Enhancing performance,self-efficacy and well-being: A randomised controlled study in solution-focused business coaching

Abstract

This randomised controlled study examines the effectiveness of Solution-Focused Brief Coaching (SFBC) in an organisational setting involving 84 white-collar workers (Experimental Group, EG: 43, Waitlist Control Group, WCG: 41). Over three coaching sessions, the EG demonstrated significant improvements in performance, self-efficacy, well-being, and positive affect, while SFBC also effectively reduced negative affect. These outcomes were measured using both self-reported questionnaires and 360-degree performance evaluations. Notably, the positive effects were maintained four months after the coaching process. The study highlights SFBC as a powerful, time-efficient intervention for organisations, offering a practical approach to sustaining employee performance and well-being over time.

Keywords

solution-focused brief coaching business coaching performance self-efficacy well-being

Introduction

Coaching is increasingly seen as a socially accepted form of support, especially compared to traditional psychological interventions (Cavanagh & Buckley, 2014). It is also recognised as one of the fastest-growing interventions in human resources (Bozer & Delegach, 2019). Organisations increasingly employ business and executive coaching to enhance the competencies, performance, self-efficacy, and well-being of leaders and employees (Cannon-Bowers et al., 2023; Corbu, Peláez, Zuberbühler, & Salanova, 2021; Grover & Furnham, 2016). According to the International Coaching Federation (ICF, 2020), approximately 71,000 coaches worked worldwide in 2019, with over two billion US dollars invested annually in workplace coaching. As a result, organisations have a growing demand for empirical evidence that supports the effectiveness of coaching.

In response to this demand, coaching psychology has experienced significant growth, with a notable increase in empirical research over the past two decades (e.g., Fillery-Travis & Corrie, 2018; Jones, Woods, & Guillaume, 2015). Numerous high-quality studies (e.g., Grant, 2013; Ladegård & Gjerde, 2014), meta-analyses (e.g., Cannon-Bowers et al., 2023; Sonesh et al., 2015; Theeboom, Beersma, & van Vianen, 2014; Wang, Lai, Xu, & McDowall, 2022), and systematic reviews (e.g., Athanasopoulou & Dopson, 2018; Bozer & Jones, 2018; Grover & Furnham, 2016; Lai & McDowall, 2014) have been published. However, despite these advancements, the practical application of coaching still outpaces its theoretical foundation and empirical validation (Fillery-Travis & Corrie, 2018). Grover and Furnham (2016) emphasise that there is still a critical need for more randomised controlled trials (RCTs) to evaluate the effectiveness of coaching interventions despite the associated costs and challenges.

Literature Review

Defining Coaching Psychology

Over the past two decades, numerous review studies and book chapters (e.g., Bachkirova, 2008; Corbu et al., 2021; Palmer & Whybrow, 2006; Whybrow, 2008) have explored the role of psychological principles, theories, and models in the coaching process. However, coaching psychology needs a universally accepted definition among organisations and professionals (Lai & Palmer, 2018). Generally, it is agreed that coaching psychology has its roots in the humanistic psychology movement of the 1960s – often referred to as the "Third Force". It is fundamentally seen as a person-centred or client-centred approach (Palmer & Whybrow, 2007), relying heavily on empathy, congruence, and unconditional positive regard (e.g., Joseph & Bryant-Jefferies, 2007).

Whybrow and Palmer (2018) emphasise that the early 21st century was crucial for the growth of coaching psychology, driven by the positive psychology movement. According to Palmer and Whybrow (2017), the real breakthrough occurred when these two fields were integrated. Linley and Joseph (2004) suggest that coaching falls under the broader concept of positive psychology, as both share a common goal. Green and Palmer (2014) describe positive psychology coaching as an evidence-based practice that uses positive psychological theories and research to enhance resilience, performance, and well-being. According to the definition of the Australian Psychological Society, Coaching Psychology Interest Group (APS CPIG; 2023):

Coaching Psychology, as an applied positive psychology, drawing on and developing established psychological approaches. It was best understood as the systematic application of behavioural science to the enhancement of life experience, work performance and well-being, for individuals, groups and organisations who did not have clinically significant mental health issues or abnormal levels of distress.

Coaching and positive psychology emphasise the positive aspects of human nature, individual strengths, and performance enhancement (Linley & Harrington, 2005). Palmer and Whybrow (2017) confirm, based on an international survey examining the practical application of coaching psychology, that positive psychology has become increasingly integrated with the theory and practice of coaching psychology over the past decade.

However, Atad and Grant (2021) point out the differences between coaching psychology and positive psychology. They argue that while positive psychology coaching primarily aims to promote optimal functioning and well-being by developing personal strengths, approaches like solution-focused coaching also help clients identify and achieve practical solutions to their problems.

In addition to positive psychology, various other psychological approaches have also influenced the practice of coaching psychology (Palmer & Whybrow, 2007).

Psychological approaches in the theory and practice of coaching psychology

In a survey reported in the Handbook of Coaching Psychology (Whybrow & Palmer, 2018), practitioners were asked to identify the primary approach they use in their coaching psychology practice. The results showed that around 20/ of coaching psychologists use a cognitive behavioural approach, 10/ use a solution-focused cognitive behavioural approach, and 8/ use either a positive psychological or a solution-focused approach. However, most practitioners noted that it was difficult to select just one approach, as the best method often "depends on the person, the role and the organisation as a whole" (Whybrow & Palmer, 2018, p. 9). This observation aligns with the idea that the effectiveness of coaching interventions can vary depending on the context and individual needs, and there is often a significant overlap between different approaches (Nielsen & Miraglia, 2017).

Wang et al.'s (2022) comparative analysis indicate that psychologically informed coaching in the workplace has a positive impact on several outcomes, including objective work performance, goal attainment, cognitive learning outcomes (such as metacognitive skills), mental health, resilience, positive mood, job satisfaction, and organisational commitment. However, no single psychological approach was consistently more effective than the others, suggesting that integrative approaches may enhance coaching effectiveness.

In addition, Corbu et al. (2021) highlight micro-coaching as an effective method for organisations to save time and reduce costs, particularly in fast-moving and unpredictable environments. This targets concrete, short-term goals and requires fewer sessions than traditional coaching methods (Peláez et al., 2020). Iveson, George and Ratner (2017) further support this view, noting that while solution-focused brief coaching may not be superior to other types of coaching, it often achieves comparable results more quickly, making it a practical choice in dynamic settings.

Solution-Focused Brief Coaching

Solution-Focused Brief Coaching (SFBC) is derived from Solution-Focused Brief Therapy (SFBT), developed by de Shazer, Lipchik and Berg (Iveson et al., 2017). This approach emphasises identifying and using clients' strengths and solutions rather than focusing on problems (de Shazer, 1985; O’Connell & Palmer, 2018). SFBC is a short, strengths-based, results-oriented method that focuses on the present and future, aiming to achieve goal-directed, positive change and problem-solving (Berg & Szabo, 2005; Grant, 2011). Due to its strong emphasis on goals and outcomes, practitioners often use the term "goal-oriented" interchangeably with "solution-focused” (see Ives, 2008).

