Client Behavior Research Articles

Patient experiences of behavioural therapy for bipolar depression: A qualitative study.

Although multiple qualitative studies have explored participants' experiences of behavioural activation (BA) for unipolar depression, none have investigated the experiences of BA in people with bipolar depression. This is of particular interest because qualitative studies concerning the experience of receiving therapy can help inform the theory of change underpinning the intervention. The purpose of this study was to explore the experiences and perspectives of individuals with bipolar disorder who received a course of one-to-one BA for bipolar depression. We sought to explore participants' experience of the effects of BA therapy, both proximally and distally. Semi-structured interviews were conducted with nine individuals meeting research diagnostic criteria for bipolar I or II disorder who had received up to 20 sessions of BA adapted for bipolar depression. Thematic analysis using a framework approach was used to explore and describe the experiences of participants. Participants' perspectives on the impact of therapy were categorized under four subthemes: client behaviour inside and outside sessions, changes in clients' perspectives, the impact on symptoms and impact on life and functioning. Participants' accounts of the impact of therapy were broadly consistent with the theory underpinning a behavioural approach. Participants described a central role for perspective change, and particularly increased acceptance of the self and mood states, as facilitating behavioural changes and more distal benefits. Process evaluations embedded in future trials may include quantitative measures of key processes described by our participants, as well as those clearly implied by the behavioural theory of depression.

Using co-design to understand consumer’s health information-seeking behaviours and design preferences for a new digital clinical dashboard in aged care

BackgroundLittle is known about the information needs of older people and their family caregivers, particularly around medication management. This is largely because this population are infrequently consulted in research. Health technologies such as digital dashboards can present comprehensive and timely data summaries to improve knowledge and guide decision-making.ObjectivesThe aim of this study was to understand current information seeking-behaviours of aged care service users and their families, and their preferences for the presentation of these types of information to support the co-design of a digital aged care dashboard.MethodsAged care clients (community and residential aged care) and their family members were invited to participate in semi-structured, one-on-one interviews conducted over the telephone. The interviews explored how participants sought health information, how this information supported their clinical decision-making, and how they would want this information to be presented via a digital dashboard. Interviews were audio-recorded, transcribed verbatim and independently analysed using an inductive content approach by three reviewers to identify prominent categories.ResultsAged care clients and family members sought health information from healthcare providers, aged care facilities and the Internet. Information regarding medications, medical conditions and an overview for family members were of particular importance to participants. Participants saw the usefulness of a digital dashboard displaying relevant information, and provided suggestions regarding presentation of this information. This included the use of large text, representative colours, symbols and graphs. Participants recommended being able to tailor the dashboard to individual end-users.ConclusionsBy engaging aged care clients and their families in research, it was found that they had positive views about a digital dashboard that presented clinical information and was readily accessible. Such a dashboard could complement their current practices of information-seeking. However, for the dashboard to be effective for this population, several suggestions for its design were raised. Understanding the information-seeking behaviours of aged care clients and their families, together with knowledge of the information that is important to them, and then subsequent incorporation of this information into a digital dashboard that reflects their design preferences, could lead to more informed decision-making in this population.

Strategic approaches to enhancing credit risk management in microfinance institutions

Microfinance institutions (MFIs) play a critical role in providing financial services to underserved, low-income populations, yet face significant challenges in managing credit risk due to the nature of their clientele and loan structures. Enhancing credit risk management in MFIs is essential for ensuring financial stability, reducing default rates, and fostering sustainable growth. This review examines strategic approaches to credit risk management tailored to the microfinance context, where traditional credit assessment methods may be less effective. It explores innovative client profiling techniques, such as segmented risk analysis and customized credit scoring models, which leverage demographic and behavioral data to more accurately assess borrower reliability. The use of data-driven decision-making, particularly through predictive analytics and machine learning, is highlighted as a means to anticipate client repayment behaviors and improve risk forecasting. Further, real-time loan monitoring systems with early warning indicators offer MFIs proactive risk mitigation strategies, enabling prompt interventions to prevent defaults. Institutional improvements, including standardized risk frameworks, enhanced training for loan officers, and regular compliance checks, strengthen organizational resilience against credit risks. Portfolio diversification strategies, such as sectoral and geographic diversification, and the integration of group lending models, are also discussed as effective means of reducing risk concentration. By implementing these multifaceted, strategic approaches, MFIs can improve their credit risk management frameworks, better serve their clients, and achieve greater financial sustainability. The review emphasizes the importance of adapting to rapidly evolving risk factors and adopting innovative tools and practices to meet the unique challenges of microfinance. The findings advocate for continuous research and investment in advanced credit risk methodologies, ultimately contributing to the broader goal of financial inclusion.

