Evaluating employee performance in smart work environment with focus on psychological distance and process versus outcome-centric approaches

The rapid evolution of IT technology and the widespread integration of smart devices have sparked profound changes in individual lifestyles, transforming how we work, communicate, and live^1,2. These technological advancements are reshaping traditional workspaces, giving rise to smart work, a novel approach where spatial boundaries dissolve and work is no longer confined to physical office walls³. Powered by advanced information and communication technology, smart work enables individuals to complete their tasks anytime, anywhere, meeting the increasing demand for flexibility in today’s workforce. As the boundaries between professional responsibilities and personal life continue to blur, understanding and optimizing this new work paradigm has become increasingly important⁴.

The rise of smart work extends beyond the flexibility it offers individuals and influences how performance is assessed within organizations^4,5. In traditional work environments, performance evaluations rely on direct observation and interaction, considering not only the final output but also the processes, attitudes, and behaviors involved. However, in smart work environments, where work is physically separated, process-based evaluations face significant challenges due to limited opportunities for direct observation and interaction. With these limitations, outcome-based evaluations may become more prominent, focusing more on the results achieved rather than the methods used to achieve them⁶.

This shift in focus raises important questions about how performance is assessed in smart work environments⁷. For example, in such environments, where physical presence and direct supervision are minimized, the change in an individual’s outcome level may become a more significant factor in evaluations⁸. In contrast, traditional work settings, where processes and efforts can be more directly observed and controlled, may continue to prioritize process-based assessments. Additionally, the level of effort an individual invests may have a different impact on evaluations depending on the work environment⁵. While effort might be directly observable in traditional settings, in remote or smart work environments, it may not have the same weight in the evaluation process.

From a psychological standpoint, the Construal Level Theory (CLT) provides valuable insights into these dynamics, particularly in how performance is evaluated across different work settings. CLT suggests that an individual’s judgments and decisions are shaped by the psychological distance between themselves and the object of evaluation⁹. This distance can manifest in various forms, including temporal, spatial, or social differences. In smart work environments, where face-to-face interactions are less frequent, this psychological distance tends to increase, which may cause evaluators to prioritize overall outcomes or final results, such as overall outcomes or general impressions, over specific, observable behaviors¹⁰. Consequently, this shift can alter the way performance is assessed and may lead to biases that differ from those observed in traditional, physically proximal work environments.

This study will specifically examine how evaluators’ interpretations of performance are influenced by work style (Smart Work vs. Traditional Work), outcome level (High vs. Low), and effort (More vs. Less). A three-way analysis model is used to explore these dynamics among evaluators with experience managing team projects, aiming to discern how their evaluation processes differ between smart and traditional work settings from an information processing perspective. These differences in performance evaluation focus—process-oriented versus outcome-oriented—suggest that the dynamics of smart work and traditional work environments require distinct evaluation approaches. Understanding these shifts and their implications for both employees and organizations is essential for developing more effective performance management systems that are adapted to the changing nature of work. This research aims to explore how these dynamics unfold in different work environments and how they influence the performance evaluation process.

Smart work

Smart work, defined as a flexible work arrangement enabled by advancements in information and communication technologies (ICTs), represents a significant shift in how work is perceived and executed globally. This work modality, characterized by spatial and temporal flexibility, allows individuals to perform tasks from virtually anywhere at any time. It has gained traction as a response to evolving technological landscapes and changing social needs^4,11,12. The historical development of Smart work can be traced back to the increased popularity of mobile technologies and the growing emphasis on employee-centric flexibility, which have made remote work more feasible and effective. According to Raguseo, et al.¹¹, smart work not only involves leveraging ICT tools to support flexibility but also demands a rethinking of office layouts, human resource practices, and organizational policies to optimize employee working conditions. Similarly, Cha and Cha¹² highlight that integrating smart work practices into traditional business frameworks often requires substantial shifts in both corporate and governmental policies.