The SFBC process follows a structured framework of ten key steps (Berg & Szabo, 2005; Iveson et al., 2017; O’Connell & Palmer, 2018; Szabo, Meier, & Hankovszky, 2010). The first is the Preliminary Conversation, which precedes the formal contract. This "problem-free" conversation allows the coach and the coachee to discuss anything except the issue that brought them to the coaching process. The goal is to establish rapport and gather valuable information that might aid the coaching process, such as the coachee's strengths, values, and the language that resonates with them. The second, the Complaint Phase (optional), occurs when the coachee begins to describe their situation. Here, the coach clarifies how much time the coachee wants to spend discussing problems versus finding solutions, ensuring the conversation stays constructive. In the Pre-Session Change phase (optional), the coach may ask the coachee to observe any positive changes or progress before the first session. This step can enhance the coachee’s sense of self-efficacy by attributing these changes to their efforts. The fourth is the Contracting Phase, where the desired outcome of the coaching sessions is clearly defined. In a business coaching context, this may involve two contracts for organisational goals and individual coaching objectives. During the Visioning the Desired Future step, the coachee is guided to imagine their ideal future using the “Miracle Question”, a technique developed by de Shazer and colleagues (1986). This metaphorical tool helps the coachee envision the desired outcome and explore what life would be like if their goals were achieved. The sixth, Identifying Working Signs, focuses on uncovering the coachee's existing resources and strengths that are already contributing to their progress. The Scaling and Small Steps phase involves using a simple scale (usually 0 to 10) to measure progress, set goals, and plan the next steps.

In the Session Conclusion, the coach provides a summary acknowledging the coachee’s challenges, successes, and the concrete action plan they have developed, reinforcing the coachee’s resources and strategies for achieving their goals. The coach may suggest an Inter-Session Task/Experiment (optional) between sessions, inviting the coachee to try new behaviours or keep a journal to track positive changes. These tasks are framed within solution-focused principles, such as continuing what works and making small changes for significant results. Finally, during the Subsequent Sessions, the focus is on exploring areas of progress, understanding how these advances were achieved, and making the changes sustainable. The coach's initial question might be, "What has improved?" to reinforce the coachee's sense of progress and encourage integrating successful behaviours into daily life.

According to Iveson et al. (2017), the present- and future-oriented nature of solution-focused questioning can strengthen the coachee’s belief that positive change is inevitable. Szabo et al. (2010) highlight that using verbs like "recognise" and "notice" can activate the coachee’s creative abilities. When individuals start observing themselves from a fresh perspective, they may notice strengths and effective strategies they previously overlooked. This increased awareness can lead to a deeper connection with their resources and productive behaviours. Examples of solution-focused questions include: ”How will you know that things are getting better?"; “Who do you think will notice first when you begin to feel better?”.

Empirical studies (e.g., Grant, 2012; Theeboom, Beersma, & Van Vianen; 2016) comparing solution-focused and problem-focused questioning consistently emphasise that while problem analysis can help identify root causes in some cases, most coachees’ problems are too complex and systemic for a linear root cause analysis. In such instances, not only might problem-focused discussions be of limited utility, but they could also lead to negative emotional states. In contrast, solution-focused questioning tends to evoke positive emotions and boost self-efficacy.

Richmond, Jordan, Bischof, and Sauer (2014) suggest that solution-focused language, even when filling out a registration form, can lead to more optimistic, goal-oriented responses.

Outcomes of Solution-Focused Coaching

The solution-focused approach has gained significant recognition in recent literature and is widely applied across various domains, including organisational development and business coaching (e.g., O’Connell & Palmer, 2018). Some practitioners use it in its pure form (e.g., Berg & Szabo, 2005; Szabo et al., 2010), while others combine it with different approaches. This has led to the development of the solution-focused cognitive behavioural approach (e.g., Grant, 2017; Grant, Green & Rynsaardt, 2010). Numerous empirical studies support the effectiveness of solution-focused coaching in both personal (e.g., Grant, 2003; Green, Grant & Rynsaardt, 2007) and organisational (e.g., Jones et al., 2015; Visser & Butter, 2008) contexts, particularly when compared to problem-focused approaches (e.g., Grant, 2012; Theeboom et al., 2016). However, the literature has no consensus regarding the most appropriate outcome criteria for evaluating coaching effectiveness (e.g., Jones et al., 2015).

Jones et al. (2015) categorised expected outcomes into three groups: cognitive, skill-based, and affective. They also added a fourth category called “results”. Cognitive outcomes involve acquiring declarative and procedural knowledge and cognitive strategies (e.g., problem-solving). Skill-based outcomes refer to the development of competencies often assessed in traditional performance evaluations, such as communication skills, delegation, and teamwork. Affective outcomes include improvements in self-efficacy and well-being, reductions in stress, and enhancements in positive attitudes and motivation. The "results" category covers organisational-level changes and performance, such as increased revenues, ROI (Return on Investment), or reduced turnover.

The first and perhaps most critical outcomes in coaching are goal attainment and performance enhancement. Solution-focused coaching consistently improves these outcomes, which are central to personal and professional success (Spence & Grant, 2007; Theeboom et al., 2014). These improvements align with the skill-based outcomes category defined by Jones et al. (2015), which includes developing and enhancing competencies such as communication, delegation, and teamwork, often assessed in traditional performance evaluations.

The second key outcome is the enhancement of self-efficacy. Solution-focused coaching has significantly boosted self-efficacy (Grant, 2012; Grant & O'Connor, 2010; Wehr, 2010), empowering coachees to achieve their goals and maintain high-performance levels. This outcome fits within the affective category outlined by Jones et al. (2015), where affective outcomes include increased self-efficacy, confidence, and motivation.

The third significant outcome is the improvement of well-being and positive affect, which falls under the affective outcomes category described by Jones et al. (2015). Solution-focused coaching has a pronounced impact on enhancing well-being (Green, Oades & Grant, 2006; Spence & Grant, 2007), positive affect (Green et al., 2007; Grant, 2012), and reducing negative affect, stress, and anxiety (Berg & Karlsen, 2013; Grant & O'Connor, 2010; Gyllensten & Palmer, 2005). These benefits often emerge even when the intervention's primary objective is not directly related to emotional well-being (e.g., Grant, 2003).

Additionally, solution-focused coaching enhances creative problem-solving abilities and solution-focused thinking, contributing to more adaptive and effective behaviours in various situations (Berg & Szabo, 2005; Theeboom et al., 2016). These outcomes, categorised by Jones et al. (2015) as cognitive and skill-based, underscore the approach's effectiveness in fostering individual development and organisational success. Finally, the broader implications of solution-focused coaching include significant improvements in job satisfaction, which is an affective outcome, and overall organisational effectiveness (Theeboom et al., 2014; Grant, 2003).

These findings highlight the approach's versatility and efficacy across different contexts and underscore the value of solution-focused coaching in enhancing individual experiences and contributing to organisational success. This makes it a valuable tool for personal development, fostering a more engaged and productive workforce.

Research Objective

The primary goal of this longitudinal study is to empirically assess the effectiveness of individual solution-focused brief coaching psychological interventions within a business context.