AI-Driven Fraud Detection in Banking: Enhancing Transaction Security

An important step forward in risk management and fraud detection has been achieved with the integration of Artificial Intelligence (AI) in the banking sector. In this paper, we take a look at how AI has revolutionized various fields, shedding light on the benefits and drawbacks of this technology. The effects of AI on risk management are complex. More complex credit risk assessment models are made possible by algorithms that can see patterns in massive datasets that people might miss. When it comes to market and liquidity issues, real-time transaction monitoring is absolutely essential for quick risk mitigation. Automating compliance with regulatory norms is another critical function of AI, which helps to decrease human mistake and assures quick adaptability to changes in regulations. The automation of mundane processes and the reinforcement of cybersecurity measures further reduce operational risks. By examining client behaviour and transaction data, enhanced algorithms may adeptly spot anomalies that could indicate fraud. Artificial intelligence's capacity to foresee future events enables it to foil possible fraud attempts. The systems are designed to respond to changing fraudster strategies with its adaptive learning feature.

Veterinary medicine under COVID-19: a mixed-methods analysis of student and practitioner experiences in Austria.

This study aimed to provide a detailed analysis of the pandemic's impact on the veterinary profession and education in Austria. Two online surveys were conducted from November 2022 to January 2023, inviting all veterinarians and veterinary students in Austria to share their experiences on the impact of the pandemic on their veterinarian work and education in a free text question. A total of n = 289 veterinarians and n = 272 veterinary students provided an answer. In summary, 39.8% of veterinarians reported no impact by the pandemic, while others experienced changes in workload (19.7%), changes in client behavior (14.9%), implementation of safety measures (13.1%), and organizational changes (13.1%). Additional impacts included effects on mental health, private life, finances, as well as physical health. Changes were more frequently mentioned in veterinarians working with pets compared to those working with livestock. In the student panel, only a minority reported no significant impact (8.1%) but 44.9% reporting changes due to online and hybrid learning. Key issues included impaired learning conditions (34.9%), and social distancing (34.6%) leading to social isolation. Some students appreciated increased flexibility and reduced commuting (10.7%), while others experienced mental health challenges (10.7%). Further impacts were related to the implementation of safety measures (5.5%), organizational changes (4.8%) and impaired physical health due to infection (1.1%). Negative changes related to distance learning were mainly reported by students in the final study phase, whereas those in the first study phase reported more positive aspects related to online/hybrid learning. Overall, this study highlights the significant impact of the COVID-19 pandemic on veterinary education and professional practice in Austria, revealing diverse challenges for students and relatively lower but still notable effects on practicing veterinarians. Future research should monitor these impacts longitudinally and explore the integration of beneficial practices into standard veterinary education and care, such as effective digital learning platforms and appointment-based systems.

Symbolic Bureaucratic Representation and Client Cooperation: Experimental Insights From Four Daily Public Service Scenarios in China

ABSTRACTRepresentative bureaucracy studies have explored the potential impact of symbolic representation on clients' attitudes and behaviors, even in the absence of direct action by bureaucrats. Through four original conjoint experiments conducted with a nationally representative sample of 1600 participants in China, we analyze the symbolic representation effects of five most commonly observable identity signals, including gender, age, accent, party affiliation, and human voice (versus AI chatbot), across two types of bureaucrat‐initiated contacts and two types of client‐initiated contacts. Our findings contribute to the existing literature by demonstrating that identity congruence has heterogeneous symbolic representation effects; clients' expectations about active representation serve as a significant mediating factor influencing their willingness to cooperate with bureaucrats.

Using Instructions and Acoustic Feedback to Improve Staff Delivery of Behavior-Specific Praise in a Clinical Setting.