Although smart work inherently aims to enhance efficiency, it introduces unique challenges, particularly in performance evaluations. Performance evaluations in smart work environments are further complicated by potential biases introduced by digital tools, such as overreliance on quantitative metrics (e.g., task completion rates) while neglecting qualitative factors like collaboration and creativity¹¹. These limitations highlight the need for a hybrid approach that balances digital monitoring with human-centric evaluations. For instance, employees working remotely may feel anxious about potential biases in evaluations, especially when assessments focus solely on observable outputs while overlooking the nuances of work processes¹³. Nugroho¹⁴ highlighted that transitioning to remote work demands adaptive evaluation strategies that account for technological readiness and its impact on employee performance. The global COVID-19 pandemic further complicated performance evaluations, with many organizations reducing or even eliminating formal appraisals due to the challenges in measuring performance remotely¹⁵.

However, while existing literature addresses the limitations of performance evaluation in smart work contexts, it often focuses on theoretical frameworks or general recommendations rather than empirical validation of how these biases impact actual evaluations^7,8,11,13. For example, Raguseo et al.¹¹ discuss how remote work supports employee flexibility through ICT and HR practices, as well as the challenges in performance evaluation. However, they have not empirically examined how psychological factors that could influence performance evaluation affect evaluators’ perceptions and decision-making. Similarly, Gajendran and Harrison⁸ explored how telecommuting impacts individual performance outcomes through psychological mediators (e.g., perceived autonomy, work-family conflict, relationship quality), but did not focus on the cognitive processes of evaluators in the context of psychological distance that may arise from remote interactions.

This study aims to fill these gaps by empirically investigating the role of psychological distance in performance evaluation and focusing on how smart work conditions (remote work and reduced face-to-face interactions) alter evaluators’ decision-making processes. Unlike previous studies that mainly provide theoretical insights or broad frameworks, this research seeks to provide an empirical test of the hypothesis that psychological distance in smart work environments may lead evaluators to prefer outcome-based evaluations over process-based evaluations. The findings from this study will offer insights into how remote work environments influence evaluation systems and contribute to discussions on optimizing performance evaluation methods in increasingly digitalized work settings.

Psychological distance based on construal level theory

Construal Level Theory (CLT) is a theoretical framework that explains how individuals’ thought processes and decision-making are influenced by the psychological distance between themselves and an object or event. As proposed by Trope and Liberman¹⁶, CLT posits that an individual’s decision-making varies depending on their construal level of a particular object, which is determined by the psychological distance present in their environment or context. Psychological distance refers to the perceived separation between the self (Self), the current moment (Now), and the current location (Here) relative to the situation or object being considered. As this distance increases—whether temporal, spatial, social, or hypothetical—the individual perceives greater psychological distance, which in turn influences their cognitive processing^9,17,18.

As psychological distance increases, individuals tend to engage in high-level construal, focusing on the abstract, essential attributes of a target and prioritizing “why” aspects over concrete, process-related details^9,18. In contrast, when psychological distance decreases, low-level construal occurs, where individuals focus more on the practical, “how” aspects and pay attention to specific, contextual details. For example, when considering a future event, individuals may focus on the broader, desirable aspects of the event (e.g., the benefits of attending a concert) rather than the logistical details (e.g., how to get there)^19,20.

This phenomenon has been demonstrated in various fields, including cognitive science and marketing^21,22,23, where increased psychological distance leads individuals to prioritize desirability (such as quality or success) over feasibility (such as convenience or probability of success)¹⁰. For instance, people tend to place more emphasis on the magnitude of benefits if successful rather than the likelihood of success when the psychological distance is greater⁹. Additionally, temporal distance—the distance between the present moment and a future event—has been shown to influence product evaluations, with distant events being evaluated based on more abstract criteria²⁴.

