The data that support the findings of this study are available from Web of Science database for account holders. The data are available from the authors upon request.
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New research from Microsoft on how much time new hires should spend in the office during their first 90 days.
As you’re navigating hybrid work, it’s a good moment to assess how your onboarding processes enable or empower your new hires to thrive. Researchers at Microsoft have conducted and identified studies that suggest that onboarding to a new role, team, or company is a key moment for building connections with the new manager and team and doing so a few days in person provides unique benefits. But just requiring newcomers to be onsite full time doesn’t guarantee success. The authors explain and offer examples of how onboarding that truly helps new employees thrive in the modern workplace is less about face time and more about intention, structure, and resources.
During the pandemic, companies around the world explored new ways of working that challenged long-held assumptions and beliefs about where work gets done. Many companies, including Microsoft , saw the benefits of flexible work and wanted to offer employees a chance to continue to work in a hybrid environment, while balancing the needs of the organization.
The recent accelerated rise in global interest rates, the fastest in decades, brought the curtain down on an extended period of cheap money but provided little clarity on the longer-term outlook. In 2024, competing forces of tepid growth, geopolitical tension, and regional conflict are creating nearly equal chances of higher-for-longer benchmark rates and rapid cuts. In the banking industry, this uncertainty presents both risks and opportunities. But in the absence of recent precedent, many institutions lack the necessary playbook to tackle the challenge.
As rates have risen from their record lows, banks have in general profited from rising net interest margins (NIMs). However, if policy makers switch swiftly into cutting mode, banks may see the opposite effect. For now, futures markets predict the start of that process toward the end of 2024. In that context, the question facing risk managers is how they can retain the benefit of higher rates while preparing for cuts and managing the potential for macroeconomic surprises.
The question facing risk managers is how they can retain the benefit of higher rates while preparing for cuts and managing the potential for macroeconomic surprises.
The volatility playing out in rates markets is reflected in bank deposit trends, with customers more actively managing their cash to make the most of shifting monetary conditions. In Europe, deposits reached 63 percent of available stable funding (ASF) in 2023, compared with 57 percent in 2021. 1 Monitoring of liquidity coverage ratio and net stable funding ratio implementation in the EU – third report, European Banking Authority, June 15, 2023. In the US, conversely, the share of deposits over total liabilities fell over a similar period as money migrated to investments such as money market funds.
In the face of accelerating deposit flows, McKinsey research shows that bank risk management and funding performance has been highly variable. Between 2021 and 2023, the best-performing US and EU banks saw interest rate expenses rise 70 percent less than at the worst-performing banks (Exhibit 1). Among the drivers were better deposit and interest rate management.
Alongside the impacts of deposit flows, funding has come under pressure from other factors, including the steady withdrawal of pandemic-related central bank liquidity facilities. Meanwhile, innovations such as instant payments have motivated customers to make faster and larger transfers. These withdrawals can happen quickly and be fueled by social media, creating a powerful new species of risk.
In the context of a more uncertain environment, regulatory authorities are doubling down on oversight of the potential impacts of rate volatility—for example, by asking banks to mitigate the potential effects of rate normalization, increasing overall scrutiny, and demanding evidence of methodology upgrades. Among European supervisory priorities for 2024–26, banks are advised to sharpen their governance and strategic frameworks to strengthen asset and liability management (ALM) and develop new funding plans and contingency measures for short-term liquidity shocks, including evaluating the adequacy of assumptions supporting some behavioral models. 2 “SSM Supervisory Priorities, 2024-2026,” in Supervisory priorities and assessment of risks and vulnerabilities , European Central Bank, 2023. In the same vein, the Basel Committee on Banking Supervision in 2023 proposed a recalibration of shocks for interest rate risk in the banking book. Banks can achieve this by extending the time series used in model calibration from the current December 2015 standard to December 2022, bringing more volatile rate distributions into the equation.
In a recent McKinsey roundtable, 40 percent of Europe, Middle East, and Africa bank treasurers said the topic that will attract most regulatory attention in the coming period is liquidity risk, followed by capital risk and interest rate risk in the banking book (IRRBB). With these risks in mind, 34 percent of treasurers said their top priorities with respect to rate risk were enhancing models and analytics, revising pricing strategies on loans and deposits, and beefing up ALM governance and monitoring capabilities.
Most participants also expected treasury teams to get more involved in strategic planning and board engagement and to engage business units more closely to define pricing strategies and product innovation (Exhibit 2).
In response to these dynamics, we expect to see many banks revisiting the role of the treasury function in the months ahead. For many, this will mean moving away from approaches designed for the low-rate era and toward those predicated on uncertainty. In this article, we discuss how forward-looking banks are redesigning their treasury functions to obtain deeper insights into probabilities around interest rates and their impacts on pricing, customer behavior, deposits, and liquidity.
To manage volatile interest rates more effectively, leading banks are revisiting practices in the treasury function that evolved during the low-interest-rate period and may no longer be fit for purpose—or at least should be updated for the new environment. Pioneers have taken steps in five broad focus areas: steering and monitoring, risk measurement and capabilities, stress testing, bank funding, and hedging.
A precondition of effective oversight of interest rate business is to ensure decision makers have a clear view of the current state of play. Currently, the standard approach across the industry is somewhat passive, meaning it is based on static or seldom-reviewed pricing and risk management decisions, often taken by relationship managers. Models are fed with low-frequency data, and banks use static fund transfer pricing (FTP) to calculate net interest margins. Monitoring often reflects regulatory timelines rather than the desire to optimize decision making.
Forward-looking banks are tackling these challenges through a more hands-on approach to steering and monitoring, including the following measures:
Leading banks are getting a grip on IRRBB risk in areas such as balance sheet management, pricing, and collateral. Many have assembled dedicated teams to help them make more effective decisions. Given the threat to deposits, some are making greater use of scenario-based frameworks, bringing together liquidity and interest rate risk management. They are using real-time data to inform funding and pricing decisions.