Past research has shown the effectiveness of contingent acoustic feedback (CAF) in various human performance settings, but its impact on staff performance in clinical settings remains unclear. The current study replicated and extended Herron et al. (2018) by using vocal instructions and CAF to teach staff to use behavior-specific praise (BSP) to reinforce designated client behavior in a clinical setting. Results from a multiple-baseline-across-staff design revealed that this intervention increased the rate at which staff used BSP to reinforce designated client behavior. Paired sample t-tests showed a significant increase in the rate of BSP for each participant from baseline to the intervention phase, and participants maintained increased rates of BSP during probes conducted 1 to 3 weeks after the intervention was complete. Additionally, paired sample t-tests showed statistically significant increases in the rates of designated behavior for each client. Participants also rated the instructions and CAF procedure as more worthwhile, helpful, relevant, pleasant, and less disruptive than feedback methods typically used in their agency.

A robust federated learning algorithm for partially trusted environments

Due to the distributed nature of federated learning, it is vulnerable to poisoning attacks during the training process. The model’s resistance to poisoning attacks can be improved using robust aggregation algorithms. Current research on federated learning to resist poisoning attacks is mainly based on two settings: No trust or Byzantine robustness. However, both settings are not close enough to reality in practical scenarios. In many practical applications, some participants in federated learning are trustworthy. For example, participants who have participated in the training of this model before and performed very well, or participants with strong compliance and credibility such as governments and some national agencies participate in the training. In existing research, these trusted participants still have to accept the judgment of the aggregation node, which generates unnecessary computation, increases overhead, and does not take advantage of a trusted environment. Since there is no attack behavior on the trusted client, its training results are used to classify the trustworthiness of other untrusted clients and identify attack nodes with higher accuracy. Therefore, this paper proposes a robust federated learning algorithm for partially trusted environments. The proposed scheme uses the experimental results of trusted clients to judge the behavior of untrustworthy clients by the cosine similarity and the Local Outlier Factor and further identify and detect malicious clients. Experiments are performed on MNIST and CIFAR datasets. Comparison with other six aggregation algorithms under 30% attack scenario. And compared with the other four aggregation algorithms under 70% attack conditions. Our algorithm is more accurate than almost all of the other aggregation algorithms. The paper is the first to conduct robust research on federated learning in a partially trusted environment, and the proposed algorithm can more effectively resist poisoning attacks.

Development of a method for refining recommendations using temporal knowledge in individual insurance tasks

The subject of the research is the process of constructing recommendations in individual insurance tasks. The aim is to develop an approach for building recommendations in personalized insurance projects using temporal knowledge to adapt proposed insurance plans according to client behavior on insurance company websites. Objectives: to structure temporal rules considering the peculiarities of the online insurance recommendation process; to develop a method for constructing recommendations for insurance product selection using temporal knowledge. Conclusions: temporal rules have been structured for the task of building recommendations in personalized insurance projects. These rules generalize behavior in relative time for multiple users, enabling the generalization of user action sequences. A method for constructing online recommendations using temporal knowledge has been developed. The method consists of two phases: temporal knowledge formation and recommendation construction. The first phase is performed offline and is designed to form a base of temporal rules that generalize knowledge about user behavior. The second phase is executed online and involves refining and further utilizing temporal rules to adapt recommendations according to current user behavior. The method enables prompt adaptation of recommendations considering the user's current actions, creating conditions for increasing user trust in the recommender system's proposals.

Model based on Machine Learning for the classification of banking transactions carried out through PSE

The financial sector, and specifically banking entities, have experienced changes in recent years thanks to technology, such as the digitization of transactions and the creation of applications such as digital wallets and PFM (Personal Finance Manager), generating gigabytes of information. Managing knowledge becomes essential to face new competitors, provide better services, understand the financial behavior of clients and face great challenges when processing and analyzing the volume of information available, which in most cases requires a complex preprocessing process and data quality. This is the case of banking transactions, which include free text information in their observation fields, making analysis and classification difficult, preventing the bank and its clients from analyzing financial behavior over a period of time. To solve this problem, the use of Machine Learning techniques was proposed to automate the transaction classification process based on text written in natural language, and provide the information that allows an analysis of the financial behavior and personal expenses of each user. Once the training, evaluation and comparison of different models was completed, using the CRISP-DM methodology as a development framework, an optimized solution was reached that solves the classification problem using the KNN algorithm, with an accuracy close to 96%. The results showed a high level of confidence when classifying a transaction, based on a description, into a category.