Previous studies have also explored virtuality distance and its impact on construal level^25,26. In these contexts, when individuals experience high virtuality (such as in virtual teams or digital communication environments), the perceived psychological distance increases, and higher-level construal occurs. Conversely, in environments where interactions are more immediate and direct (low virtuality), individuals tend to focus on lower-level construal, attending to more tangible, concrete details. The study of Park²⁶ induced virtuality distance through videos, showing that when individuals are exposed to more remote or virtual environments, their construal level shifts to a more abstract, goal-oriented focus, akin to the effects of temporal or spatial psychological distance.

This research suggests that the effects of psychological distance on decision-making can extend beyond traditional contexts, influencing how individuals assess and make judgments in remote or digitally mediated environments. By applying CLT to smart work settings, where virtuality plays a significant role, this study aims to further explore how psychological distance shapes evaluators’ decision-making processes in performance evaluations.

Employee performance evaluation based on psychological distance in the context of smart vs. traditional work conditions

The traditional work environment is typically characterized by team members gathering in a physically confined space and communicating face-to-face while performing their tasks. Consequently, the perceived remoteness and virtuality in such an environment are low. In contrast, the smart work environment allows for remote operations through smart devices without the constraints of a fixed workspace, resulting in a relatively higher perception of remoteness and virtuality compared to traditional settings²⁷.

Individuals perceiving low virtuality feel that their situation is closely aligned with the real world, whereas those perceiving high virtuality feel more detached from reality^26,27. For example, in a traditional work setting, when working on a project with team members, the psychological distance between individuals is minimal due to face-to-face communication and shared physical space. Conversely, in a smart work environment, the inherent remoteness means work is not confined to the same space, and the lack of face-to-face communication increases the psychological distance between team members, as illustrated in Fig. 1.

Psychological distance on personal depending on work condition (left: short, right: long).

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Similarly, regarding the relational distance between team members within a group, individuals who perceive low virtuality tend to feel a stronger sense of belonging as group members with their team members, and consequently perceive the psychological distance to be closer. On the other hand, individuals who perceive high virtuality tend to feel a weaker sense of belonging as group members with their team members, and consequently perceive a greater psychological distance, as shown in Fig. 2. Therefore, the following hypothesis can be proposed:

Psychological distance in group depending on work condition (left: short, right: long).

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Hypothesis 1

In a smart work environment, the psychological distance between individuals will feel greater than in the traditional environment.

An individual’s perceived virtuality can influence the level of construal during subsequent decision-making processes^25,26,27. In scenarios where virtuality is high, individual decisions will focus more on the goal itself (End-related Thinking Style) rather than on the means required to achieve the goal (Means-related Thinking Style). Furthermore, when evaluating specific objects or events in highly virtual situations, individuals are more likely to emphasize desirability over feasibility. For example, an individual who perceives high virtuality will evaluate an outcome based on its desirability rather than the process or feasibility of achieving that outcome. In other words, individuals who perceive high virtuality utilize a high-level construal of thinking that predominantly pursues desirability in subsequent decision-making and evaluation processes^16,19. As a result, even in a smart work environment, when evaluating an individual’s performance, there is a high likelihood that the performance evaluation will focus on the level of outcome or desirability of the result rather than the process or feasibility, such as the degree of effort. Based on this reasoning, the following hypothesis is proposed:

Hypothesis 2

In a smart work environment, performance evaluation will be outcome-centric rather than process-centric.

Hypothesis 2-1

In a smart work environment, changes in the level of outcome generated by an individual will have a positive effect on performance evaluation.

Hypothesis 2-2

In a smart work environment, changes in individual effort levels will not affect performance evaluation.

In situations where virtuality is low, individual decisions are likely to be made with a focus on means-related thinking rather than end-related thinking. In other words, when evaluating specific objects or events under low virtuality conditions, individuals will prioritize feasibility over desirability in their assessments. For instance, individuals perceiving low virtuality are more likely to focus on process-oriented factors, such as feasibility or the degree of effort, rather than on the outcome itself or its desirability. In this context, individuals experiencing low virtuality engage in low-level construal, which emphasizes feasibility in subsequent decision-making and evaluation processes. Consequently, even in traditional environments, when evaluating an individual’s performance, it is highly probable that the evaluation will center around the degree of effort or the process rather than the outcome or desirability of the performance. Based on this reasoning, the following hypothesis is proposed:

Hypothesis 3

In traditional environments, performance evaluation will be process-centric rather than outcome-centric.