To ensure they consider all aspects of rate risk, leading banks employ a cascade of models, feeding the outputs into steering and stress-testing frameworks, and capturing behavioral indicators that can inform balance sheet planning and hedging activities. Some banks are employing behavioral models to forecast loan acceptance rates and credit line drawings. Best practice involves using statistical grids differentiated by type of customer, product, and process phase.
When it comes to loans, some banks are leveraging AI to predict prepayments and their impacts on balance sheets and hedging requirements. Best practice in prepayments modeling is to move away from linear models and toward machine learning algorithms such as random forests to consider nonlinear relationships (for instance, between prepayments and interest rate variation) and loan features (for example, embedded options), as well as behavioral factors. We see five key steps:
Another important focus area is deposit decay. Many banks still prioritize moving-average approaches segmented by maturity and backed by expert judgment. A best practice would be to identify a core balance through a combined expert and statistical approach, looking at trends across customer segmentation, core balance modeling, deposit volume modeling, deposit beta and pass-through rates, and replicating portfolio/hedge strategies. This would mean leveraging AI and high-frequency data relating to transactions, to estimate each account’s non-operational liquidity, which customers may be more likely to move elsewhere (see sidebar “Case study: Deposit modeling to limit deposit erosion”). Some banks also use survival models to gauge non-linearities in deposit behaviors.
One bank achieved an equivalent of €150 million to €200 million positive P&L impact on €30 billion of deposits by using AI techniques for repricing. The tool provided transparency on the following measures:
Armed with this transparency, the bank was able to formulate client-specific strategies for repricing actions and product offerings (for example, investment products and transaction banking services), optimizing both its funding sources and profitability. New capabilities to support the effort included a deposits command center, producing a real-time dashboard for monitoring, including early warning triggers, sales team mobilization, and new product offering, especially for cash-rich corporate clients.
In the context of IRRBB strategy, leading banks are keeping a close eye on both deposit beta and pass-through rates (the portion of a change in the benchmark rate that is passed on to the deposit rate). They back their judgments with views on client stickiness, which they traditionally arrive at through expert judgment and market research. A more advanced approach is to derive regime-based elasticities, capturing data from historical economic cycles.
A European global bank wanted to improve its forecasting in a rising-interest-rate context. Managers decided to focus more on customer behavior. They moved away from expert-judgment buffers to AI and stochastic modeling and a more focused approach to model calibration. They also updated scenario planning based on regulatory guidelines and best-in-class approaches, such as an interest rate risk in the banking book (IRRBB) dynamic balance sheet methodology. Through these changes, the bank was able to estimate its duration gap (between assets and liabilities) more accurately and thereby reduce delta economic value of equity (EVE). As a result, the bank recorded a 70-basis-point uplift in return on equity, resulting from capital savings on interest rate risk and a direct P&L impact from reduced hedging.
Finally, risks need to be optimally matched with hedges. The recent trend is to use stochastic models to support hedging decisions, enabling banks to gauge non-linearities. Forward-looking banks increasingly integrate deposit, prepayment, and pipeline modeling directly into their hedging strategies. They also ensure model risk is closely monitored, with models recalibrated frequently to reduce reliance on expert input (see sidebar “Better modeling enables more resilience: One bank’s story”).
Several players are integrating interest rate risk, credit spread risk, liquidity risk, and funding concentration risk in both regulatory and internal stress tests. Indeed, the IRRBB, liquidity risk, and market risk (credit spread risk in the banking book, or CSRBB) highlight the trade-off between capital and liquidity regulations. In short, higher capital requirements may reduce the need for excessive liquidity, and vice versa, for a bank with stable funding—a situation that remains a challenge to current regulatory frameworks.
Stress testing to measure interest rate risk is also evolving, with some banks adopting reverse stress testing (see sidebar “Enhancing Basel's interest rate risk measures: Exploring the efficacy of reverse stress testing and VAR”).
Research conducted by a group of bank risk managers suggests that the current supervisory outlier tests for interest rate risk in the banking book (IRRBB) may not adequately address all significant risk scenarios. Specifically, the scenarios outlined in the BCBS 368 guidelines for stress-testing economic value of equity (EVE) and net interest income (NII) may fall short in identifying substantial IRRBB risks. This oversight could make it more difficult for banks to recognize material risks of loss, especially if they have complex or unconventional portfolios.
To identify more material risks, experts are recommending a shift in approach. Instead of focusing solely on extreme and plausible scenarios, they are advised to consider all possible scenarios and integrate reverse stress testing. This would involve simulating thousands of historical and hypothetical scenarios, covering almost the entire spectrum of possible yield curves. After computing NII and EVE, attention would be directed to the scenarios that could have the most adverse impact on the bank’s balance sheet.
In alignment with this proposed methodology, Australian banks will be mandated from 2025 to calculate IRRBB capital using measures of expected shortfall rather than value at risk (VAR). The change is intended to incorporate tail risk, with the new methodology utilizing data from the past seven years, coupled with a distinct one-year stress period.
In upgrading their stress-testing frameworks and interest rate strategies, banks need to balance net interest income (NII) and economic value of equity (EVE) risks that may materialize as a function of rate volatility. On NII, banks can productively apply scenario-based yield curve analysis across regulatory, market, and bank-specific variables and weigh these in the context of overall balance sheet exposures, hedges, and factors including deposits, prepayments, and committed credit lines. Additional economic risks include basis risk, option risk, and credit spread risk, which also should be measured.
Bank funding plans are often generic, periodic, and spread across different frameworks and methodologies, including funding plans, capital plans, internal capital adequacy assessment processes (ICAAP), and internal liquidity adequacy assessment processes (ILAAP). They are often designed for a range of purposes and audiences and updated only when prompted by regulatory requirements. In future, banks will need dynamic, diversified, and granular funding plans—for example, tailored to products and regions. The plans should reflect flexible and contingent funding sources, central bank policies, and the trade-off between risks and costs.