Based on literature (e.g., Grant, 2003; Wehr, 2010; Grant, 2012; Berg & Karlsen, 2013; Theeboom et al., 2016), the main research question is whether participation in a solution-focused brief coaching (SFBC) process effectively enhances performance and the likelihood of successful goal attainment, while also increasing self-efficacy, well-being, positive affect, and reducing negative affect. Building on these fundamental assumptions, the study is structured around five main hypotheses:

Hypothesis 1: The Experimental Group (EG) will show higher performance during follow-up (FUP) compared to the pre-intervention period (PRE) and the Waitlist Control Group (WCG).

Hypothesis 2: The Experimental Group (EG) will exhibit higher levels of self-efficacy immediately after the intervention (POST) and during follow-up (FUP) compared to the pre-intervention period (PRE) and the Waitlist Control Group (WCG). Additionally, within the Experimental Group (EG), a more significant increase in self-efficacy will be observed after the sessions (POST) compared to the pre-intervention period (PRE), with a slight increase during the follow-up (FUP).

Hypothesis 3: The Experimental Group (EG) will demonstrate higher levels of well-being immediately after the intervention (POST) and during follow-up (FUP) compared to the pre-intervention period (PRE) and the Waitlist Control Group (WCG). Additionally, within the Experimental Group (EG), a more significant increase in well-being will be observed after the sessions (POST) compared to the pre-intervention period (PRE), with a slight increase during the follow-up (FUP).

Hypothesis 4: Participants in the Experimental Group (EG) will demonstrate higher levels of overall positive affect immediately after the intervention (POST) and during follow-up (FUP) compared to the pre-intervention period (PRE) and the Waitlist Control Group (WCG). Additionally, within the Experimental Group (EG), a more significant increase in positive affect will be observed after the sessions (POST) compared to the pre-intervention period (PRE), with a slight increase noted during the follow-up (FUP).

Hypothesis 5: Participants in the Experimental Group (EG) will show lower levels of overall negative affect immediately after the intervention (POST) and during follow-up (FUP) compared to the pre-intervention period (PRE) and the Waitlist Control Group (WCG). Additionally, within the Experimental Group (EG), a more significant reduction in negative affect will be observed after the sessions (POST) compared to the pre-intervention period (PRE), with a slight decrease during the follow-up (FUP).

Methodology

The present study was conducted at a subsidiary of a multinational company involved in developing and researching information technology and communication systems. The subsidiary employs approximately 2000 workers. Grant et al. (2010) highlight increasing evidence that supports the effectiveness of coaching in organisational contexts, not just for executives but also for other roles, such as business coaching. However, these areas still need to be researched, so we focused on impact studies related to business coaching.

We began recruiting white-collar employees after obtaining ethical approval for the research (reference number: 2019/70). However, the start date was significantly delayed due to the unexpected COVID-19 pandemic, which reshaped the economy, large corporations, and individual lives. The main phase of the research finally commenced in the autumn of 2021 and concluded in January 2023.

During the research period, the organisation adopted a hybrid work model, with participants working two to three days per week in the office and the remaining days remotely. Consultations and coaching sessions occurred in company-provided offices, which were always appropriate for confidential coaching conversations.

The coaching processes were led by a professionally qualified individual with an MA in psychology and specialised training in solution-focused coaching. This coach also had over a decade of professional experience in a multinational environment. Throughout the research, they maintained regular contact with a qualified supervisor who was highly skilled in systemic coaching and coaching psychology.

According to Grover és Furnham (2016, p. 36.), “best research practices for practitioners” include the following: (1) pre-, post-, and longitudinal assessment; (2) the use of relevant individual, organisational, and distal outcomes; (3) the application of objective or multisource ratings of outcome variables; (4) the use of a control group, potentially utilising waitlisted participants; and (5) an adequate sample size. They note that for a t-test analysis between two independent groups (the coached group and the control group), adequate power levels require sample sizes of 52 participants for a large effect (0.8), 128 participants for a medium effect (0.5), and 352 participants for a small effect (0.2), as calculated using G*Power 3.1. We adopted these practices as the foundation for our research design. To measure the effectiveness of the coaching psychology interventions and mitigate potential biases, we employed multiple assessment methods, including self-report questionnaires and 360-degree performance evaluations. Additionally, to achieve more differentiated results and enhance internal validity, we designed a randomised controlled trial (RCT) with participants randomly assigned to either an experimental or waitlist control group.

Participants

The study sample was recruited using non-probability convenience sampling from employees of the organisation where the coaching psychology interventions took place. The recruitment call was sent via the company’s online communication platform as an email. Potential participants were informed that the process could differ for each individual and that coaching sessions might not occur in the same quarter. They were also advised that specific details and timing would be communicated after recruitment. Participation in the study offered no financial compensation; instead, the incentive was the opportunity to engage in coaching sessions free of charge.

According to the G*Power analysis, the appropriate sample size for our regression analyses, based on a significance level of 0.05 and a statistical power of 0.95, is as follows: 18 participants are required for a large effect size (0.35); 44 participants are needed for a medium effect size (0.15); and 260 participants are required for a small effect size (0.02).

101 employees expressed interest, but due to unexpected changes affecting the organisation and the world (e.g., COVID-19), 87 employees ultimately participated. To our knowledge, the 14 employees who dropped out were no longer with the organisation in 2021, when the research officially began. Of the 87 individuals who participated in the initial assessment, 84 (96.55/) completed the programme. During the study, three individuals experienced severe crises (e.g., illness or the loss of a close relative) that prevented the continuation of coaching psychology interventions following the guidelines. We focused on creating a supportive environment to help alleviate stress and anxiety and assisted these individuals in finding competent professionals for further support.

Data from 84 participants will be analysed for this study. Given that most coaching outcome studies consider medium to large effect sizes (see, e.g., Jones et al., 2015), our sample size is deemed acceptable overall. The average age of the participants was 31 (SD=3.04), with the youngest being 25 and the oldest 40 years old. The gender distribution was reasonably balanced, with 47 women and 37 men. The final number of participants was as follows: experimental group – 43 participants (M=30.28, SD=2.98; 25 women, 18 men), waitlist control group – 41 participants (M=31.27, SD=3.06; 22 women, 19 men). Most participants held a university BA degree, making up over half of the sample (50 individuals). 32 participants had an MA degree, and 2 had a high school diploma.

All participants were assured of their voluntary participation, anonymity, and the confidentiality of their data. They were informed that the process would take approximately 6-8 months. An online schedule was provided, and participants were asked to use their chosen code name to select a consultation time. Those in the experimental group were also asked to choose three additional slots, with two-week intervals, for coaching sessions. This marked the beginning of Phase 1 of the study.

Procedure

Table 1 illustrates the experimental procedure. We collected demographic data and conducted 360-degree performance evaluations. In addition to self-assessments, each participant was evaluated by two colleagues: their direct manager and a peer of the same hierarchical level. The evaluators, excluding senior management, were selected from among the participants.