“The client can get caught out”: Tax structure maintainability and the intricacies of tax planning aggressiveness

AbstractIn this field study, we examine tax advisors' decision‐making process when developing tax planning arrangements. Through interviews with 40 tax advisors, our analysis indicates that tax savings may come at a price in practice by unveiling adverse post‐implementation experiences shared by tax partners. Partners find themselves in a tricky position at the time they form their recommendation as they cannot be certain that their client will be able to “live with their tax structure”—that is, maintain it and cope with the inherent risks once implemented. Their main concern is that their client may “get caught out” by a structure too aggressive or complicated for them, having no control over the client's behavior once the plan is implemented. This has significant implications in the tax planning decision‐making process as these concerns shape how partners adapt their work to their client's perceived competency and possibly restrain corporate firms' tax aggressiveness. Following Feller and Schanz (2017, Contemporary Accounting Research, 34(1), 494–524), we conceive of this as the fourth hurdle of tax planning— whether a tax structure is maintainable, as perceived by tax advisors—and unpack how it operates. Interestingly, restraining the client's tax planning aggressiveness (and the corresponding potential tax savings) is not necessarily perceived by partners as detrimental to the client relationship. Our findings contribute to a better understanding of tax planning in action, highlighting how tax partners seek to influence their client's tax planning aggressiveness.

Sexual Misconduct & Inappropriate Client Sexual Behavior in OT

Abstract Date Presented 03/23/24 Investigators surveyed OT practitioners regarding sexual misconduct. Results documented that sexual misconduct and inappropriate client sexual behavior occur with regularity in OT. This study may guide policies on sexual misconduct in OT practice. Primary Author and Speaker: Brenda S. Howard Additional Authors and Speakers: Erin Clarke, Kaitlyn S. Ditmars, Hannah John, Ryan Smith, Sydney Wiedeman, Janel S. Williams

Automated Behavioral Coding to Enhance the Effectiveness of Motivational Interviewing in a Chat-Based Suicide Prevention Helpline: Secondary Analysis of a Clinical Trial.

With the rise of computer science and artificial intelligence, analyzing large data sets promises enormous potential in gaining insights for developing and improving evidence-based health interventions. One such intervention is the counseling strategy motivational interviewing (MI), which has been found effective in improving a wide range of health-related behaviors. Despite the simplicity of its principles, MI can be a challenging skill to learn and requires expertise to apply effectively. This study aims to investigate the performance of artificial intelligence models in classifying MI behavior and explore the feasibility of using these models in online helplines for mental health as an automated support tool for counselors in clinical practice. We used a coded data set of 253 MI counseling chat sessions from the 113 Suicide Prevention helpline. With 23,982 messages coded with the MI Sequential Code for Observing Process Exchanges codebook, we trained and evaluated 4 machine learning models and 1 deep learning model to classify client- and counselor MI behavior based on language use. The deep learning model BERTje outperformed all machine learning models, accurately predicting counselor behavior (accuracy=0.72, area under the curve [AUC]=0.95, Cohen κ=0.69). It differentiated MI congruent and incongruent counselor behavior (AUC=0.92, κ=0.65) and evocative and nonevocative language (AUC=0.92, κ=0.66). For client behavior, the model achieved an accuracy of 0.70 (AUC=0.89, κ=0.55). The model's interpretable predictions discerned client change talk and sustain talk, counselor affirmations, and reflection types, facilitating valuable counselor feedback. The results of this study demonstrate that artificial intelligence techniques can accurately classify MI behavior, indicating their potential as a valuable tool for enhancing MI proficiency in online helplines for mental health. Provided that the data set size is sufficiently large with enough training samples for each behavioral code, these methods can be trained and applied to other domains and languages, offering a scalable and cost-effective way to evaluate MI adherence, accelerate behavioral coding, and provide therapists with personalized, quick, and objective feedback.

Examining the role of expectations in outsourcing success utilizing the hierarchy of effects model

PurposeThis paper aims to involve both the development of a quantitative measure of outsourcing success that integrates recent research findings on expectations and applying the hierarchy-of-effects (HOE) model to investigating the influence of success on client satisfaction and recommendation intention.Design/methodology/approachThis paper conducted a global survey of information systems managers and Chief Information Officers from firms who have engaged in outsourcing and analyzed the data using partial least squares (PLS).FindingsThe study analysis demonstrates the impact of client expectations on perceived outsourcing success, client satisfaction and intention to recommend. This paper also discusses how findings of this study provide important implications for both researchers and practitioners.Originality/valueTo further investigate the theoretical trend toward examining the impact of expectations on outsourcing success, this study extends the foundational success research by quantitatively demonstrating the robustness of an outsourcing success construct that incorporates expectations. Moreover, this study extends the traditional models of success by incorporating factors from each of the stages of client behavior, including cognition, affect and conation.