In the era of low rates, hedging of interest rate risk was a less prominent activity. Banks often employed simple, static, short-term, or isolated strategies, mostly aimed at protecting P&L. Few banks paid a great deal of attention to collateral management.
Now, in a more volatile rate environment, the potential for losses is much higher, suggesting banks need more sophisticated, agile, and frequent hedging to respond to shifts in interest rates, credit spreads, and customer deposit behaviors (Exhibit 3). Indeed, in 2023, the traded volume of euro-denominated interest rate derivatives increased by 3.4 times compared with 2020, according to the International Swaps and Derivatives Association. 3 “Interest rate derivatives US: Transaction data,” ISDA.
Hedging strategies are evolving to be dynamic, horizontally integrated across the organization, and wedded to risk appetite frameworks, so banks can balance P&L priorities and reductions in tail risk. On the ground, banks will likely need to recalibrate their strategies frequently, ideally leveraging a comprehensive scenario-based approach to reflect changes in the external environment. Many, for example, have already revisited hedging to reflect higher rates, but as rates fall, they will need to assess factors such as the impact of convexity on short positions. The objective of these exercises would ideally extend beyond risk mitigation to the optimization of NII (see sidebar “Replication and hedging: The upsides of NIM optimization”).
Broadly, banks may consider four approaches to replication and hedging, each of which offers benefits that will vary according to the bank’s unique asset base.
Static replication is a widely applied and robust approach that involves derivation and adjustment of cash flows from deposit volume models for deposit rate elasticity and pass-through rates. The remainder of cash flows are replicated with bonds, interest rate swaps, or loans. Future deposit growth can be incorporated if desired.
Dynamic hedging of present value of net interest margin (NIM) treats the deposit portfolio like a structured product. Banks calculate the present value of NIM arising from deposits, enabling derivation of present value sensitivity to changes in interest rates. The method supports dynamic hedging and can take into account negative convexity.
Static NIM optimization provides the recommended trade-off between granularity and sophistication on the one hand and usability on the other, and it is our preferred approach. It involves design of the fixed-income portfolio to replicate deposit balance dynamics over a sample period. The analyst then selects the portfolio yielding the most stable margin, represented by minimization of margin standard deviation of the spread between the portfolio return and deposit rate. The approach enables NIM maximization, with the caveat that shorter tenors tend to be preferred in periods of low benchmark rates.
Dynamic NIM optimization permits banks to model future interest rates with NIM and investment strategy optimized for a future horizon. Again, NIM can be maximized, but the approach requires assumptions on volume growth, and the optimization horizon may not extend to the full rate cycle.
A key principle of best-in-class hedging strategy is that a proactive, forward-looking approach tends to work best and will enable banks to hedge more points on the yield curve. And with forward-looking scenario analysis, they should be able to anticipate risks more effectively. Consider the case of a bank that was exposed to falling interest rates and did not meet the regulatory threshold for outliers under the new IRRBB rules for changes in NII. Through analysis of potential client migrations to other products and a push to help clients make those transfers, combined with a new multi-billion-dollar derivative hedging strategy, the bank brought itself within the threshold.
Banks should not view hedging as a stand-alone activity but rather as integrated with risk management, backed by investment in talent and education to ensure teams choose the right hedges for the right situation. These may be traditional interest rate derivatives but equally could be options or swaptions to bring more flexibility to the hedging strategy. AI will be table stakes to support decision making and identify risks before they materialize. A more automated approach to data analytics will likely be required. And collateral management should be a core element of hedging frameworks, with analytics employed to forecast collateral valuations and needs, optimize liquidity reserves, and mitigate margin call risk.
To effectively implement change across the activities highlighted here, best practice would be to bring together modeling capabilities under a dedicated data strategy. The target state should be comprehensive capabilities, a unified and actionable scenario-based framework, and routine use of AI techniques and behavioral data for decisions around pricing and collateral. Most likely, a talent strategy also will be required to support capability building across analytics, trading, finance, pricing, and risk management.
Banks must marshal a broad range of market data to support effective modeling. The data will include all credit lines, including both on–balance sheet and off–balance sheet items, deposit lines, fixed-income assets and liabilities, capital items, and other items on the banking book. Ideally, banks would assemble 15 to 20 years of data, which would take in the previous period of rising interest rates from 2004 to 2007. Alongside these basic resources, banks need information on historical residual balances, amortization plans, optionality, currencies, indexing, counterparty information, behavioral insights, and a full set of macro data. Some cutting-edge models incorporate about 150 different features.
Armed with comprehensive data, banks can build behavioral models (for example, prepayments, deposits) to estimate parameters and infer behavioral effects in different scenarios. They can then integrate behavioral outputs into stress-testing simulations, alongside expert-based insights. Once macroeconomic data has been inputted, banks should be able to compute delta NII and EVE for three years. Visualization tools and hedging replica analysis can help teams clarify their insights and test their hedging strategies across risk factors.
Banks that have embraced the levers discussed here have set themselves on a course to more proactive and effective interest rate risk management. Through a sharper focus on high-quality data and the use of AI and scenario-based frameworks, banks have shown they can make better decisions, upgrade their hedging capabilities, optimize the cost of funding, and ensure they stay within regulatory thresholds. In short, they will be equipped to respond faster and more flexibly as interest rates enter a new era of volatility.
Andreas Bohn is a partner in McKinsey’s Frankfurt office, Sebastian Schneider is a senior partner in the Munich office, Enrique Briega is a knowledge expert in the Madrid office, and Mario Nargi is an associate partner in the Milan office.
The authors wish to thank Gonzalo Oliveira and Stefano Terra for their contributions to this article.
