Algorithmic fatigue, human talent adaptation, and perceived digital competitiveness among general and financial services professionals: a configurational analysis

1 Introduction

Digital transformation changes not only technologies but also organizational routines, value-creation processes, employee roles, and decision structures. Research describes it as a process in which digital technologies trigger strategic and structural responses that may generate both beneficial and adverse organizational outcomes (Verhoef et al., 2021; Vial, 2019). In general and financial services, employees increasingly interact with digital platforms, automated workflows, customer-management systems, online reports, and algorithmic performance indicators. These systems can improve speed, coordination, and analytical capacity while simultaneously increasing monitoring, information intensity, and demands for continuous adaptation (Kellogg et al., 2020; Parent-Rocheleau and Parker, 2022).

Algorithmic fatigue is defined in this study as perceived psychological, cognitive, and work-related depletion associated with sustained exposure to digitally mediated demands, including overload, complexity, uncertainty, insecurity, and mental exhaustion. The construct is not treated as a synonym for technostress. Instead, it captures a focused employee assessment of resource depletion produced by the combined use of platforms, automated systems, artificial intelligence, and data-driven management practices. This distinction is important because algorithmic systems can simultaneously enable performance and alter autonomy, evaluation, task allocation, and organizational control (Liu et al., 2024; Zayid et al., 2024).

The literature remains fragmented in three respects. First, studies of digital strain frequently examine adverse employee outcomes separately from the organizational capabilities that may compensate for those demands. Second, research on digital capabilities and competitive performance often emphasizes technological resources while giving less attention to employee adaptation, organizational support, and responsible algorithmic governance. Third, most empirical models estimate isolated net effects, even though the same level of perceived digital competitiveness may arise through different combinations of human, organizational, technological, and ethical conditions. Evidence that digital capabilities generate competitive value through broader organizational capabilities supports the need for an integrated model (Mikalef et al., 2020).

This study addresses that gap by connecting conservation of resources theory with dynamic capabilities theory in a configurational framework. Conservation of resources theory explains why intensive digital demands can threaten or deplete valued cognitive, emotional, temporal, and occupational resources (Hobfoll, 1989). Dynamic capabilities theory explains how organizations sense opportunities, seize digital possibilities, and reconfigure resources in changing environments (Teece et al., 1997). Perceived organizational support and responsible governance provide the organizational mechanisms through which employee adaptation and technological capabilities can be converted into perceived competitive value.

The objective is to determine how algorithmic fatigue, human talent adaptation, digital organizational support, digital dynamic capabilities, and responsible algorithmic governance combine to explain high perceived digital competitiveness among professionals working in general and financial services. The contribution is twofold: theoretically, it links resource depletion and capability-building mechanisms within one sociotechnical model; methodologically, it uses fsQCA to identify necessity, equifinality, conjunctural causation, and causal asymmetry rather than assuming that one universal linear pathway applies to all participants (Pappas and Woodside, 2021).

2 Theoretical framework

2.1 Conservation of resources and algorithmic fatigue

Conservation of resources theory proposes that individuals seek to acquire, protect, and restore valued resources and experience strain when those resources are threatened, lost, or insufficiently replenished after investment (Hobfoll, 1989). In digitally mediated work, attention, time, autonomy, confidence, occupational security, and emotional energy are relevant resources. Algorithmic monitoring, rapidly changing interfaces, automated evaluation, and continuous information flows can consume these resources even when the technology improves operational efficiency.

Algorithmic fatigue therefore represents the resource-depletion side of the model. Its ten indicators capture pressure to work faster, additional tasks, difficulty understanding systems, skill uncertainty, job insecurity, frequent platform changes, mental tiredness, and psychological exhaustion. This operationalization is broader than a repeated use of the label technostress and is directly linked to how algorithmic and digital systems organize contemporary work. Research on algorithmic control and job design similarly shows that digital management can reshape autonomy, task demands, feedback, and perceptions of threat or fairness (Kellogg et al., 2020; Parent-Rocheleau and Parker, 2022; Zayid et al., 2024).

2.2 Human talent adaptation and digital organizational support

Human talent adaptation refers to employees' capacity to learn new systems, revise routines, solve digitally mediated problems, and collaborate with technological tools. Adaptation is treated as an active organizational resource rather than an individual obligation imposed on workers. Digital competencies and flexible learning enable employees to transform technological access into productive use, especially in frontline sales and service work (Süße et al., 2025).