Hypothesis 3-1

In traditional environments, changes in the level of outcome generated by an individual will have a positive effect on performance evaluation.

Hypothesis 3-2

In traditional environments, changes in an individual’s effort level will have a positive effect on performance evaluation.

Design and participants

To empirically examine the hypotheses, this study employed a 2 × 2 × 2 factorial experimental design, manipulating work style (traditional vs. smart work), effort level (more vs. less), and outcome level (high vs. low). This design was chosen to systematically investigate how different work environments and conditions influence evaluators’ perceptions of employee performance. While performance evaluation is influenced by multiple factors, including task nature, team composition, and evaluator characteristics, our study primarily focuses on the impact of physical separation and virtuality on psychological distance in smart work settings. To mitigate the potential impact of uncontrolled variables, participants were randomly assigned to conditions, ensuring that extraneous factors were evenly distributed across experimental groups. This methodological choice helps isolate the effect of psychological distance on evaluation tendencies while allowing for a more realistic representation of smart work environments.

A total of 200 participants (100 male, 100 female) were recruited through targeted outreach via professional networks and industry affiliations. Data collection was conducted over two weeks through both online and offline methods. The study specifically targeted working professionals between the ages of 35 and 45, as this group represents a key segment of the workforce in South Korea, where smart work practices are actively being implemented or considered. By focusing on this demographic, we aimed to ensure that all participants had prior exposure to smart work environments or traditional office settings, thereby enhancing the study’s practical relevance.

To ensure that all participants had relevant experience in evaluating employee performance, they were required to meet specific eligibility criteria. All participants had prior experience as a team leader in a professional or academic setting, ensuring that they were familiar with assessing subordinates’ work. Additionally, they were required to have held a supervisory role, either currently or in the past, with direct responsibility for evaluating team members’ performance. While we did not collect detailed income or occupational data, our targeted sampling approach ensured that participants were actively employed in roles relevant to performance evaluation. The participants’ average age was 39.76 years (SD = 2.95). By ensuring that all participants had prior leadership and evaluation experience, the study aimed to enhance the reliability of performance assessments across different experimental conditions.

Procedure

To examine how work style, effort level, and outcome level influence performance evaluations, participants were randomly assigned to one of eight experimental conditions, following a 2 × 2 × 2 factorial design. The study was conducted over a period of two weeks using a structured, scenario-based survey method.

Each participant was instructed to assume the role of a team leader managing a three-member team (A, B, and C) responsible for contributing to a final report, which was planned to be approximately 100 pages in total. At the start of the project, which was described as beginning in September (an arbitrary date used for scenario consistency), participants, in their role as team leaders, assigned each team member the task of drafting 15 pages for a mid-project review scheduled for today. The scenarios were designed to manipulate work style (traditional vs. smart work), effort level (more vs. less), and outcome level (high vs. low).

In the smart work condition, team members were presented with a remote work environment where they could complete their reports regardless of location and communicate as needed using online tools. In contrast, the traditional work condition described a setting where team members worked in the same physical office, allowing for direct face-to-face interactions throughout the task. A and B were consistently assigned to submit 15 pages across all experimental conditions to maintain uniformity in results, while C’s output varied, with some cases exceeding expectations at 23 pages and others falling short at 7 pages.

Following this scenario, participants were asked manipulation check questions to assess their perception of the work environment, along with measures to determine the psychological distance they felt toward their team members (see Table 1 for detailed measures). Additionally, participants evaluated the outcome level of team member C based on their submitted work. Next, participants were presented with an interview scenario, in which they, as the project leader, conducted a follow-up meeting with team member C, who had submitted a different number of pages than expected. The response provided by C varied depending on the experimental condition.

