Time pressure in organizational work contexts and software development

Time pressure–defined as the perception that the time available to complete a task is scarce in relation to the demands of the task [19, 20]–is common in organizational settings [21–23]. However, research is inconclusive concerning the relation between time pressure and employees’ behavior and performance [6]. Diverse results comprise indicators that time pressure and work performance have a positive [e.g., 20, 23, e.g., 24], negative [e.g., 25, 26], or an inverted U-shaped relationship [6]. These works also suggest that the level of time pressure is decisive for the type of impact: whereas moderate time pressure is necessary to ensure motivation, high or no time pressure leads to distraction or lack of stimulation, respectively.

The general inconclusive findings concerning time pressure hold for the domain of software development as well, finding no effect [27], positive effects [4], or an inverted U-shape effect [7].

On a more detailed conceptual level, it is traditionally presumed that tight deadlines lead to time pressure [8], which in turn tempts software developers to take shortcuts (i.e., reductions of software quality) since reporting delays is typically stigmatized in software projects [10]. People under time pressure do not necessarily work more efficiently (i.e., get more done in a smaller amount of time), they simply reduce the amount of work that needs to be done, which leads to worse software quality [3, 10], especially if performance cannot be observed or verified [28]. This typically applies to settings in which a software developer (an agent) can manipulate outputs at the expense of project quality without the supervising project manager (a principal) noticing this manipulation. Thus, agents can hazard the consequences of reducing software quality by taking quality-compromising shortcuts without having to fear punishment from their principal.

The usual approach to reduce shortcut taking is to add slack to the project schedule [10]. Such safety buffers are intended to reduce risks of wrong estimates [29, 30]. However, they lead to inefficiencies and also bear the risk that developers, knowing of the actual real estimates, misapply given buffers to reduce working speed [31], or that they engage into ‘gold plating’ [32], meaning that developers design, implement, test, document, and support ‘fancy’ or new features that are not required and lengthen the schedule.

Austin’s agency model

A game-theoretical model that is counter-intuitive at first glance, because it actually turns the usual slack-based approach on its head and claims a positive effect of higher levels of time pressure on software quality, has been proposed by Austin [10]. The model conceptualizes the “software quality under time pressure” scenario as follows. Two agents (i.e., software developers) compete for the favor of a principal (i.e., the project manager). This includes rewards (e.g., promotions, pay raises, future business) from the principal to whom they report at regular intervals. The players in the game are these two agents, who independently decide on whether to report their inability to meet a given deadline or to take a shortcut to meet it. The prevailing deadline-setting policy is represented by the probability p > 0 of a software developer being confronted with an unrealistic deadline and is taken as given. The central conclusion of the model is that a critical value of p (henceforth referred to as p_crit) exists; for p < p_crit, the number of shortcuts taken increases with p, whereas for p ≥ p_crit, shortcuts are completely avoided.

According to Austin [10], shortcuts “are decisions made in private [by software developers] that are motivated by a desire to stay on schedule, but are not in the best interests of the project”. Shortcuts can lead to serious software failure during runtime, and software developers are usually not (fully) aware of the possible consequences when they decide to take such shortcuts. It is also important to mention that software developers with concerns for quality who take a shortcut under time pressure would not do so in case of alleviated pressure. In addition, it is presumed that software developers do not have to fear personal consequences when taking shortcuts since it is difficult for non-specialists (such as the principal) “to trace complex system problems to causal sources” [10].

The project manager assigns software projects to the two developers. The respective developer is aware of her own deadline situation but does not know about the deadline situation of the other agent. (We refer to managers and software developers as ‘she’ or ‘her’, representing both sexes.) The deadline situations of the two agents are independent but are subject to the same probability p. In case of a realistic deadline, the respective agent delivers high quality with no decision to make. Otherwise, she must decide between delivering high quality (H), which results in missing the deadline, and taking a shortcut, which results in low quality (L) while meeting the deadline. The following penalties define the payoff of each outcome:

1. Concern for career (penalty C): Occurs in the case of one agent ‘looking bad’ in the eyes of the principal because she is behind schedule while the other agent is not. The penalty arises for an agent who chooses H in an unrealistic deadline situation, while the other agent either has an unrealistic deadline and takes a shortcut (i.e., chooses L) or has a realistic deadline.
2. Concern for quality (penalties Q1 and Q2): Represent the negative effect of poor software quality on the company’s image, which is presumed to reflect equally on all software developers in this company. The penalty arises for both agents if one (penalty Q₁) or both (penalty Q₂) have an unrealistic deadline and take a shortcut (L).

In the following, we presume that C > Q₂ > Q₁ > 0. Fig 1 displays the extensive form of Austin’s game. The agents’ payoffs (u₁, u₂) are shown on the respective leaves of the game tree. Note that the information sets I₁ and I₂ represent the missing information of the respective agent about the deadline situation of the other. Four situations exist:

1. Both agents face an unrealistic deadline (branch 1, with probability p²): In this case, both agents must choose simultaneously between high quality (H) and taking shortcuts (L).
2. Only agent 1 faces an unrealistic deadline (branch 2, with probability p(1-p)): In this case, only agent 1 must choose between H and L.
3. Only agent 2 faces an unrealistic deadline (branch 3, with probability (1-p)p): Analogous to situation 2.
4. No agent faces an unrealistic deadline (branch 4, with probability (1-p)²).

However, due to agents’ lack of information, their single decision for their information set (i.e., if their deadline is tight) cannot depend on the other agent’s deadline situation.