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Journal of Medical Case Reports volume 18 , Article number: 279 ( 2024 ) Cite this article
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Glycated hemoglobin is a well-known marker for evaluating long-term glycemic control. However, the accuracy of glycated hemoglobin measurement can be affected by the presence of hemoglobin variants, which makes the determination and interpretation of glycated hemoglobin values in terms of glycemic control not only difficult but also misleading. Here we present the first ever case of a patient with type 2 diabetes with hemoglobin E from Nepal, diagnosed incidentally because of spurious glycated hemoglobin levels.
A 45-year-old Hindu Mongolian female with a history of type 2 diabetes for around 9 years but not very compliant with follow-ups was referred to our facility for plasma fasting and postprandial blood glucose levels and glycated hemoglobin. Fasting and postprandial blood sugars were found to be high. A consistent very low glycated hemoglobin by two different high-performance liquid chromatography (HPLC) methods compelled us to call the patient for a detailed clinical history and for the records of investigations done in the past. The patient has been a known case of type 2 diabetes for around 9 years and presented irregularly for follow-up visits. Around 4 years ago, she presented to a healthcare facility with fatigue, severe headaches, pain in the abdomen, discomfort, and dizziness for a couple of months, where she was shown to have high blood glucose. She was referred to a tertiary-level hospital in Kathmandu, where she was prescribed metformin 500 mg once daily (OD). Due to her abnormal hemoglobin A1c reports, she was then sent to the National Public Health Laboratory for repeat investigations. Her blood and urine investigations were sent. Complete blood count findings revealed high red blood cell and white blood cell counts, a low mean corpuscular volume, and a high red cell distribution width-coefficient of variation. Other parameters, including serum electrolytes, renal function tests, liver function tests, and urine routine examinations, were within normal limits. A peripheral blood smear revealed microcytic hypochromic red cells with some target cells. Hemoglobin electrophoresis showed a very high percentage of hemoglobin E, a very low percentage of hemoglobin A2, and normal proportions of hemoglobin A and hemoglobin F. A diagnosis of homozygous hemoglobin E was made, and family screening was advised.
Clinicians should be aware of the limitations of glycated hemoglobin estimation by ion exchange high-performance liquid chromatography in patients with hemoglobin E and other hemoglobin variants. If the clinical impression and glycated hemoglobin test results do not match, glycated hemoglobin values should be determined with a second method based on a different principle, and glycemic status should be confirmed through alternative investigations, preferably those that are not influenced by the presence of hemoglobin variants (for example, boronate affinity chromatography, fructosamine test, glycated albumin test, the oral glucose tolerance test, continuous glucose monitoring, etc.). Consistent or even doubtful results should also raise the suspicion of a hemoglobin variant, which should be confirmed through further evaluation and investigations.
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Glycated hemoglobin (HbA1c), initially identified as “unusual” hemoglobin in patients with diabetes over half a century ago [ 1 ], has now become a well-known marker of long-term glycemic control in individuals with diabetes mellitus [ 2 ], reflecting an average blood glucose level over a period of around 3 months. Glycemic control being an important factor for the progression to long-term complications, HbA1c strongly correlates with the risk of developing chronic complications associated with diabetes as well [ 3 ]; hence, it has become not only a diagnostic tool but also a screening tool for individuals at risk of diabetes [ 4 ]. However, the accuracy of HbA1c measurement can be affected by various factors such as erythropoiesis, glycation, erythrocyte destruction, and the presence of hemoglobin (Hb) variants [ 5 ], which makes the determination and interpretation of HbA1c values in terms of glycemic control not only difficult but also misleading [ 6 ]. Here, we present a case of a variant of Hb, the HbE, diagnosed incidentally in a patient with type 2 diabetes mellitus suspected initially because of repeated abnormal HbA1c levels detected with an ion-exchange high-performance liquid chromatography (HPLC) and hence subjected to further investigations by Hb capillary electrophoresis to confirm the diagnosis. To our knowledge, this is the first ever incidentally diagnosed case of HbE in a patient with type 2 diabetes from Nepal, all because of spurious HbA1c levels.
A 45-year-old Hindu Mongolian female, with a history of type 2 diabetes for 9 years and under medication for the last 4 months, visited the National Public Health Laboratory (NPHL), Teku, to test for plasma fasting and postprandial blood glucose levels and HbA1c.
We tested HbA1c by high-performance liquid chromatography (HPLC) Biorad VARIANT II, which showed HbA1c to be only 1.1% (Fig. 1 ). The control test run for the day was within range. Fasting and postprandial blood sugars were found to be 173 mg/dL and 280 mg/dL, respectively. The patient sample was rechecked with HPLC TOSO 7234X, which showed HbA1c of 2.1%. In view of this, the patient was contacted and advised to have a complete blood count (CBC) and hemoglobin electrophoresis. We took a detailed history of the patient and requested the records of investigations done in the past.
HbA1c detection by HPLC
According to the patient, she was apparently well around 9 years ago (the patient herself was not sure about the exact date) when she started developing increased urination, increased thirst, weakness, and occasional dizziness for a couple of months. She then visited a local health care facility near her hometown, where she was examined and sent for some blood and urine investigations (she has misplaced the lab reports, or probably lost them, according to her). According to her, she was told that she has high blood sugar levels and was advised to make some lifestyle modifications, such as dietary changes and increasing physical activities. She was also prescribed medication (she has no records of the medications prescribed then) and was asked to follow up in the next 3 months. Thereafter, she was doing fine with no complaints for a couple of years, and she never followed up with any healthcare facility until around 3–4 years ago (May 2020), when she presented herself to a healthcare facility in Kathmandu city with complaints of fatigue, severe headaches, pain and discomfort in the abdomen, and occasional dizziness and a feeling of lightheadedness for a couple of months. She also complained of weakness and decreased tolerance for physical activity. Physical examination was unremarkable, and laboratory investigations revealed high blood glucose and serum triglyceride levels (fasting blood glucose: 175 mg/dL; postprandial blood glucose: 193 mg/dL; serum triglyceride: 290 mg/dL). Other investigations were within normal limits (Table 1 ).