After reading team member C’s justification, participants were asked to evaluate C’s effort level, considering the explanation provided during the interview. Following this, participants rated the overall performance of all three team members (A, B, and C), using both a 7-point Likert scale and an A–E grading system. This final step allowed the study to measure how work condition, psychological distance, outcome level, and perceived effort influence performance evaluations across all team members.

Ethical considerations

The present study was conducted in full compliance with the ethical research guidelines and was reviewed and approved by the Kookmin University Institutional Review Board (KMU-202410-HR-435). All participants provided informed consent before participating, and they were informed that their participation was voluntary and that they could withdraw at any time without any consequences. To protect participant confidentiality, all responses were anonymized, and no personally identifiable information was collected. To ensure data integrity and reliability, random assignment of participants to experimental conditions was implemented to minimize potential biases. Additionally, manipulation checks were conducted to confirm that participants correctly perceived the assigned work style, effort level, and outcome level conditions.

Measurements

Independent variables

The independent variables in this study were work style, effort level, and outcome level. These variables were measured using specific items designed to assess participants’ perceptions based on the experimental conditions. Work style was categorized into traditional work and smart work. The traditional work condition refers to an environment where employees perform their tasks in the same physical space and engage in face-to-face communication. In contrast, the smart work condition refers to a remote work environment where employees can work autonomously from any location and communicate online. These definitions were provided to survey participants to ensure they clearly understood the conditions when responding.

To confirm that the scenario-based experimental conditions were appropriately manipulated, questions were presented to survey participants. Specifically, to assess the work style conditions, three aspects were measured: the degree of freedom in the workspace, the degree of collaboration during the work process, and the degree of communication both online and offline.

The outcome level was measured to assess how participants perceived the performance of team member (e.g., member C) in achieving their respective tasks. The outcome level focused on four key aspects: whether the member C achieved the goal, the amount of work submitted compared to other team members, whether the member C made a successful submission of work, and whether the member C successfully completed the task. These aspects were evaluated using the following questions: ‘Achieved the goal’, ‘Amount of work submitted compared to other team members’, ‘Successful submission of work’, and ‘Successful completion of the task’.

The effort level was measured to assess participants’ perceived dedication and engagement in completing the assigned task. This variable was measured using four items that evaluated both quantitative and qualitative aspects of effort. Specifically, ‘participants were asked to rate’, ‘the amount of time spent writing the report’, ‘the level of effort put into writing the report’, ‘the level of sincerity in writing the report’, and ‘their overall sincerity in performing the task’. These items were designed to capture the extent to which participants exerted effort in their assigned work. All questions were rated on a 7-point bipolar scale, ranging from ‘not at all (1)’ to ‘very much (7).’

Dependent variables

This study examined two dependent variables: (1) psychological distance, which reflects the level of perceived closeness between evaluators and employees in different work environments, and (2) performance evaluation, which captures the actual assessment of employee performance by evaluators. Psychological distance was measured using Schubert and Otten (2002) Pictorial Measures, which included two items assessing the perceived distance and closeness between the survey participant (team leader) and their team members. The first item evaluated the subjective closeness between the team leader and their team members, while the second assessed perceived agreement between the team leader and team members within the organization. Both items were rated on a 7-point bipolar scale, with higher scores indicating greater psychological distance. Performance evaluation was assessed using a 7-point bipolar scale, where participants rated the employee’s performance on a continuum ranging from “poor (1)” to “excellent (7)”. This measure was designed to capture the direct performance assessment made by evaluators across different work conditions.

To ensure the robustness of the measurements, the validity and reliability of the independent variables were assessed using principal component analysis (PCA) and Cronbach’s alpha. As shown in Table 2, all constructs demonstrated strong internal consistency, with Cronbach’s alpha values exceeding 0.95 and factor loadings above 0.95, confirming the robustness of the measures. The eigenvalues for each construct exceeded 1.7, indicating that the extracted components accounted for a substantial proportion of variance. These results confirm that all measurement constructs were valid and reliable, ensuring the robustness of the study’s findings.