Bank Reputation and Trust: Impact on Client Satisfaction and Loyalty for Portuguese Clients

The aim of this article is to assess the most relevant factors influencing customer trust in the Portuguese banking sector following the Global Financial Crisis. It also aims to evaluate the impact of trust on satisfaction and satisfaction on loyalty. To address the research objectives and the hypotheses posed, a quantitative study with a descriptive design was conducted. Data was collected through an online survey administered to a sample of bank clients residing in Portugal. The findings indicate that respondents generally trust Portuguese banking institutions, although this trust has been affected by the Global Financial Crisis. The bank’s reputation and financial performance were identified as critical factors in the respondents’ choice of bank. Additionally, the results suggest that both global and domestic financial conditions, bank reputation, client satisfaction, and overall trust significantly influence client loyalty to the bank. This study provides valuable insights into client behavior and perceptions of banks, emphasizing the importance of factors such as trust, client satisfaction, and bank reputation in shaping client loyalty.

Development and Management of E-Commerce Information Systems Using Edge Computing and Neural Networks

E-commerce refers to implementing e-commerce on mobile devices, cell phones, and mobile Internet connections. The fast advancement of mobile electronic gadgets and mobile Internet connections has facilitated the emergence of this new e-commerce market. The e-commerce systems contain several concealed client behavior data and forthcoming growth patterns. Data mining technologies extract valuable data and facilitate the growth of e-commerce. This article focuses on studying mobile e-commerce systems within the framework of edge computing, specifically in the setting of industrial clusters. This study examines the importance and benefits of data mining techniques in implementing e-commerce management platforms using neural networks. It also evaluates the corresponding methods of data mining and predicts purchasing trends. The study has used the benefits of grouping and back propagation neural network approaches in data analytics to categorize goods details, buying tastes, and related data. The advantage of neural networks in processing nonlinear patterns is employed to forecast forthcoming buying power. The findings demonstrate that data analytical methods and neural networks exhibit higher precision in predicting the buying pattern. The correlating factor between actual and projected usage information was 0.98, with the highest relative average inaccuracy of 2.4%. Data mining technologies effectively extract previously unrecognized relevant data and anticipated buying patterns in e-commerce platforms. Neural networks have a solid ability to predict future consumption capacity and consumption behaviors accurately.

A Quantitative Analysis of Default Risk Using Machine Learning and SHAP Value Interpretation

Abstract In finance, creating a model that balances risk reduction with opportunity is essential. This investigation addresses the necessity for risk evaluation frameworks that combine efficiency with adaptability, thus preserving opportunities for transactions critical to some organizations. The present study identifies, within a factoring process involving two key players: the invoice seller and the debtor, the essential variables that determine the likelihood of the debtor defaulting on the invoice payment. The event of non-payment is most often associated with the debtor's inability to pay due to insolvency, making it crucial in this type of activity to emphasize an efficient credit scoring system capable of proactively highlighting a debtor company with a high risk of default. Nonetheless, some companies pass this filter and enter the factoring process, but end up being unable to pay. The study identifies them based on a set of real data and uses supervised machine learning techniques to select the optimal classification model, also highlighting the variables with a major impact on the target. The specialized literature is focused on identifying the models that perform best in the credit scoring activity or studies that identify the non-payment behavior of clients. What this work adds is the combination of these two dimensions, for example, it provides an additional filter to credit scoring, using parameters identified as essential in determining defaulters and using them as inputs for an unsupervised learning model, thus classifying the entire population of companies in Romania to identify clusters containing the highest proportion of non-payment companies.