With these reports, she was referred to a tertiary-level hospital in Kathmandu. On 1 January 2021, she visited Shree Birendra Hospital (a tertiary-level hospital in Kathmandu), where she was further evaluated. At this time, she also admitted having a history of a slight decrease in vision for the last few months and was evaluated for any eye findings. Apart from a slightly presbyopic finding, everything else was normal. Intraocular pressure was within normal limits. There were no findings suggestive of diabetic retinopathy. The fundus examination was normal. Further lab workup findings revealed the following: TLC: 11,660 cells/mcL; Differential Count (DC): Neutrophils = 76, Lymphocytes = 17, Monocytes = 5, Eosinophils = 1, Basophils = 1; MCV: 64.4 fL; MCH: 21.9 pg; RDW-SD: 36.1 fL; RDW-CV: 17.3 fL; postprandial (PP) blood glucose (two hours after taking a meal): 294 mg/dL; and HbA1c: 2.1%. Ultrasonography (USG) of the abdomen and pelvis showed a bulky uterus. All other investigations were within normal limits (Table 1 ).
She was prescribed metformin 500 mg OD and advised to make lifestyle modifications once again. She was then referred to the National Public Health Laboratory (NPHL), the central reference laboratory in Nepal, for repeat investigations to confirm the abnormal HbA1c reports and for all other relevant investigations. On arrival at NPHL (21 January 2021), she was thoroughly interviewed for a detailed medical history. She was a nonvegetarian, and she did not smoke tobacco or consume alcohol. She had never received a blood transfusion before. Apart from the metformin prescribed earlier, she was not on any other medications. There was no family history of low hemoglobin levels or blood transfusions. There was no other significant medical history in any of her family members, as far as the patient could remember. She gave a history of iron supplementation a long time ago (around 9 years ago when she first presented to a healthcare facility) for around 3 months but not thereafter. She denies any history of any means of blood loss. She has been married for around 25 years and has two kids (elder son: 23 years old, younger daughter: 21 years old). Physical examination was unremarkable and provided the following data: height: 5 feet; weight: 68 kg; body mass index (BMI): 29.3. Laboratory investigations are as follows:
CBC findings revealed hemoglobin of 13.6 g/dL, RBC of 6.2 × 10 6 cells/mcL, total leucocyte counts of 13,700 cells/mcL ( N = 77, L = 18, M = 4, E = 1, B = 0), platelet count of 2.8 × 10 5 cells/mcL, and packed cell volume (PCV) of 42.1%. Red cell indices included mean corpuscular volume (MCV), mean hemoglobin concentration (MCH), mean corpuscular hemoglobin concentration (MCHC), and RDW-CV%, which were found to be 67 fL, 21.7 pg, 32.3 g/dL, and 17.9%, respectively. All other investigations were within normal limits (Table 1 ).
The ultrasonography (USG) report of the abdomen and pelvis revealed a bulky uterus and was otherwise unremarkable.
Hb electrophoresis was performed in the SEBIA MINICAP FLEX PIERCING electrophoretogram, which showed 93.2% HbE, 2.4% HbA2, 2.9% HbF, and 1.5% HbA (Fig. 2 ). A peripheral blood smear revealed microcytic hypochromic red cells with some target cells. White blood cell (WBC) and platelet morphology seem to have no abnormalities.
Detection of HbE by capillary electrophoresis
On the basis od these findings, a diagnosis of homozygous HbE was made, and family screening was advised.
Glycated hemoglobin (HbA1c), an effective and objective retrospective marker reflecting an average blood glucose level over a period of around 3 months, has now become a well-known indicator of long-term glycemic control in individuals with diabetes mellitus. However, there are various factors that may influence and falsely alter the level of HbA1c and its measurement and hence need to be considered in patients with abnormal readings.
HbA1c levels seem to inversely correlate with the rate of erythropoiesis, and hence factors that decrease the rate of erythropoiesis (including iron, vitamin B 12 , and folate deficiency) and/or increase erythrocyte life span (for example, due to splenectomy) falsely increase the level of HbA1c. Conversely, administration of erythropoietin, iron, and vitamin B 12 and conditions associated with reticulocytosis and decreased erythrocyte lifespan (for example, splenomegaly or even pregnancy) tend to falsely decrease the level of HbA1c [ 1 , 7 ]. There has been evidence of a false increase in HbA1c in cases of alcoholism and in patients with chronic kidney disease (CKD), as well, possibly through the same mechanism (alcohol interferes with folate metabolism, and patients with CKD have decreased erythropoietin levels) [ 1 , 7 , 8 ]. Several other conditions, for example, hyperbilirubinemia, carbamylated hemoglobin, chronic opiate use, etc., are also seen to be associated with a high HbA1c level [ 1 ]. However, chronic liver disease, rheumatoid arthritis, hypertriglyceridemia, and even the use of drugs such as ribavirin and dapsone have been shown to be associated with decreased HbA1c [ 1 , 7 , 9 , 10 ]. Genetic or chemical alterations in hemoglobin undoubtedly have some associations with HbA1c, and hence certain hemoglobinopathies, including the HbE disease, the presence of HbF, and methemoglobinemia, may also alter the level of HbA1c [ 1 , 7 , 11 ]. Here we discuss the presence of HbE and the misleading value of HbA1c levels.
Hemoglobin E, a variant hemoglobin, is characterized by a mutation in the β globin gene ( HBB gene) causing substitution of glutamic acid for lysine at position 26 of the β globin chain, resulting in a heterogeneous group of disorders whose phenotypes range from asymptomatic to severe disease [ 12 , 13 ]. HbE trait and HbEE are mild disorders, while a combination of HbE with other forms of hemoglobinopathies does exist that can have a markedly different and more serious clinical course, producing a wide range of clinical syndromes of varying severity [ 13 , 14 ]. The heterozygous form of HbE is usually characterized by minimal red cell morphological abnormalities and normal red cell indices, while homozygotes for HbE can have red cells with significant morphological abnormalities, including increased numbers of target cells, and can present with mild microcytic hypochromic anemia [ 14 ].