Manipulation check

To confirm that the three experimental variables considered in this study were manipulated as intended, an ANOVA test was conducted using the measurement questions for each variable. To verify the work method, the average scores from three questions (Eigen Value = 2.850, Cronbach’s alpha = 0.972) regarding remoteness and virtuality of the work method were analyzed to determine if there were differences based on the work method conditions. Participants exposed to conditions presented in a smart work manner scored higher on average than those in traditional work conditions (M_{smart_work} = 4.47, M_{traditional_work} = 3.30, F(1198) = 73.001, p < 0.001). This confirms that the work method was manipulated as intended.

Additionally, to check whether the team member’s work performance, presented as an evaluation target, was properly manipulated, the averages of four questions (Eigen Value = 3.756, Cronbach’s alpha = 0.978) relating to the team member’s work performance were compared. Participants who viewed stimuli presented in a high performance condition had a higher mean score than those in a low performance condition (M_{high_performance} = 4.46, M_{low_performance} = 3.33, F(1199) = 69.267, p < 0.001). This indicates that the manipulation of work performance was successful.

Lastly, a manipulation check was conducted to verify that the team member effort condition, which manipulated the level of effort the team member exerted during the project period, was accurately represented. The averages of four questions related to team members’ efforts (Eigen Value = 3.714, Cronbach’s alpha = 0.974) were compared. The average score for the condition in which team members actively participated in the project was 4.40, significantly higher than the average for the condition in which they participated less actively, which was 3.42 (F(1198) = 53.825, p < 0.001). Summarizing these results, it can be confirmed that each variable was well manipulated as intended in the study.

Hypotheses test

To test the hypothesis regarding the relationship between smart work and psychological distance proposed in this study, we examined how the average scores from two questions measuring psychological distance (Eigen Value = 1.942, Cronbach’s alpha = 0.96) varied based on work style. It was confirmed that the psychological distance between individuals in the smart work environment (M = 4.54) was greater than in the traditional method (M = 3.46) (F(1198) = 61.028, p < 0.001), thus supporting Hypothesis 1 ( Fig. 3).

Psychological distance depending on work style.

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To examine the relationship between work style, outcome level, and effort in the context of team leadership, an ANOVA analysis was conducted, with the performance evaluation of team member C as the dependent variable and work style, outcome level, and effort as independent variables. Table 3 presents the main effects, two-way interactions, and three-way interaction effects of these variables.

The ANOVA analysis revealed that the main effects of outcome level and effort were statistically significant. Specifically, higher outcome levels (i.e., superior performance) and greater effort by team members were positively associated with enhanced overall performance evaluations. However, there was no significant main effect of work style (smart work vs. traditional work) on performance evaluations (F = 2.226, P = 0.137), suggesting that the mode of work did not independently influence evaluation outcomes.

Additionally, a two-way interaction between work style and effort was identified, as illustrated in Fig. 4. The left side of the graph indicates that in traditional work settings, effort level had a significant effect on performance evaluations, meaning that higher effort was associated with higher ratings. In contrast, in the smart work condition (right side of the graph), variations in effort did not significantly influence performance evaluations. This suggests that while effort is a key factor in traditional work environments, it plays a diminished role in performance evaluations within smart work settings.

Performance evaluation by work style and effort.

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A significant three-way interaction was identified, allowing for an examination of process-oriented versus outcome-oriented evaluations based on the work method proposed in this study. As illustrated in Fig. 5, in the traditional work setting, performance evaluations were significantly influenced by both outcome level (F(1,96) = 69.143, p < 0.001) and effort (F(1,96) = 96.571, p < 0.001). Additionally, the interaction effect between outcome level and effort was also significant (F(1,96) = 8.501, p < 0.001). This suggests that in traditional work environments, performance evaluations are not solely outcome-centered but also process-oriented, with higher evaluations awarded when both effort and outcome were favorable.

Performance evaluation in the traditional work style.