Digital organizational support provides the complementary context for adaptation through training, technical assistance, autonomy, process clarity, feedback, and psychological safety. Perceived organizational support theory holds that employees form general beliefs about whether the organization values their contribution and cares about their well-being (Eisenberger et al., 1986). In this model, support replenishes resources threatened by digital demands and reduces the likelihood that technological change is experienced solely as pressure or opaque control.

2.3 Digital dynamic capabilities and perceived digital competitiveness

Dynamic capabilities are the organizational capacities to identify digital opportunities, mobilize resources, experiment, use data, and reconfigure processes in response to change. They differ from the simple ownership of technologies because they concern how resources are integrated and renewed (Teece et al., 1997). Empirical research shows that data and analytics capabilities can contribute to competitive performance through dynamic and operational capabilities rather than through technology alone (Mikalef et al., 2020).

The outcome is termed perceived digital competitiveness because all ten indicators are employee-reported assessments of the organization's efficiency, productivity, innovation, market responsiveness, differentiation, and data-driven decision-making. The construct therefore represents perceived organizational capability, not audited market share, profitability, or objective competitive performance. Renaming the outcome prevents a perceptual measure from being interpreted as a direct firm-level performance indicator.

2.4 Responsible algorithmic governance

Responsible algorithmic governance refers to transparent, fair, supervised, privacy-conscious, and contestable use of digital tools, data, and automated decisions. Global analyses of AI ethics frameworks consistently identify transparency, justice, responsibility, privacy, and non-maleficence as central principles (Jobin et al., 2019). Organizational decision structures also require deliberate choices about when decisions should be delegated to algorithms, retained by humans, or performed through hybrid arrangements (Shrestha et al., 2019). This governance perspective also recognizes that artificial intelligence creates an automation-augmentation paradox: systems may replace some tasks while complementing human judgment in others (Raisch and Krakowski, 2021).

2.5 Configurational model and directional expectations

The model integrates a risk condition and four enabling conditions. High algorithmic fatigue is expected to constrain perceived digital competitiveness because it signals resource depletion. Human talent adaptation, digital organizational support, digital dynamic capabilities, and responsible algorithmic governance are expected to support the outcome by strengthening competence, replenishing resources, enabling reconfiguration, and creating trust. However, the framework does not assume that every enabling condition must be present in every successful case.

The configurational expectation is that high perceived digital competitiveness can emerge through alternative sufficient pathways. Low algorithmic fatigue may support the outcome, but strong adaptive and organizational resources may also compensate for its presence. This possibility of compensation, together with equifinality and causal asymmetry, provides the theoretical basis for fsQCA rather than a model restricted to additive net effects.

3 Materials and methods

The research used a quantitative, non-experimental, cross-sectional, and exploratory-configurational design. Variables were measured through participants' perceptions in their actual work context and were not manipulated. The unit of analysis was the employed participant's assessment of digitally mediated work and organizational capabilities. The configurational design was selected because the research question concerns combinations of conditions associated with high perceived digital competitiveness rather than a single universal net effect.

The instrument comprised six multi-item constructs: algorithmic fatigue, human talent adaptation, digital organizational support, digital dynamic capabilities, responsible algorithmic governance, and perceived digital competitiveness. The first five constructs were modeled as causal conditions and perceived digital competitiveness as the outcome. Each construct contained ten items rated from 1 = strongly disagree to 5 = strongly agree, for a total of 60 items. Composite scores were calculated as arithmetic means after checking missing values, response ranges, corrected item-total correlations, Cronbach's alpha, and McDonald's omega. Table 1 summarizes the constructs, item groups, and methodological interpretation used in the study.

Table 1. Constructs, item groups, and methodological interpretation.

3.1 Participants and occupational context

The institutional population consisted of 581 Business Administration students. The analytical sample included 128 participants who were employed in general or financial services and therefore had direct experience with digital platforms, automated systems, online reporting, performance metrics, or data-driven tools. Employment in these areas was the inclusion criterion; student status was not treated as evidence of organizational knowledge. Seventeen percent of participants held managerial or coordination roles, while the remaining 83% held middle-level professional, administrative, or operational roles. Table 2 summarizes the occupational position profile of the participants. Because company-size information was not collected, the employers cannot be classified empirically as SMEs, and the study is framed around general and financial service organizations.

Table 2. Occupational position profile of the participants.

Percentages correspond to the occupational distribution reported for the 128 employed participants.