Despite advances in the standardization of methods for glycohemoglobins, including HbA1c, an increasing number of hemoglobinopathies have been shown to interfere with the accurate measurements and determination of these glycohemoglobins. Even the most commonly used methods, that is, the HPLC methods for HbA1c determination, lacked the resolution necessary to differentiate hemoglobin variants [ 6 ]. The demonstration of additional peaks in the chromatograms and either too low or too high values of HbA1c has been shown as compared with the nondiabetic reference range in different types of hemoglobinopathies [ 15 ].
Patients homozygous for HbE, that is, those receiving mutated genes from both parents, have a very low HbA level, with around 80% of Hb being HbE itself. The mutation associated with HbE tends to alter the ionic charges on Hb and hence interferes with the measurement of HbA1c via the ion-exchange HPLC method, especially in homozygous cases [ 16 , 17 ]. Unique mutation(s) on the N-terminal of β-globin in some hemoglobinopathies such as the Hb Graz and the Hb Long Island variants also seem to cause inappropriately high and low apparent HbA1c titers via HPLC methods. However, estimations with the boronate affinity technique and the immunoassay technique seem to be unaffected. The boronate affinity method has shown values in an acceptable and clinically reasonable range for all hemoglobin variants, as evidenced by several studies [ 18 , 19 ]. In fact, affinity methods have already been suggested as an acceptable and more useful method for reflecting glycemic control because they mainly measure glycohemoglobin regardless of the glycation site and hence may be clinically more accurate [ 15 ]. Studies have shown that results from the HbA1c immunoassays were also comparable to those from HPLC assays, showing good correlation, appropriate precision, and low bias [ 20 ]. Immunoassays utilizing various antibodies raised against specific epitopes of hemoglobin, for example, the Amadori product of glucose plus the first eight amino acids on the N-terminal end of the beta chain of hemoglobin, and many more, have shown good correlation with established methods for estimating glycohemoglobin [ 21 ]. However, as the quality of an immunoassay typically depends on the specificity of the antibody to the specific epitope on HbA, specific mutations altering the common epitopes used for the assays will hinder the accuracy of the test. One such example includes the hemoglobin variant with mutations affecting or altering the epitope at the N-terminal chain. The mutation seems to affect the ability of the monoclonal antibody that is used in the assay to detect hemoglobin [ 22 ]. Some uncommonly occurring variants that span the commonly used epitope include HbE and HbD (Los Angeles), where mutations occur at β26 and β121, respectively. There are some other evidences/studies that have reported that immunoassays have been shown to produce false HbA1c results in certain Hb variants [ 23 , 24 , 25 , 26 ]. Hence, choosing a method where the antibody epitope does not span the specific area in the Hb variant is crucial. However, it is not practical or even feasible to produce several specific antibodies (according to the individual patient) at each facility, and hence understanding the effects of such hemoglobinopathies while estimating the glycohemoglobins is crucial. To be precise, the effect of various hemoglobinopathies on HbA1c measurements is highly method-dependent. So, it is always better to be correlated clinically, and whenever the HbA1c results do not fit the clinical picture, some additional peaks in HPLC chromatograms are displayed, or any such doubtful scenario has been presented, it should not be ignored, and further investigations are advisable. Glycemic status over a short period of time (1–3 weeks) can also be reflected by the fructosamine test. Some researchers have therefore recommended confirmation with the fructosamine test or the glycated albumin test as an alternative [ 14 , 24 , 27 , 28 ]. Fructosamine results depend on the glycation of serum proteins and are not influenced by hemoglobin variants [ 19 ]. However, falsely low levels may occur in patients with hypoalbuminemia, for example, in patients with nephrotic syndrome or severe liver disease [ 29 ]. The glycated albumin test, reported as a percentage of total albumin, also reflects short-term glycemic status, typically over the preceding 2–3 weeks, and is not influenced by situations that falsely alter A1C levels [ 29 , 30 ]. Moreover, the tests that rely purely on blood glucose levels, including the oral glucose tolerance test (OGTT) and even continuous glucose monitoring, could possibly be the ones that are least affected by various factors, as discussed earlier. The OGTT is advocated for screening and diagnosis, and self-monitoring of blood glucose levels is advised for management during pregnancy [ 29 ]. Continuous glucose monitoring for up to 5 days has also been shown to correlate well with HbA1c levels [ 29 ].
The mutations associated with hemoglobin E disease are primarily seen to be prevalent in the eastern half of the Indian subcontinent and throughout Southeast Asia [ 12 , 23 ]. In 1954, Chernoff and colleagues first described that it has occurred in conjunction with β thalassemia, in which case it presents with a severe form of the disease known as the compound heterozygosity for hemoglobin E/β thalassemia [ 24 ], and since then several other cases have been reported from several parts of Southeast Asia [ 25 , 26 , 27 , 31 ]. Cases have been reported from some parts of Nepal, as well, but to our knowledge, this case report is the first ever report of an incidentally diagnosed HbE variant in a patient with type 2 diabetes mellitus in Nepal.
Several studies have evidenced and reported that various hemoglobinopathies, including HbE disease, interfere with accurate measurements of glycosylated hemoglobin, including HbA1c. A study conducted on the prevalence of hemoglobin variants and their effect on HbA1c measurement among the indigenous population of north Bengal showed Hb variants to have a significant effect on HbA1c measurement [ 32 ]. A clinically silent and very rare hemoglobinopathy, hemoglobin Himeji, has been reported in a Portuguese patient with diabetes with a discrepancy between fasting plasma glucose and HbA1c [ 33 ]. Yet another case series of two female Malay patients with HbJ, an Hb variant, showed persistently high HbA1c levels despite good glycemic control [ 34 ].