Following this, all participants had a consultation with the coach. During this session, a brief introductory conversation took place. For the experimental group, this was followed by goal setting. Afterwards, all participants completed the State Assessment Questionnaire, including the 20-item version of the Positive and Negative Affect Schedule (PANAS), the General Self-Efficacy Scale, the 5-item WHO Well-Being Index (WHO-5), and the Goal Attainment Scale we developed. One or two days later, the first coaching session for the experimental group began.

Phase 1 consisted of three 60-minute coaching sessions, each preceded by a test, while the waitlist control group had a waiting period (with tests). The sessions were spaced approximately two weeks apart.

In Phase 2, the longitudinal effectiveness of the coaching was assessed monthly over the 20 weeks following the last session using the same State Assessment Questionnaire. This phase also included final 360-degree performance evaluations for all participants, following the optimal 6–12-month test-retest period recommended for multi-rater feedback (see Bracken, Timmreck & Church, 2001). The Waitlist Control Group could only participate in the three-session coaching process after this approximately six-month period.

In this study, the 360-degree performance evaluations were conducted twice: once at the beginning and once at the end of the research process. The State Assessment Questionnaire was administered seven times throughout the study (see Table 1).

Table 1

Procedure Steps

	Phase 1
	0^th and 1^st time – PRE measurement (Week 0 – Starting point)		2^nd time (2-3 weeks later)	3^rd time (4-5 weeks later)
EG	- Demographic questionnaire - 360-degree performance assessment - State Assessment Questionnaire	- Preliminary Conversation - Contract setting - 1st coaching session	- State Assessment Questionnaire - 2nd coaching session	- State Assessment Questionnaire - 3rd coaching session - Action plan
WCG		- Preliminary Conversation	- State Assessment Questionnaire	- State Assessment Questionnaire
	Phase 2
	4^th time – POST measurement (approx. in 10 weeks)	5^th time (approx. in 15 weeks)	6^th time (approx. in 20 weeks)	7^th time – FUP measurement (approx. in 25 weeks)
EG	- State Assessment Questionnaire	- State Assessment Questionnaire	- State Assessment Questionnaire	- State Assessment Questionnaire - 360-degree performance assessment
WCG	- State Assessment Questionnaire	- State Assessment Questionnaire	- State Assessment Questionnaire

Contracting and Goal Setting

The entire process was designed based on the framework of SFBC (Iveson et al., 2017). Following the brief social conversation, contracting and goal setting took place within the framework of a semi-structured interview for the experimental group. However, the competent professional was free to shape the conversation as needed.

During the interview, participants received information about the competencies measured in the performance evaluation and their results, though this was not necessarily part of the discussion. If the professional believed enough information had surfaced to outline the targeted future, they did not push for further details.

The conversation concluded with identifying the competency area to be developed, illustrating the goal with specific behavioural examples, and then scaling the set goal. The details of the interview guide: “What is your boldest hope regarding the outcome of our work together? … According to the 360-degree performance evaluation results, the [NAME OF COMPETENCY] area seems challenging for you. It sounds like this aligns with what you mentioned...? … On a scale of 0 to 10, where 10 represents the ideal state, and 0 represents the opposite, where do you currently stand? … How high do you want to get on the scale to see the next achievable steps clearly? … How will you notice that you have reached this point?”

The Solution-Focused Brief Coaching Psychological Intervention

The planning of coaching sessions was based on the SFBC framework (see above and, e.g., Iveson et al., 2017). However, the coach was given the freedom to shape the conversation.

Key components of the first session included (1) the miracle question, (2) reframing, (3) scaling and competence seeking, and (4) feedback. The main objectives of the second and third sessions were to track progress and support the coachee's advancement. The two sessions may have included the following elements and exercises: progress, resources, scaling tasks, motivation scale, and observation experiment as homework. The third session always concluded with defining the concrete action plan and providing feedback. Details of the intervention script: „When was the last time that even a small part of this went better or was already achieved? And when else? ... Who noticed it? And who else? What was the impact?... Let us assume you move up one step on the scale towards your goal... What will you notice? ... What is the first small step you could take, even right after this session, that will set you on your way? ... What has improved over the past two weeks? And what else has improved? ... I can imagine that achieving these things wasn’t easy. How did you do it? ... What do you think about this? Are things clearer now? ... How did you accomplish all this? What else helped? And what else? ... How can you maintain what is already working well?”

Measures

360-degree feedback. The methodology of 360-degree feedback enables the exploration of professional competencies, including strengths and areas for improvement (see, e.g., Shipper & Dillard, 2000), thereby facilitating the development of more effective supportive and developmental interventions.

According to McDowall and Kurz (2008), effective 360-degree feedback must meet at least the following conditions: (1) it should ensure comprehensive and valid measurement of workplace behaviours; (2) the feedback received from various sources should encourage individuals to make changes; and (3) each feedback should provide valuable information on its own.

Taking these considerations into account, we formulated behavioural statements for assessing competencies with the assistance of a data-driven organisational development company. In line with the IT organisation's competency-based framework that allowed room for the research, the questionnaire consists of seven main dimensions (decision-making, planning, adaptability, teamwork, problem-solving, communication, conflict management) with seven subscales. The internal consistency of the questionnaire we developed for performance evaluation is considered good (Cronbach's alpha = 0.72-0.94).

The questionnaire consists of 28 statements, with four items per scale. Participants must rate their agreement on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree) regarding each item. The maximum achievable score for overall performance is 196, and for each subscale, it is 28. Higher scores indicate higher-quality performance. Below are some example items from the questionnaire: “I recognise problems and opportunities and decide on the necessary next steps.” “I view change as an opportunity for growth and development.” “I ensure that the information I convey is understood by seeking feedback from my audience.”

The State Assessment Questionnaire Package included the 20-item version of the Positive and Negative Affect Schedule (PANAS), the General Self-Efficacy Scale, the 5-item WHO Well-Being Index (WHO-5), and the Goal Attainment Scale we developed.

Positive and Negative Affect Schedule (PANAS; Watson, Clark & Tellegen, 1988). The Positive and Negative Affect Schedule measures positive and negative emotional states. The scale consists of ten positive (e.g., interested, proud, determined, active) and negative, aversive (e.g., hostile, nervous, afraid, guilty) emotional states or moods.

Participants in the study rate each expression on a 5-point Likert scale, indicating the extent to which the statement describes their current emotional climate (1 = not at all, 5 = extremely). The list of expressions is then presented again, and participants provide answers about how they generally feel. The total scores for each sub-scale were computed by averaging the scores, resulting in values ranging from 1 to 5.

The internal consistency of the sub-scales is excellent, with Cronbach's alpha coefficients ranging from 0.84 to 0.90 (Watson et al., 1988). In this study, the internal reliability of the sub-scales was adequate in all cases (Cronbach's alpha = 0.90-0.95).

General Self-Efficacy Scale (Schwarzer & Jerusalem, 1995). The General Self-Efficacy Scale measures perceived self-efficacy, which refers to the general optimistic attitude and belief that a person can effectively cope with life's challenges (Bandura, 1997).