Participation was voluntary, responses were treated confidentially, and analyses were conducted only in aggregate form. No personally identifying or sensitive employment information was included in the analytical database. The questionnaire was used for academic research, and participants could choose whether to complete it.

3.2 Analytical strategy and software

All analyses were conducted in R through the RStudio environment. Descriptive statistics, construct-level frequency distributions, reliability coefficients, Spearman correlations, the auxiliary linear model, and collinearity diagnostics were calculated in R. The set-theoretic procedures were implemented with the QCA workflow and included direct calibration, necessity analysis, truth-table construction, and complex, parsimonious, and intermediate solution minimization.

fsQCA was selected because the primary objective was to identify necessary and sufficient combinations under equifinality, conjunctural causation, and causal asymmetry. A covariance-based path model or SEM would answer a different question focused on average net effects. In addition, estimating six latent variables with 60 indicators would impose a substantial parameter burden on a sample of 128. Reliability, correlations, and an auxiliary regression were therefore used as complementary evidence rather than as substitutes for the configurational analysis. Future studies with larger samples may combine PLS-SEM and fsQCA to examine symmetric and asymmetric relationships jointly (Rasoolimanesh et al., 2021).

Direct calibration used three substantive anchors: 2.00 for full non-membership, 3.00 for the crossover, and 4.00 for full membership. These values were selected from the semantic structure of the five-point scale rather than from sample-dependent percentiles: a score of 2 represents disagreement with membership in the target set, 3 represents maximum ambiguity or neutrality, and 4 represents agreement with membership. The direct logistic transformation mapped these anchors approximately to 0.05, 0.50, and 0.95 membership, respectively. This approach follows the requirement that calibration thresholds be theoretically and substantively interpretable (Pappas and Woodside, 2021; Ragin, 2008).

The truth table used a consistency cutoff of 0.80 and a frequency cutoff of two cases. The frequency threshold excluded single-case configurations while retaining sufficient diversity for 128 observations. Directional expectations were absence of high algorithmic fatigue and presence of human talent adaptation, digital organizational support, digital dynamic capabilities, and responsible algorithmic governance. PRI was examined as an additional diagnostic of contradictory sufficiency.

4 Results

Table 3 presents descriptive statistics and internal reliability. Means ranged from 3.13 to 3.44, but central tendency alone did not determine whether a construct contained enough heterogeneity for configurational analysis. Table 4 therefore adds frequency bands linked to the calibration thresholds and item-level ranges of means and standard deviations.

Table 3. Descriptive statistics and internal reliability of the constructs.

Table 4. Distribution of construct scores and item-level variability.

The bands correspond to scores below the crossover, between the crossover and full-membership anchors, and at or above the full-membership anchor. Item ranges summarize the ten indicators within each construct.

Algorithmic fatigue had a mean close to the scale midpoint (M = 3.13), but it was not empirically constant. Scores ranged from 1.50 to 4.60, the interquartile range was 0.73, 37.5% of participants scored below 3.00, and 10.2% scored at or above 4.00. In addition, the ten algorithmic-fatigue items had standard deviations from 0.89 to 1.12. These indicators demonstrate sufficient between-participant and item-level variability to retain algorithmic fatigue as a causal condition.

All constructs exceeded 0.80 for both Cronbach's alpha and McDonald's omega. Perceived digital competitiveness showed the highest reliability (alpha = 0.908; omega = 0.909), followed by human talent adaptation (alpha = 0.884; omega = 0.885). Corrected item-total correlations were positive and acceptable across all constructs, supporting the use of composite scores for the subsequent configurational analysis.

Figure 1 provides a descriptive profile only. Human talent adaptation had the highest mean, followed by perceived digital competitiveness, digital dynamic capabilities, responsible algorithmic governance, and digital organizational support. Algorithmic fatigue remained above the midpoint. No inferential test of differences among construct means was performed because the constructs represent distinct theoretical domains rather than repeated interchangeable measures; the figure should therefore not be interpreted as evidence that one mean is statistically higher than another.

Figure 2 displays the reliability coefficients and the conventional 0.70 reference line. The close values of alpha and omega show that internal consistency was not dependent on a single estimator. These results support score aggregation but do not, by themselves, constitute a full confirmatory factor analysis.

Spearman correlations were used to examine monotonic associations among construct scores. Perceived digital competitiveness was positively associated with human talent adaptation (rho = 0.73), digital organizational support (rho = 0.64), digital dynamic capabilities (rho = 0.54), and responsible algorithmic governance (rho = 0.51). Algorithmic fatigue was negatively associated with the outcome (rho = −0.21). These coefficients describe association and do not establish causal effects. Figure 3 presents the Spearman correlation heatmap among the six constructs and visually summarizes the association structure reported above.