Because of the local occurrence of Hb variants and the ethnic origin of a given population, every individual laboratory must establish and validate its own assay method. Also, while managing patients with diabetes, knowledge of hemoglobinopathies influencing HbA1c determination methods is essential. Moreover, in populations with a high prevalence of hemoglobinopathies, hemoglobin typing should be considered basic information prior to HbA1c measurement, as suggested by some other studies, as well [ 35 ].
Hence, to conclude, clinicians should be aware of this limitation of HbA1c estimation by ion-exchange HPLC in patients with HbE and other Hb variants, though HPLC has been an important and one of the most commonly used modalities [ 26 ], among the several others such as immunoassay techniques, boronate affinity chromatography, etc., to detect it [ 18 ]. If the clinical impression and HbA1c test results do not match, then HbA1c values should be determined with a second method based on a different principle and confirmation of the glycemic status through alternative investigations. The boronate affinity method has been shown to be clinically reasonable for all hemoglobin variants. Similarly, the fructosamine test and the glycated albumin test could also be used as alternatives, as they are also not influenced by the presence of hemoglobin variants. The OGTT and continuous glucose monitoring can obviously be other reliable alternatives, though they have their own disadvantages, such as the fact that they will not reflect glycemic control over a longer period of time, as does the HbA1c, and need repeated measurements. Moreover, an abnormal HbA1c level in a diabetic patient or any other subject during routine evaluation or screening for diabetes could raise the suspicion of an Hb variant. Therefore, physicians and especially endocrinologists should take this fact into account and immediately seek further evaluation and investigations to confirm the diagnosis of Hb variants in such patients and advise the patients to screen their family members.
Data and other materials can be made available if required.
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Rashmi Karki, Samir Lamichhane, and Rekha Manandhar have contributed equally to this work.
National Public Health Laboratory (NPHL), Kathmandu, Nepal
Rashmi Karki, Runa Jha & Rekha Manandhar
Department of Clinical Pharmacology, Maharajgunj Medical Campus (MMC), Institute of Medicine (IOM), Tribhuvan University (TU), Kathmandu, Nepal
Samir Lamichhane
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RK and RM conceptualized and designed the case report. The acquisition, analysis, and interpretation of patient data were also performed by RK and RM. The collection and assembly of relevant literature and background information, as well as writing and revising the manuscript focusing on the clinical aspects of the case, were done by SL. RK assisted SL in drafting and revising the manuscript, with a focus on the literature review. RK also contributed to the intellectual content and critical revision of the manuscript. Supervision and mentorship throughout the case report development were done by RJ and RM. All authors have read and approved the final version of the manuscript to be published.
Correspondence to Samir Lamichhane .
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Karki, R., Lamichhane, S., Jha, R. et al. An incidental finding of a hemoglobin E variant in a diabetic patient with an abnormal glycated hemoglobin level: a case report. J Med Case Reports 18 , 279 (2024). https://doi.org/10.1186/s13256-024-04518-y
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Received : 06 December 2023
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DOI : https://doi.org/10.1186/s13256-024-04518-y
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solomon endris, ethiopian institute of agricultural research, debre zeit research center, ethiopia., main article content, urban crop production and natural resource management practices, challenges, and intervention options in addis ababa, tolesa alemu, solomon endris, getachew tabor, melkamu demelash, samuel diro.
The population of Addis Ababa is growing at rapid pace and currently reaches about 5 million. Food shortage, unemployment of youths and women, and increasing prices of major food items are critical constraints. In spite of the efforts to overcome the limitations, information is lacking on urban crop production and natural resource development and management practices to take informed decisions to enhance urban crop production and environmental sustainability. Therefore, this study was undertaken in Addis Ababa to identify and generate information on urban crop production and natural resource management practices, bottlenecks of the practices, and recommend possible intervention options to mitigate the challenges. Quantitative and qualitative secondary and primary data were collected through review of secondary sources and sample survey of urban producers and stakeholders using distinctive checklists. Data were collected through focus group discussions, key informant interviews, and matrix rankings by multidisciplinary research team. The collected data were analyzed using thematic and narrative analyses to achieve objectives of the study. Most of urban producers grow Swiss chard, lettuce, head cabbage, Ethiopian kale (gomen). These crops are selected due to their short life cycle (could be grown three to four times annually), ease of cultivation and low disease incidence. Carrots, beet roots, cauliflower, garlic, onion and potatoes were also grown by some producers. A few producers grew spices and high value crops such as leeks, chives, celery, zukuni, parsley, spinach and spices like coriander. Cereal crops and mushrooms were also produced by limited number of producers. Managing tree seedling nurseries, afforestation and reforestation, and agroforestry practices are carried out to develop and manage natural resources and keep ecological balance. However, shortage of improved technologies, land, and water supply were main constraints in the city. Environmental degradation, inadequate waste disposal and management, limited waste recycling and reuse, and food safety and quality were constraints in Addis Ababa. Overcoming the challenges need involvement of all stakeholders to jointly plan, formulate policy and strategy, and take coordinated and targeted actions.
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COMMENTS
3. Identify the article. Start your review by referring to the title and author of the article, the title of the journal, and the year of publication in the first paragraph. For example: The article, "Condom use will increase the spread of AIDS," was written by Anthony Zimmerman, a Catholic priest.
2. Read the article thoroughly: Carefully read the article multiple times to get a complete understanding of its content, arguments, and conclusions. As you read, take notes on key points, supporting evidence, and any areas that require further exploration or clarification. 3. Summarize the main ideas: In your review's introduction, briefly ...
Article Review vs. Response Paper . Now, let's consider the difference between an article review and a response paper: If you're assigned to critique a scholarly article, you will need to compose an article review.; If your subject of analysis is a popular article, you can respond to it with a well-crafted response paper.; The reason for such distinctions is the quality and structure of ...