The test consists of 10 statements, and participants are asked to rate on a 4-point Likert scale the extent to which they believe each statement applies to them (1 = not at all true, 4 = precisely true). The maximum score for self-efficacy is 40. A higher score indicates a higher level of perceived self-efficacy. Example items from the questionnaire: “I can always manage to solve difficult problems if I try hard enough.” “When I am confronted with a problem, I can usually find several solutions.”

This study considers the self-efficacy scale's internal reliability good (Cronbach's alpha = 0.95 or above).

WHO-5 (Staehr-Johansen, 1998). In its 5-item version, the WHO Well-Being Scale aims to provide information about individuals' general well-being based on the preceding two weeks.

The scale contains statements that can be rated on a 4-point Likert scale (0 = at no time, 3 = all the time). The maximum score achievable is 15, with higher scores indicating a more positive and favourable psychological state. Example items from the questionnaire: “Over the past two weeks...” (1) I have felt cheerful and in good spirits. (2) “I have felt calm and relaxed.”

In this study, the internal reliability of the well-being scale, which measures the level of well-being, is considered adequate (Cronbach's alpha = 0.86-0.91).

Solution-focused Goal Achievement Scaling. The Goal Attainment Scale consists of a brief introductory text, a scale illustration, and two simple questions: “You previously determined where you stand on a scale from 0 to 10 regarding attaining your goal. 10 represents the ideal state, and 0 represents the opposite. What was the starting point?… Where do you currently stand on the scale?”

Results

The data were analysed using the IBM SPSS statistical program (IBM® SPSS® Statistics, 2022). As a first step in the data analysis and result evaluation, reliability and distribution tests were conducted on the questionnaires used in the study. Table 2 shows the main variables' means, standard deviations, and standard errors for the experimental and waitlist control groups. The State Assessment Questionnaire was administered seven times throughout the study to better understand the participant's emotional and cognitive states over time. Our hypothesis testing (H2-H5) primarily relied on data from three key points: the initial measurement (PRE), immediately after the coaching sessions (POST), and during the follow-up period (FUP).

Table 2

Means, medians and standard deviations for all variables (Experimental Group and Waitlist Control Group)

		Experimental Group			Waitlist Control Group
	Time	M	SD	STD ERROR	M	SD	STD ERROR
General positive affectivity	PRE	3.47	0.57	0.09	3.71	0.64	0.10
	POST	3.86	0.62	0.09	3.57	0.93	0.14
	FUP	3.84	0.72	0.11	3.64	0.74	0.12
General negative affectivity	PRE	1.93	0.54	0.08	1.59	0.47	0.07
	POST	1.67	0.50	0.07	1.85	0.84	0.12
	FUP	1.60	0.58	0.09	1.76	0.59	0.09
General Self-Efficacy	PRE	22.79	5.93	0.90	28.51	7.55	1.18
	POST	26.9	6.71	0.90	25.56	8.78	1.35
	FUP	27.23	7.91	1.21	27.83	7.82	1.22
Subjective well-being	PRE	6.90	2.75	0.42	8,66	3.31	0.52
	POST	8.04	2.98	0.47	5.90	4.09	0.60
	FUP	8.90	3.19	0.49	7.85	3.31	0.52
Performance	PRE	19.09	2.09	0.32	19.79	2.68	0.42
Performance	FUP	20.91	2.67	0.41	19.83	3.17	0.50

We performed a Pearson's correlation analysis between the different scales to check the discriminant validity. Negative affectivity has a significant medium-strength negative correlation with all the other scales (-0.353 – -0.723). With a few exceptions, the other scales show a significant positive correlation of medium to high strength with each other (0.273 – 0.893). All in all, these variables are highly connected. Therefore, it is unsurprising that the way they work is also similar in many cases.

Given our current sample of 84 participants, the Central Limit Theorem (Kwak & Kim, 2017) justifies using parametric statistical methods for detecting medium and large effect sizes, as the sample size is generally sufficient to assume that the sampling distribution of the mean approaches normality. However, the statistical power might not be adequate for smaller effect sizes, necessitating a cautious interpretation of the results.

A 2x3 and 2x2 two-way mixed-design ANOVA was employed to test the hypotheses. This analysis included one between-subjects factor (Group: Experimental Group [EG] and Waitlist Control Group [WCG]) and one within-subjects factor (Time: Pre-Intervention [PRE], Post-Intervention [POST], and Follow-Up [FUP]). While PRE refers to the first pre-intervention test for both EG and WCG, POST refers to the immediate post-intervention test for EG and the 4th pre-intervention test for WCG. FUP refers to EG's last post-intervention test and WCG's last pre-intervention test, just before this group started the intervention. Additionally, t-tests were conducted to compare PRE-POST times, PRE-FUP times, and POST-FUP times within and between the groups (EG and WCG).

Performance

H1: The Experimental Group (EG) will show higher performance during follow-up (FUP) compared to the pre-intervention period (PRE) and the Waitlist Control Group (WCG).

This study conducted the 360-degree performance evaluations twice: once at the research process's beginning (PRE) and once at the end (FUP). This allowed for a comprehensive assessment of changes in performance as perceived by the participants themselves, their managers, and their peers. A two-way mixed ANOVA revealed a significant effect of the time of measurement on overall performance (F(1;82) = 52.404; p < 0.001), while participation in the coaching process did not show a significant effect (F(1;82) = 0.114; p = 0.737). However, a significant interaction between the two variables was observed (F(1;82) = 48.176; p < 0.001). As shown in Figure 1, the EG's performance increased by the end of the study, a finding further supported by the results detailed in Table 3. The t-test results indicate a significant improvement within the EG (PRE-FUP), whereas the WCG showed no change from the initial level. This largely confirmed our first hypothesis.

Figure 1

Evolution of performance as a function of participation in the coaching process

Table 3

Differences in the performance variable between groups and within-group PRE and FUP measurements (t-test results)

	PRE measurement		FUP measurement
	sign. (p)	std. error	sign. (p)	std. error
Experimental Group vs. Waitlist Control Group	0.184	0,523	0.093	0.639
	PRE-FUP measurements
	sign. (p)		std. error
Experimental Group	<0,001		0,182
Waitlist Control Group	0,834		0,182

Similar results were observed across the performance subscales, which include competencies like decision-making, planning, adaptability, teamwork, problem-solving, communication, and conflict management. In each instance, the time of the measurement had a significant effect (Fs(1;82) ≥ 18.280; ps < 0.001), while participation in the coaching process itself did not yield significant differences (Fs(1;82) ≤ 1.282; ps ≥ 0.261). However, significant interactions between the variables were found across all subscales (Fs(1;82) ≥ 14.554; ps < 0.001).

The members of the WCG started at a higher performance level than those in the EG, although the difference was not statistically significant. To gain a more nuanced understanding of the initial differences between the two groups, we decided to compare the feedback received from the 360-degree performance evaluation, divided into three parts: self-assessment (SA), manager evaluation (ME), and peer evaluation (PE). Based on this comparison, there was no significant difference between the EG and WCG in the initial ME or PE. However, members of the WCG rated themselves (SA) significantly higher than EG members (see Table 4).