Table 5 reports an auxiliary linear association model. It is included to show the direction and relative magnitude of average associations, not to replace fsQCA or to establish a structural causal model. The model explained 66.55% of the variance in perceived digital competitiveness (adjusted R-squared = 65.18%). Human talent adaptation had the largest positive coefficient (beta = 0.450, p < 0.001), followed by digital dynamic capabilities (beta = 0.257, p = 0.002) and digital organizational support (beta = 0.243, p = 0.011). Algorithmic fatigue had a negative coefficient (beta = −0.130, p = 0.049), while responsible algorithmic governance was positive but not statistically significant at 0.05 (beta = 0.146, p = 0.084). All VIF values were below 3.00.

Table 5. Associations with perceived digital competitiveness and auxiliary collinearity diagnosis.

Table 6. Individual conditions of necessity and sufficiency for high perceived digital competitiveness.

The symbol “∼” represents absence or low membership in the set.

The average associations indicate that perceived digital competitiveness is more closely related to adaptive and organizational capabilities than to algorithmic fatigue alone. This pattern supports a configurational test because the effect of a risk condition may depend on the presence of compensatory resources and because different causal recipes can produce the same outcome.

Composite scores were calibrated into fuzzy sets using the direct 2.00/3.00/4.00 anchors described in the methods. Perceived digital competitiveness was the outcome, and the other five constructs were causal conditions. The truth table used consistency ≥ 0.80 and frequency ≥ 2. Necessity and sufficiency were interpreted as set-inclusion relations rather than as conventional statistical causation. Table 6 summarizes the necessity and sufficiency results for each individual condition.

Human talent adaptation was the only condition exceeding the conventional 0.90 necessity threshold (consistency = 0.904; coverage = 0.870). This means that cases with high perceived digital competitiveness were generally members of the set of high human talent adaptation. Digital organizational support, digital dynamic capabilities, and responsible algorithmic governance had high but sub-threshold necessity consistency and therefore were not classified as individually necessary.

At the single-condition level, human talent adaptation, digital organizational support, digital dynamic capabilities, responsible algorithmic governance, and the absence of high algorithmic fatigue showed sufficiency consistencies above 0.84. These results were treated as preliminary because configurational sufficiency is better evaluated through the minimized solution and its coverage, PRI, and unique coverage.

Figure 4 shows descriptive relationships between membership in each condition and membership in high perceived digital competitiveness. The fitted lines are visual summaries, not inferential tests. Positive patterns appear for human talent adaptation, digital organizational support, digital dynamic capabilities, and responsible algorithmic governance, while algorithmic fatigue shows a negative tendency with substantial dispersion. The dispersion is consistent with the expectation that fatigue can be compensated by other conditions.

The intermediate solution identified three alternative pathways to high perceived digital competitiveness: digital dynamic capabilities, responsible algorithmic governance, and the joint presence of human talent adaptation and digital organizational support. The overall solution had consistency = 0.806, PRI = 0.749, and coverage = 0.968, indicating that the pathways jointly represented a large share of membership in the outcome while maintaining acceptable set-theoretic consistency.

Table 7 confirms equifinality. The combination ATH*AOD had the highest pathway consistency (0.918), indicating that employee adaptation is most reliably associated with high perceived digital competitiveness when supported by training, technical assistance, process clarity, autonomy, and psychological safety. Digital dynamic capabilities and responsible algorithmic governance also operated as alternative sufficient pathways.

Table 7. Intermediate fsQCA solution for high perceived digital competitiveness.

In fsQCA, “*” means the logical combination “and”; “+” represents alternative pathways “or”.

The CDD pathway indicates that organizations can achieve high perceived digital competitiveness when they identify technological opportunities, experiment, use data, and reconfigure processes. The GAR pathway indicates that transparency, human oversight, fairness, and protection of work-related data can independently provide a credible route through which digital systems are accepted and converted into perceived organizational value.

Figure 5 displays the highest-consistency truth-table configurations and their empirical frequencies. Configurations without high algorithmic fatigue achieved some of the strongest consistencies, but high perceived digital competitiveness also occurred in configurations where fatigue was present together with adaptation, support, dynamic capabilities, and responsible governance. This pattern demonstrates compensation rather than a claim that fatigue is harmless.