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Conversely, in the smart work setting, only outcome level had a significant effect on performance evaluations (F(1,96) = 96.785, p < 0.001), while effort and the interaction between outcome level and effort were not significant (ns). As depicted in Fig. 6, these findings indicate that evaluations in smart work environments are primarily outcome-driven, with less emphasis placed on effort and the overall process. These results support the hypothesis that in a smart work environment, the psychological distance between team members increases, leading evaluators to focus more on results rather than processes.

Performance evaluation in the smart work style.

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Based on the findings above, the summary of hypothesis testing results is presented in Table 4.

Summary of the findings

This study examines remoteness and virtuality as defining characteristics of smart work and applies the concept of psychological distance from construal level theory to explore how individual evaluations are shaped in a smart work environment. The findings indicate that smart work, characterized by high levels of remoteness and virtuality compared to traditional work settings, increases psychological distance among individuals. This heightened psychological distance leads to a greater level of abstraction and a stronger emphasis on outcome-oriented thinking. Conversely, in traditional work environments, where psychological distance is relatively lower, individuals engage in less abstract thinking and consider both the process and the results in their evaluations. These findings highlight the critical role of psychological distance in shaping evaluation tendencies across different work modalities.

Theoretical implication

This study offers a unique theoretical perspective by exploring smart work through the lens of psychological distance. While existing research has largely focused on the operational benefits and challenges of smart work, our study introduces a deeper understanding of the psychological dynamics within remote work environments. Specifically, it investigates how the characteristics of smart work—such as increased virtuality and reduced face-to-face interactions—influence psychological distance among team members. By examining how these factors shape interpersonal dynamics, this research provides valuable insights into the impact of psychological distance on performance evaluations in smart work settings. This contribution extends our understanding of remote work environments, offering a more nuanced view of how team interactions in these contexts differ from traditional work settings.

Another key contribution of this study is the application of Construal Level Theory (CLT) to performance evaluations in smart work environments. While CLT has been widely used to understand decision-making and behavior in a variety of contexts, its application to performance evaluation in remote work settings remains underexplored. This study demonstrates how virtuality, a key feature of smart work, functions as a dimension of psychological distance, which influences evaluators’ cognitive processes. By integrating virtuality into CLT, this research contributes to the development of CLT-based frameworks that can better explain the decision-making processes in remote work contexts, particularly regarding how evaluators weigh outcomes versus effort. This provides a more refined understanding of how psychological distance shapes performance evaluation strategies in digital and remote work environments.

This study also lays the groundwork for future research on smart work by establishing a connection between psychological distance and performance evaluation. It highlights the role of psychological distance in influencing evaluators’ decisions and offers a new avenue for investigating how remote work environments affect organizational processes. Building on this, future studies could explore how team dynamics, leadership strategies, and task design in smart work settings interact with psychological distance to shape performance management systems. The findings suggest that understanding the influence of psychological distance can lead to the development of more effective performance evaluation strategies in smart work environments. This research can inform the design of performance management systems that account for the psychological aspects of remote work, contributing to the optimization of work environments in an increasingly digitalized world.

Additionally, research by Lee, et al.¹³ highlights the critical role of institutional frameworks in promoting the successful implementation and diffusion of smart work. They emphasize that smart workers should not face disadvantages in their work conditions, especially concerning performance evaluations and human resource management. They argue that clear institutional recognition and structured systems are necessary to enable the effective use of smart work practices. In particular, Kurland and Egan²⁸ identified face-to-face cultures as a major obstacle to the widespread adoption of smart work, advocating for evaluation systems that focus on qualitative performance rather than quantitative metrics. This insight further supports the importance of adapting evaluation systems to smart work environments, where process-oriented factors, such as effort and collaboration, should be prioritized over merely output-based evaluations²⁸.