Table 4

Differences in the performance variable between groups (Experimental Group, Waitlist Control Group) based on different evaluators (managers, ME; colleagues, PE; self-assessment, SE) (results of t-tests)

		Experimental Group vs. Waitlist Control Group
SA PRE	sign. (p)	0,002
SA PRE	std. error	0.507
SA UK	sign. (p)	0.504
SA UK	std. error	0.626
ME PRE	sign. (p)	0.915
ME PRE	std. error	0.598
ME UK	sign. (p)	0.038
ME UK	std. error	0.648
PE PRE	sign. (p)	0.458
PE PRE	std. error	0.674
PE UK	sign. (p)	0.053
PE UK	std. error	0.749

General Self-Efficacy

H2: The Experimental Group (EG) will exhibit higher levels of self-efficacy immediately after the intervention (POST) and during follow-up (FUP) compared to the pre-intervention period (PRE) and the Waitlist Control Group (WCG). Additionally, within the Experimental Group (EG), a more significant increase in self-efficacy will be observed after the sessions (POST) compared to the pre-intervention period (PRE), with a slight increase during the follow-up (FUP).

Based on two-way mixed-design ANOVA, there was a significant effect of the time of measurement (F(1;82) = 10.143; p = 0.002), no significant effect of participation in the coaching process (F(1;82) = 1.234; p = 0.270), and a significant interaction between the two variables (F(1;82) = 18.853; p < 0.001). As shown in Figure 2 and detailed in Table 5, the EG began with a significantly lower baseline of self-efficacy (PRE) compared to the WCG. However, the EG's self-efficacy significantly increased by the end of the coaching process (PRE-POST; PRE-FUP) and stabilised during the follow-up (POST-FUP). In contrast, the WCG showed an initial significant decrease (PRE-POST), followed by a significant increase (POST-FUP). Yet, significant differences were not found between the groups by the end of the study (FUP). This partially supports our second hypothesis.

Figure 2

Evolution of general self-efficacy as a function of participation in the coaching process

Table 5

Differences in the self-efficacy variable between groups and within-group PRE, POST and FUP measurements (t-test results)

	PRE measurement		POST measurement		FUP measurement
	sign. (p)	std. error	sign. (p)	std. error	sign. (p)	std. error
Experimental Group vs. Waitlist Control Group	<0.001	1.479	0.434	1.712	0.729	1.718
	PRE-POST measurements		PRE-FUP measurements		POST-FUP measurements
	sign. (p)	std. error	sign. (p)	std. error	sign. (p)	std. error
Experimental Group	<0.001	0.667	<0.001	0.808	0.570	0.568
Waitlist Control Group	0.009	1.080	0.433	0.861	0.014	0.883

Subjective well-being

H3: The Experimental Group (EG) will demonstrate higher levels of well-being immediately after the intervention (POST) and during follow-up (FUP) compared to the pre-intervention period (PRE) and the Waitlist Control Group (WCG). Additionally, within the Experimental Group (EG), a more significant increase in well-being will be observed after the sessions (POST) compared to the pre-intervention period (PRE), with a slight increase during the follow-up (FUP).

Based on the two-way mixed-design ANOVA, there was no significant main effect of the measurement time (F(1;82) = 3.656; p = 0.059) or participation in the coaching process (F(1;82) = 0.668; p = 0.416). However, a significant interaction between the two variables was found (F(1;82) = 20.135; p < 0.001). As illustrated in Figure 3 and detailed in Table 6, the EG started from a significantly lower baseline in subjective well-being compared to the WCG (PRE measurement). However, the EG consistently showed significant or marginally significant increases throughout the coaching process, with improvements during the follow-up period (PRE-POST, PRE-FUP, POST-FUP). In contrast, the WCG experienced an initial decline (PRE-POST), followed by a recovery (POST-FUP), ultimately resulting in a slight overall decrease in well-being (PRE-FUP). This broadly supports our third hypothesis.

Figure 3

Evolution of subjective well-being as a function of participation in the coaching process

Table 6

Differences in the subjective well-being variable between groups and within-group PRE, POST and FUP measurements (t-test results)

	PRE measurement		POST measurement		FUP measurement
	sign. (p)	std. error	sign. (p)	std. error	sign. (p)	std. error
Experimental Group vs. Waitlist Control Group	0.010	0.663	0.008	0.784	0.142	0.710
	PRE-POST measurements		PRE-FUP measurements		POST-FUP measurements
	sign. (p)	std. error	sign. (p)	std. error	sign. (p)	std. error
Experimental Group	0.008	0.409	<0.001	0.394	0.052	0.429
Waitlist Control Group	<0.001	0.630	0.108	0.489	0.004	0.634

General positive affectivity

H4: Participants in the Experimental Group (EG) will demonstrate higher levels of overall positive affect immediately after the intervention (POST) and during follow-up (FUP) compared to the pre-intervention period (PRE) and the Waitlist Control Group (WCG). Additionally, within the Experimental Group (EG), a more significant increase in positive affect will be observed after the sessions (POST) compared to the pre-intervention period (PRE), with a slight increase noted during the follow-up (FUP).

For the general positive affectivity subscale, a significant effect was observed for the time of measurement (F(1, 82) = 7.688; p = 0.007), while participation in the coaching process showed no significant effect (F(1, 82) = 0.336; p = 0.564). However, a significant interaction between the two variables was found (F(1, 82) = 18.427; p < 0.001). Based on Figure 4 and Table 7, the EG started from a lower baseline than the WCG. However, by the end of the process, the EG exhibited higher general positive affectivity, although the difference was not statistically significant. The WCG showed a declining or stagnating trend in positive affectivity. Significant or marginally significant differences were observed only in the EG's PRE-POST and PRE-FUP measurements, indicating a significant increase in positive affectivity due to the coaching process. This largely supports our fourth hypothesis.

Figure 4

Evolution of general positive affectivity as a function of participation in the coaching process

Table 7

Differences in the general positive affectivity variable between groups and within-group PRE, POST and FUP measurements (t-test results)

	PRE measurement		POST measurement		FUP measurement
	sign. (p)	std. error	sign. (p)	std. error	sign. (p)	std. error
Experimental Group vs. Waitlist Control Group	0.003	0.111	0.225	0.151	0.208	0.129
	PRE-POST measurements		PRE-FUP measurements		POST-FUP measurements
	sign. (p)	std. error	sign. (p)	std. error	sign. (p)	std. error
Experimental Group	<0.001	0.457	<0.001	0.060	0.133	0.044
Waitlist Control Group	0.020	0.108	0.051	0.086	0.247	0.075

General negative affectivity

H5: Participants in the Experimental Group (EG) will show lower levels of overall negative affect immediately after the intervention (POST) and during follow-up (FUP) compared to the pre-intervention period (PRE) and the Waitlist Control Group (WCG). Additionally, within the Experimental Group (EG), a more significant reduction in negative affect will be observed after the sessions (POST) compared to the pre-intervention period (PRE), with a slight decrease during the follow-up (FUP).