The truth table contained 32 logically possible configurations. Sixteen were coded as sufficient for high perceived digital competitiveness, 15 remained unresolved, and one was coded as not sufficient. The sufficient rows contained 112 cases. This distribution supports the use of a configurational approach because participants reached the outcome through multiple combinations rather than one uniform profile.

Taken together, the findings indicate that adaptive human resources, organizational support, digital dynamic capabilities, and governance are the main components of perceived digital competitiveness. Algorithmic fatigue is a relevant risk condition, but its role is asymmetric and context dependent: its absence can support the outcome, while its presence can be offset by stronger enabling conditions.

The analytical sequence combined data-quality checks, reliability, descriptive distributions, association analyses, substantive fuzzy-set calibration, necessity tests, truth-table analysis, and solution minimization. The results should be interpreted as configurations of perceived organizational conditions and not as objective proof of firm-level competitive performance.

5 Discussion

The findings support a sociotechnical interpretation of perceived digital competitiveness. Technology is not experienced as competitively valuable merely because it is available; value is perceived when employees can adapt, receive organizational support, participate in responsible digital processes, and observe that technologies are used to improve organizational functioning. This interpretation is consistent with dynamic capabilities research, which emphasizes resource integration and reconfiguration rather than technology ownership alone (Mikalef et al., 2020; Teece et al., 1997).

Human talent adaptation was the strongest correlate and the only potentially necessary condition. This finding suggests that digital transformation depends on employees' ability to learn, revise routines, and solve problems in changing digital environments. However, the most consistent pathway combined adaptation with digital organizational support. Adaptation should therefore not be treated as the employee's sole responsibility; it becomes more reliable when the organization supplies training, technical assistance, autonomy, clarity, and psychological safety, consistent with perceived organizational support theory (Eisenberger et al., 1986).

Algorithmic fatigue was negatively associated with perceived digital competitiveness, but it did not function as an absolute barrier. Conservation of resources theory helps explain this pattern: resource depletion can weaken employees' capacity to convert technology into value, while compensatory resources can preserve functioning even under elevated demands (Hobfoll, 1989). The result is also compatible with research showing that algorithmic systems can produce both controlling and enabling effects depending on transparency, fairness, autonomy, and organizational design (Deng et al., 2025; Parent-Rocheleau and Parker, 2022).

Digital dynamic capabilities and responsible algorithmic governance were independent sufficient pathways. The first reflects sensing, experimentation, data use, and process reconfiguration. The second reflects transparency, fairness, privacy, human oversight, and accountability. Their independent presence in the solution indicates that perceived digital competitiveness can emerge through strategic reconfiguration or through trusted governance, although the strongest organizational design would integrate both.

The outcome must be interpreted as perceived digital competitiveness. Participants assessed whether their organizations use digital technologies to improve efficiency, innovation, responsiveness, differentiation, and decision-making. These responses provide meaningful information about employee-observed capabilities, but they do not establish objective market competitiveness, productivity, profitability, or financial performance. This distinction is especially important because company size and audited performance indicators were not collected.

The study has limitations. The 128 participants were employed Business Administration students working in general or financial services, so generalization to other occupations and sectors is limited. The cross-sectional and self-reported design may involve common-method, social-desirability, and interpretation biases. The occupational profile included mainly middle-level positions, with a smaller managerial or coordination group. fsQCA identifies set-theoretic necessity and sufficiency but not experimental causality. A full measurement model and SEM were not estimated, and future research should use larger samples, objective performance indicators, longitudinal designs, and combined SEM-fsQCA analyses.

6 Conclusions

High perceived digital competitiveness was not explained by a single universal condition. It emerged through three alternative pathways: digital dynamic capabilities, responsible algorithmic governance, and the combination of human talent adaptation with digital organizational support. Human talent adaptation was the only potentially necessary condition, while algorithmic fatigue operated as a risk that could be compensated by stronger human and organizational resources.

For general and financial service organizations, the practical implication is clear: digital transformation should combine employee learning, technical and psychological support, process reconfiguration, transparent rules, human oversight, and responsible data use. Future research should test whether these perceptual configurations predict objective productivity, innovation, customer, and financial outcomes across larger and more diverse occupational samples.

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Footnotes

Edited by: Digvijay Pandey, Department of Technical Education Uttar Pradesh, India

Reviewed by: Ethem Topcuoglu, Giresun University, Türkiye

Branislav Zagorsek, PitchBatch s.r.o. Business Incubator, Slovakia