Step 1: Define the right organization for your review. Knowing the future setup of your paper will help you define how you should read the article. Here are the steps to follow: Summarize the article — seek out the main points, ideas, claims, and general information presented in the article.
An article review is a critique or assessment of another's work, typically written by experts in the field. It involves summarizing the writer's piece, evaluating its main points, and providing an analysis of the content. A review article isn't just a simple summary; it's a critical assessment that reflects your understanding and ...
Here is a basic, detailed outline for an article review you should be aware of as a pre-writing process if you are wondering how to write an article review. Introduction. Introduce the article that you are reviewing (author name, publication date, title, etc.) Now provide an overview of the article's main topic.
Step 2: Read the Article Thoroughly. Begin by thoroughly reading the article. Take notes on key points, arguments, and evidence presented by the author. Understand the author's main thesis and the context in which the article was written.
For an article review, your task is to identify, summarize, and evaluate the ideas and information the author has presented. You are being asked to make judgments, positive or negative, about the content of the article. The criteria you follow to do this will vary based upon your particular academic discipline and the parameters of your ...
Read the Article Thoroughly. The first step in writing an article review is to read the article carefully and thoroughly. This may seem obvious, but it is crucial to ensure a comprehensive understanding of the work before attempting to critique it. During the initial reading, focus on grasping the main arguments, key points, and the overall ...
Step 4: Make an Introduction. In your introduction, provide a brief overview of the title's subject and purpose. Capture the reader's attention and clearly state your thesis or main point related to the title. For instance, you might start your article review template like this.
Example 1: Title of the Article Review. In this example, we present a review of an article titled "Exploring the Impact of Climate Change on Biodiversity." The review delves into the author's research methodology, provides a detailed analysis of the findings, and offers insights into the implications of the study.
An article review is a critical assessment of a scholarly article or research paper. It involves analyzing the content, methodology, and findings of the article and providing an evaluation of its strengths and weaknesses. The review typically includes a summary of the article's main points, an evaluation of its contribution to the subject ...
Example. Following, we have an example of a summary and an evaluation of a research article. Note that in most literature review contexts, the summary and evaluation would be much shorter. This extended example shows the different ways a student can critique and write about an article. Citation. Chik, A. (2012).
A journal article review is written for a reader who is knowledgeable in the discipline and is interested not just in the coverage and content of the article being reviewed, but also in your critical assessment of the ideas and argument that are being presented by the author. Your review might be guided by the following questions:
Start the first paragraph of your review with concise and clear article identification that specifies its title, author, name of the resource (e.g., journal, web, etc.), and the year of publication. Intro. Following the identification, write a short introductory paragraph.
A review article is a type of professional essay writing. So you need to study its subject carefully. Use multiple sources and highlight the main arguments. Then form your own opinion on the given topic. In conclusion of your article review, you should bring new arguments for or against the author's opinion.
Actions to Take. 1. Skim the article without taking notes: Read the abstract. The abstract will tell you the major findings of the article and why they matter. Read first for the "big picture.". Note any terms or techniques you need to define. Jot down any questions or parts you don't understand.
Article Review Example "While the article presents compelling evidence linking social media usage to mental health issues, it is important to acknowledge some limitations in Smith's study. The sample size of the research was relatively small. It comprises only 100 participants, which may limit the generalizability of the findings.
Here, I provide tips on planning and writing a review article, with examples of well-crafted review articles published in The FEBS Journal. ... Most review articles are between 4000 and 6000 words in length and as a rule of thumb, 80-90% of the text should be within the main section/devoted to the core topic—make sure that your outline ...
An article review is a critical evaluation of an article. To write an article review, you select and read an article carefully, and summarize the author's main ideas and research findings. You then provide your own evaluation and critique based on your analysis of the article and your knowledge of the topic. Begin assignment. Assignment due ...
A review article can also be called a literature review, or a review of literature. It is a survey of previously published research on a topic. It should give an overview of current thinking on the topic. And, unlike an original research article, it will not present new experimental results. Writing a review of literature is to provide a ...
The fundamental rationale of writing a review article is to make a readable synthesis of the best literature sources on an important research inquiry or a topic. This simple definition of a review article contains the following key elements: The question (s) to be dealt with.
Below, you can find examples of MLA and APA format article review. APA Article Review APA style article review is one of the two most common formats. In a nutshell, if you were assigned to write an article review APA, it means that you will need to format your citations according to this style manual. The rest of the paper will have standard ...
His hit single, "Houdini," is the latest example of Eminem, for more than a decade now, successfully defying every aging critic and each disillusioned fan imploring him to grow up
Chip and Joanna discuss plans for the lake house exterior. (Magnolia Network) There are plans to turn an old flower bed inside the home's courtyard into a koi pond with a water feature of some sort.
This paper reviews the literature on agility and its relationship with organisational performance using a sample of 249 recent empirical studies from 1998 to February 2024. We find support for a relatively strong and consistent contribution of different aspects of agility to organisational performance. Our analysis highlights numerous salient issues in this literature in terms of the ...
The authors explain and offer examples of how onboarding that truly helps new employees thrive in the modern workplace is less about face time and more about intention, structure, and resources.
The recent accelerated rise in global interest rates, the fastest in decades, brought the curtain down on an extended period of cheap money but provided little clarity on the longer-term outlook.In 2024, competing forces of tepid growth, geopolitical tension, and regional conflict are creating nearly equal chances of higher-for-longer benchmark rates and rapid cuts.
Glycated hemoglobin (HbA1c), initially identified as "unusual" hemoglobin in patients with diabetes over half a century ago [], has now become a well-known marker of long-term glycemic control in individuals with diabetes mellitus [], reflecting an average blood glucose level over a period of around 3 months.Glycemic control being an important factor for the progression to long-term ...
Quantitative and qualitative secondary and primary data were collected through review of secondary sources and sample survey of urban producers and stakeholders using distinctive checklists. Data were collected through focus group discussions, key informant interviews, and matrix rankings by multidisciplinary research team.