Audit Market Concentration for Listed Entities in Romania and the Impact Following the Latest EU Audit Reform

Abstract The concentration of the audit market has been of interest for several years as the dominance of a few companies has always been an issue of debate. The need or not to address this situation, the reasons that cause it, any possible risks assessed and solutions to reduce them have been addressed by a wide range of studies. Our research seeks to examine the structure of the Romanian Audit Market for listed entities, both before and following the recently adopted European Union Audit Reform. The main purpose is to understand the market structure, identify some of the main factors that influence auditor choice and if there was any impact following the new directive. The analyses focused on 74 listed companies, for the period from 2013 to 2022, for which we collected the relevant data in order to analyze the audit market structure, its evolution following the new audit directive and, using linear regressions models, test the influence that size, share class category, existence of long-term debt, shareholder type, evidence of shareholder direct involvement and auditor office location has on choosing one of the Big 4 auditors or not. Our analysis showed that Big4 auditors ecreased in market share especially after 2016, the date the new audit reform came into effect, from 49% to 41% and that the number of auditor changes increased significantly after this date. We also found that the number of cases where another Big4 auditor was selected decreased from 64%, in the period from 2013-2016, to only 28%, in the period from 2017-2022. These findings support therefore the assumed existence of an impact in the market following the latest European Union audit directive. The linear regression analysis performed showed no significant influence for the variables analyzed, when it comes to choosing a Big4 Auditor, the highest coming from the share class variable (26%) and size (12%). Even though our paper offers some light regarding the audit market for listed companies in Romania and the evolution following the new directive further research is needed to better understand client behavior and what influences auditor choice.

The Transmission of Values Specific to Midwifery and their Integration by Student Midwives

Québec midwives adhere to a set of values viewed as essential. These values set midwives apart from other obstetrics professionals and play an important role in the formation of their professional identity. This article explores whether these values are transmitted to student midwives. To achieve our research objectives, we presented 21 student midwives with scenarios where a client's behaviour corresponds only more or less to midwifery values (as defined by the regroupement Les sages-femmes du Québec). Student responses were analyzed according to their level of correspondence with these values. The results show that regardless of the scenario, first-year students are most likely to form negative attitudes about the behaviour submitted to their attention and, contrary to their more advanced sisters, they base interventions with a client less on the need to refrain from judgment. Starting with the second year, the students' evaluations of behaviour tend to be neutral. The difference observed between the first-year cohort and the others leads us to believe that the start of the mentoring stage is the pivotal moment of training.

Construction framework process: a qualitative investigation of behaviour change drivers and project outcomes

Purpose Construction framework agreements are identified by the UK Government’s Construction Strategy 2025 as an integrated procurement path to improve construction industry efficiency. However, criticisms from the industry have arisen from the lack of transparency and incorrect application of such frameworks. This paper aims to examine the client and supplier relationships within a framework agreement to discover what behaviour change drivers should be applied in the framework process to achieve the desired project outcomes. Design/methodology/approach A triangulation approach was adopted. An initial literature review on organisational behaviour and job performance theories was conducted followed by a qualitative expert review survey of client construction managers to confirm the project outcomes desired by public-sector clients and the associated behaviour change drivers. This was followed by a qualitative multiple-case study investigation of eight typical framework projects to form a commonality of views to explain the impact of behaviour change drivers on project outcomes. Findings Results from both qualitative studies demonstrated that improvements in project outcomes of time, cost, quality, sustainability and closer relationships can be driven by two sets of behaviour drivers: client organisational behaviour change drivers (setting up incentive and risk-sharing procurement approach, effective communication through development of stronger relationships and performance monitoring using contract key performance indicators) and supplier contextual behaviour change drivers (motivating conscientious behaviour and attitude towards self-improvement and innovations, supporting shared culture of providing services aligning with the client needs, promoting learning and development amongst all suppliers and most importantly providing trust and collaboration to the client). Research limitations/implications The investigation was based on an expert review with eight multiple-case studies conducted within the geographical area of England. Further research should be conducted nationwide so that the findings can become more robust and benefit the entire public sector. Practical implications It is suggested that framework managers should apply the supplier contextual performance drivers as selection criteria in the procurement process, whilst the client organisational performance drivers should be applied in the whole procurement and construction monitoring process to drive project outcomes aligned with the government construction policy objectives. Originality/value This research demonstrates that the project behaviour of clients and suppliers can be shifted within the construction framework environment to yield the desired project outcomes. This can be achieved by applying the client organisational behaviour drivers and the supplier contextual behaviour change drivers in tandem to optimise the framework process.