For the general negative affectivity subscale, the time of measurement had no main effect (F(1;82) = 2.138; p = 0.148), nor did participation in the coaching process (F(1;82) = 0.001; p = 0.972). However, there was a significant interaction between the two variables (F(1;82) = 22.891; p < 0.001). Based on Figure 5 and Table 8, the EG starts from a significantly higher baseline than the WCG (PRE); however, the difference between the two groups becomes negligible as the study progresses. In the EG, a significant decrease is observed (PRE-POST and PRE-FUP). At the same time, the WCG shows a significant increase (PRE-POST) and a marginally significant increase (PRE-FUP). This partially confirms our fifth hypothesis.

Figure 5

Evolution of general negative affectivity as a function of participation in the coaching process

Table 8

Differences in the general negative affectivity variable between groups and within-group PRE, POST and FUP measurements (t-test results)

	PRE measurement		POST measurement		FUP measurement
	sign. (p)	std. error	sign. (p)	std. error	sign. (p)	std. error
Experimental Group vs. Waitlist Control Group	0.003	0.111	0.225	0.151	0.208	0.129
	PRE-POST measurements		PRE-FUP measurements		POST-FUP measurements
	sign. (p)	std. error	sign. (p)	std. error	sign. (p)	std. error
Experimental Group	<0.001	0.457	<0.001	0.060	0.133	0.044
Waitlist Control Group	0.020	0.108	0.051	0.086	0.247	0.075

Discussion

The primary goal of this longitudinal study was to empirically assess the effectiveness of solution-focused brief coaching (SFBC) psychological interventions in an organisational setting, particularly among white-collar workers. The study sought to determine whether participation in an SFBC process effectively reduces negative emotions and enhances positive affectivity, self-efficacy, well-being, performance, and goal attainment. The research was structured around five hypotheses.

H1: The Experimental Group (EG) will show higher performance during follow-up (FUP) compared to the pre-intervention period (PRE) and the Waitlist Control Group (WCG).

The results from this study largely support the first hypothesis, showing that the EG's performance increased significantly by the end of the study (PRE-FUP). In contrast, the WCG exhibited no significant change from the initial level. This improvement in the EG was consistent across various performance subscales, including competencies such as decision-making, planning, adaptability, teamwork, problem-solving, communication, and conflict management. These findings align with previous research demonstrating the effectiveness of solution-focused coaching in enhancing performance outcomes (e.g., Grant et al., 2009).

The observed performance enhancement in the EG can be attributed to several factors inherent in the coaching process. Firstly, solution-focused coaching helps individuals clarify their goals and align their actions with these objectives, leading to more focused and efficient efforts (Grant, 2003; Spence & Grant, 2007). Additionally, the coaching process fosters a supportive environment that encourages self-reflection, continuous learning, and adaptive problem-solving strategies, all contributing to improved performance (Cavanagh & Grant, 2014; Theeboom et al., 2016).

The results from this study partially confirm the second hypothesis. The EG demonstrated a significant increase in self-efficacy by the end of the coaching process (PRE-POST; PRE-FUP), stabilising during the follow-up period (POST-FUP). Although the EG started with a significantly lower baseline of self-efficacy (PRE) compared to the WCG, it showed consistent improvement. In contrast, the WCG experienced an initial significant decrease in self-efficacy (PRE-POST), followed by a significant increase during the follow-up (POST-FUP). However, by the end of the study (FUP), no significant differences were observed between the two groups.

These results are consistent with existing literature, recognising self-efficacy as a crucial psychological variable influenced by coaching interventions (Bozer & Jones, 2018; Grant, 2012; Grant & O'Connor, 2010). Bandura (1997) defined self-efficacy as “beliefs in one’s capabilities to organise and execute the courses of action required to produce given attainments” (p. 3). Solution-focused coaching, which operates on the premise that individuals can achieve their goals, plays a significant role in challenging negative beliefs, shifting perspectives, and helping individuals recognise and leverage their resources (Berg & Szabo, 2005). Additionally, the coaching process often involves setting and achieving small, manageable goals, which gradually builds confidence and reinforces the belief that larger goals are attainable (Iveson et al., 2017). This process enhances self-efficacy, leading to improvements in overall performance (Bozer & Jones, 2018).

The results from this study largely support the third hypothesis. The EG showed a significant increase in subjective well-being immediately after the coaching sessions (POST), which continued to improve, albeit marginally, by the end of the study (FUP), compared to the WCG. Despite starting with a significantly higher baseline of well-being, the WCG experienced fluctuations during the study, ultimately leading to a significant decline in well-being by the end (PRE-POST). The EG demonstrated significant improvements between the PRE-POST and PRE-FUP measurements, while the POST-FUP measurements did not show significant differences.

These findings align with existing literature that highlights the positive impact of solution-focused coaching on well-being (e.g., Grant, Curtayne & Burton, 2009; Green et al., 2006; Spence & Grant, 2007). The increase in well-being observed in the EG can be attributed to several factors inherent to solution-focused coaching. This method encourages setting approach-oriented goals, which are linked to positive progress and enhanced subjective well-being (Neenan & Palmer, 2012). By focusing on what can be achieved rather than what should be avoided, participants are more likely to experience positive emotional outcomes (Wehr, 2010). Additionally, recognising and leveraging personal strengths during coaching contributes to a more positive self-view, fostering a sense of achievement and well-being sustained over time (Berg & Szabo, 2005).

The results from this study partially confirm the fourth hypothesis. The EG began with a slightly lower baseline of overall positive affect compared to the WCG; however, it showed continuous improvement throughout the coaching process, eventually reaching a higher level by the end of the study, although this difference was not statistically significant. A significant positive change was observed within the EG between the PRE-POST and PRE-FUP measurements, while no significant differences were found between the POST-FUP measurements. In contrast, the WCG decreased from the initial state, although this change was not significant.

These findings align with existing literature on the positive impact of solution-focused coaching on positive affect (Grant, 2012; Green et al., 2007). The increase in positive affect observed in the EG can be attributed to several key aspects of solution-focused coaching. Taking action towards one’s goals, even small steps can generate a sense of accomplishment and empowerment (O’Connell & Palmer, 2018). This goal-oriented and strengths-based focus of SFBC boosts self-efficacy and fosters positive feelings as individuals see progress made (Iveson et al., 2017).

The results broadly support the fifth hypothesis. The EG showed significant reductions in negative affect between the PRE-POST and PRE-FUP measurements. By the end of the process (FUP), the EG demonstrated lower negative affect levels than the WCG, although these differences were not statistically significant. In contrast, the WCG experienced a significant increase in negative affect between the PRE-POST measurements, which continued to rise throughout the study. These findings align with previous research that highlights the efficacy of solution-focused coaching in reducing negative emotions, stress, and anxiety (e.g., Grant & O'Connor, 2010; Gyllensten & Palmer, 2005; Berg & Karlsen, 2013). Such reductions in negative affect are often observed even when the primary goal of coaching is not explicitly focused on emotional well-being (Grant, 2003).