Practical implication

The findings of this study provide a number of valuable insights that can guide organizations as they adopt or refine smart work practices. As smart work becomes more widespread, organizations need to rethink their performance evaluation systems to ensure they are effective in a remote work environment. Traditional evaluation methods that rely heavily on physical presence and direct supervision may not be suitable in this context. With the increased psychological distance that comes with working remotely, it is essential that organizations develop clear and transparent evaluation criteria that emphasize outcome-based assessments. One effective way to accomplish this is by incorporating digital tools to track and assess employee contributions, even when working remotely. This ensures that performance evaluations remain fair and accurate despite the lack of in-person observation.

Furthermore, organizations must carefully evaluate the nature of tasks and the dispositions of employees to determine which roles are best suited for remote work. Some tasks, particularly those that focus on outcomes and goals rather than processes, are more suited to a smart work model. Similarly, employees who are comfortable working independently and focusing on high-level goals rather than detailed processes may perform better in a remote environment. For this reason, it is important for organizations to assess both the tasks and personal characteristics of employees before implementing smart work. Ensuring the right fit between tasks and employees can significantly enhance productivity and job satisfaction, as both will be better aligned with the demands of smart work.

Another important implication from this study is the necessity for organizations to provide the right technological infrastructure. Remote work can only be effective if employees have access to the right tools for communication, collaboration, and performance tracking. Digital platforms such as Slack for communication²⁹, Asana or Trello for task management³⁰, and Zoom or Microsoft Teams for virtual meetings are essential to ensure that performance is tracked accurately and that teams can collaborate effectively³¹. In addition to the tools, it is essential for organizations to establish clear performance guidelines and regular feedback mechanisms to keep employees motivated and aligned with the organization’s goals. By providing this structure, organizations can support employees in performing to the best of their abilities in a remote work environment.

Finally, organizations must also implement supportive policies to create a productive and healthy remote work culture. Offering mental health resources, professional development opportunities, and flexible work hours will help ensure employees feel supported, both professionally and personally. These policies can also contribute to employee engagement, as employees will feel valued and motivated, ultimately leading to better performance and higher satisfaction in a smart work environment.

Limitation and future research

While this study offers valuable insights into the relationship between work style and performance evaluation, it is important to consider its limitations. One key limitation is the use of hypothetical scenarios rather than actual project outcomes, which may limit the external validity of the findings in real-world contexts. Additionally, this study primarily focused on effort and performance outcomes, potentially overlooking other important variables, such as personal traits, task types, team composition, and contextual factors, that could influence performance evaluation processes. While these factors undoubtedly play a role in shaping performance assessments, our study specifically aimed to isolate the effect of psychological distance created by smart work conditions. To account for the potential influence of uncontrolled variables, we employed random assignment, which naturally distributes these factors across conditions and reduces their impact on the findings. However, we acknowledge that future research could explore how these variables interact with psychological distance in performance evaluations. Investigating how team dynamics, leadership structures, or task complexity moderate the observed effects would provide a more comprehensive understanding of performance evaluations in smart work settings.

Furthermore, future studies could explore the universality and transferability of these findings. Specifically, research could examine how the results of this study apply to different work environments, industries, and cultural contexts. For instance, examining the impact of smart work in organizations with varying degrees of digital infrastructure, or across different geographical locations, would help assess whether the observed effects hold universally. Longitudinal studies could also explore how psychological distance and performance evaluation processes evolve over time, providing deeper insights into the long-term implications of smart work practices. By addressing these areas, future research can provide a broader understanding of the applicability and transferability of these findings, further contributing to the development of effective strategies for managing performance in diverse, increasingly digital work environments.

Moreover, the sample was limited to working professionals between the ages of 35 and 45 with team leadership experience, which may not fully reflect the diversity of experiences across a broader workforce. While this selection ensured that participants had direct exposure to performance evaluations in both traditional and smart work environments, it also introduces a limitation in terms of generalizability. Future research could expand the demographic scope to include participants from different age groups, industries, and economic backgrounds to assess whether our findings hold across diverse professional settings. Additionally, incorporating occupational and income-based demographic variables could provide further insights into how job roles and economic status influence the psychological dynamics of performance evaluations in smart work environments.