The reduction in negative affect observed in the EG can be attributed to several mechanisms inherent to solution-focused coaching. Firstly, this approach encourages individuals to shift their focus from problems to solutions, which naturally diminishes the focus on negative emotions and reduces rumination (Theeboom et al., 2016). By guiding participants to identify and build upon their strengths, solution-focused intervention fosters a more optimistic outlook, which counteracts the negative thought patterns that often underlie anxiety and stress (Lipchik & de Shazer, 2017). Finally, the supportive and non-judgmental environment created by the coach-coachee relationship also plays a critical role in alleviating negative emotions, as individuals feel more understood and less isolated in their experiences (Iveson et al., 2017).

The findings of this study revealed that several hypotheses were not fully supported, particularly those suggesting that the Experimental Group (EG) would achieve significantly higher (or, in the case of negative affect, lower) scores compared to the Waitlist Control Group (WCG) by the end of the coaching process (FUP). One potential reason for this discrepancy is the generally higher baseline (or lower in the case of negative affect) scores in the WCG across all measured variables at PRE. To explore these differences, a detailed comparison of the 360-degree performance evaluation feedback was conducted, focusing on self-assessment (SA), manager evaluations (ME), and peer evaluations (PE). The analysis revealed no significant initial differences between the EG and WCG in ME and PE scores. However, WCG participants rated themselves (SA) significantly higher than those in the EG, suggesting that WCG participants had a more positive self-assessment, though external evaluations did not corroborate this perceived superiority.

It is essential to consider the potential impact of participants' awareness of the timing of their involvement in the coaching process. From the outset, participants knew whether they would start the coaching sessions immediately or later, which likely compromised the randomisation and overall integrity of the study. This foreknowledge may have influenced the self-reported outcomes. Aware that their participation in coaching would be delayed, WCG participants may have inflated their self-assessments to cope with the uncertainty and frustration of waiting.

Another issue to consider is the trend observed in the WCG, where scores frequently showed a decreasing tendency throughout the process (or increasing in the case of negative affect). In contrast, the EG did not exhibit such declines when comparing the PRE, POST, and FUP measurements. However, when examining the trends across the seven measurement points (which we observed for self-efficacy, well-being, and affectivity), we noticed that both groups exhibited a similar pattern of change between POST and FUP. This suggests that an external factor, such as an organisational change, may have influenced the mental state of all participants. According to the coach's observations, the fluctuations in the participants' well-being levels could reflect a mild to moderate crisis within the company, which subtly impacted all measured variables across the seven sessions. Based on these observations, it appears that participation in the coaching process may have made individuals more adaptable to these changes (e.g., Kamali & Naghavi, 2023)—though we cannot substantiate this claim with concrete measurements.

Limitations of the study

While this study provides valuable insights into the effectiveness of psychological coaching interventions, several limitations should be considered that may affect the generalisability and validity of the results. One of the primary limitations is the composition and size of the sample. The data was collected from a single organisation among white-collar workers, leading to a homogeneous sample. This may limit the generalisability of the findings to other sectors, blue-collar workers, or individuals less open to participating in coaching processes. The organisation does, however, represent a typical environment for business coaching (Bene, Móré és Gerhát, 2018), which somewhat mitigates this concern. The sample size was adequate for detecting medium effect sizes. However, it may not have provided sufficient statistical power for smaller effects (e.g., Cohen, 1992; Grant & O’Connor, 2010).

We employed a randomised controlled longitudinal design with a waitlist control group. However, the randomisation might have been compromised since participants were aware from the outset whether they would start the coaching sessions immediately or later. Additionally, most of the measurement tools used were self-reported, which, despite being standard in coaching research, carries the risk of biases such as social desirability or the tendency to respond in a manner that participants believe is expected (e.g., Cook & Campbell, 1979).

It is also important to acknowledge that, while participants were instructed not to engage in other helping processes during the study, numerous uncontrolled life factors and synergistic interactions could have influenced their personal development. Moreover, every individual coaching process is unique and tailored to the coachee’s specific needs and goals, making exact replication of the work impossible (see Lai & Palmer, 2018). Therefore, it cannot be stated with absolute certainty that goal attainment and growth occurred solely due to the coaching process. Additionally, it should be noted that statistical correlation does not imply causation. Finally, this study did not include measuring organisational-level outcomes, such as ROI or employee turnover, which could have provided additional insights into the broader impact of the coaching interventions. However, based on the results, coaching likely contributes positively to individuals' and organisations’ lives.

Recommendations for future research

Considering the identified limitations, several avenues for future research can enhance the validity and generalisability of coaching outcome studies.

We recommend following the "best research practices" outlined by Grover and Furnham (2016) to improve research methodology. This approach includes pre-, post-, and long-term assessments, using objective evaluations from multiple sources and including relevant organisational outcomes such as ROI or employee turnover alongside individual variables. Extending the follow-up period to one or two years could provide more meaningful insights into the long-term effects of coaching, particularly concerning organisational outcomes like employee engagement or turnover. It may also be beneficial to employ quantitative and qualitative data collection methods that rely less on self-reporting, such as behavioural measures, which can offer a more objective view of participants' development.

Expanding the composition and size of the sample is another critical step forward. Future research should include participants from multiple organisations and industries, increasing sample heterogeneity and ensuring the findings are more broadly applicable. To preserve randomisation, it is advisable that participants remain unaware of whether they will begin the coaching process immediately or later during the initial assessments. This could help prevent biases related to the expectancy or perception of group assignments.

Moreover, Passmore and Theeboom (2016) emphasise that the next phase in coaching psychology research should focus on identifying and measuring the active ingredients of coaching interventions, including individual differences and receptivity to coaching.

In addition to these methodological improvements, it is crucial to foster closer collaboration between coaching psychologists and practitioners to gather more data on the factors influencing coaching effectiveness. Involving multiple experts—both coaching psychologists and coaches—could allow for comparative studies of coaching competencies and methodologies, providing further valuable insights into coaching efficacy.

In today’s increasingly online world, systematically varying the coaching environment (e.g., online vs. face-to-face coaching) would enable researchers to investigate how different settings impact coaching effectiveness.

Recommendations for practitioners

This study underscores the effectiveness of Solution-Focused Brief Coaching (SFBC) in enhancing employee performance and well-being in organisational settings. SFBC is a short, results-oriented approach that emphasises identifying and leveraging individual strengths to achieve specific goals (Berg & Szabo, 2005). This makes it particularly well-suited for fast-paced, results-driven environments where time is critical.

Our research, in line with previous literature (e.g., Gyllensten & Palmer, 2005; Wehr, 2010), highlight SFBC’s ability to increase self-efficacy, well-being and positive affectivity while reducing negative emotions. Moreover, the goal-oriented nature of SFBC aligns well with organisational objectives. It encourages employees to set clear, actionable goals and take small steps towards achieving them. This process enhances individual performance and contributes to the organisation’s broader success by fostering a culture of continuous improvement (see, e.g., Bozer & Jones, 2018).

For practitioners, integrating SFBC into employee development programs offers a flexible, cost-effective strategy that can be easily implemented within existing frameworks. As organisations increasingly prioritise evidence-based methods to enhance job satisfaction, engagement and productivity, our research suggests that SFBC provides a practical and effective solution that aligns with individual and organisational goals.

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