To deepen the understanding of the model in view of interpreting experimental data, in the supporting information S1 Appendix we extend Austin’s analysis (1) by classifying the game depending on its parameters into three types, (2) by exploring risk-averse agents, and (3) by analyzing repeated play of his one-shot game.

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Fig 1. Extensive-form representation of Austin’s game.

Austin’s hypothesis on software quality is as follows. Agents will play according to the unique NEQ (L, L) for p < p_crit. For p ≥ p_crit, agents will play the payoff-dominant NEQ (H, H). Thus, conditional on having an unrealistic deadline, for low p all decisions will be L and for high p all decisions will be H, that is, the percentage of H-decisions increases in p.

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This predicts the following rate of shortcut taking (r). Define r as the number of shortcuts taken divided by the total number of deadline situations, and assume the game is played k times by n pairs of agents. The rate r is zero if there are no deadlines and increases equally with the probability of an unrealistic deadline p for p ≤ p_crit, because every agent will seize any unrealistic deadline she faces (play L), such that r = 2npk/(2nk) = p. At p = p_crit, the rate abruptly drops to zero because every agent solely plays H for p ≥ p_crit. Thus, r and p have a non-monotonic relationship.

Austin’s prediction has not been empirically tested so far. Therefore, we test for the first time in two experiments Austin’s hypothesis that agents play (L, L) in case this is the unique NEQ and play (H, H) when it is the payoff-dominant NEQ.

General setup

Our research includes two experiments. The first is designed to analyze the effects on time pressure on software quality in an abstract setting (i.e., the software development setting was described to participants, who then decided between abstract options facing purely financial tradeoffs; see Study 1). This is the common approach in laboratory experiments on behavior in economics and psychological studies. The second experiment focuses on the actual behavior and includes real programming tasks that the participants were requested to solve under conditions of (un-)realistic deadlines (see Study 2).

The goal of the experiments is to empirically test Austin’s predictions and thus to test the following hypothesis: “For p>0, higher probabilities of unrealistic deadlines reduce shortcut taking in software projects when exceeding a critical threshold compared to lower probabilities.” Our experiments are based on the relation between the probability p of being assigned a task with an unrealistic deadline (independent variable) and either the decision (Study 1) or the action (Study 2) taken by a software developer (dependent variable)–either taking a shortcut (L) or reporting a delay (H) if facing an unrealistic deadline. Both variables are categorical. While the dependent variable can be either L or H, p as the probability of an unrealistic deadline, for which the impact on the decision of strategy (H or L) is to be investigated, depends on the parameters Q₁, Q₂, and C.

In designing and planning our experiments, we followed the guidelines of Wohlin, Runeson [33]. The experimental sessions lasted about 90 minutes. Participants for both experiments were recruited among students of an undergraduate course on software development at a German university. All students were close to graduation and finishing the course. During the software development course, the students had to develop a software product jointly with customers from industry. This experience of the students ensured a high-quality pool of participants. This is also evidenced by the self-reported skills in programming (mean 3.18, st.d. 0.81 on a five-point Likert scale) and HTML development (mean 2.57, st.d. 1.25). Although our participants have both project management and programming experience gained in the software development course, we acknowledge that they do not have the longstanding management, programming, and testing experience of professionals in industry. However, in software development, students have been shown to be suitable as representatives for professionals and that their results are generalizable [34]. This is especially the case if students are last-year software engineering students and if performed tasks are in the context of maturity and understanding of dependencies and relationships in software development [35]. Our setting fulfills both conditions. Moreover, students are easy to recruit and can be remunerated with a smaller amount compared to professionals [34, 35]. Each subject participated in only one treatment and in only one of the experiments (between-subject design, random assignment). For participating in our experiments, the students were asked to sign the written informed consent that is used for all experiments conducted in the laboratory at the corresponding author’s university. The consent defines the ground rules such as being paid as well as guaranteeing the anonymity of the participants and data privacy issues. All participants gave written consent.

Austin implies a scenario in which two agents interact with each other for several rounds, where one round represents one deadline situation. The payoff of subjects is important in order to incentivize proper decision-making. Each subject was awarded eight points per round, which represented the earnings in the given round. The realized penalties 0, Q₁, Q₂, C, or C+Q₁ were then subtracted accordingly. The maximum penalty to be obtained in a single round was C+Q₁ = 6; therefore, eight points were chosen as the earning amount in order to assure positive payoffs. Payoff points were converted into Euro after the sessions to determine the final earnings of the participants.

In both experiments, we established four treatments (p, Q₁, Q₂, C), which only varied in p. In a test session (with students working at the authors’ department and not being involved in the actual experiment), with parameters (Q₁, Q₂, C) = (1, 2, 3) implying p_crit = 2/3 and two treatments with p = 0.4 and 0.9 both did not lead to coordination to H. This observation is in line with the analysis (see S1 Appendix), showing that risk aversion might increase the actual value of p_crit. Therefore, (Q₁, Q₂, C) = (2, 3, 4) were chosen in order to realize a lower p_crit = 0.5. The selected values of Q₁ = 2, Q₂ = 3, and C = 4 define p_ps = 0. As a result, for p < p_crit the game can be categorized as a Prisoner’s Dilemma and for p ≥ p_crit as a Stag Hunt coordination game (see S1 Appendix for the types of games and, e.g., Andreoni and Miller [36], Cooper, DeJong [37], and dal Bo [38] for experiments on the Prisoner’s Dilemma, and Devetag and Ortmann [39] for an overview of experiments on the Stag Hunt).

For our subsequent data analyses, we used IBM SPSS Statistics 24 and R version 3.4.2 with the package “mlogit”.

Study 1

Design.

The subjects were requested to make strategic decisions concerning whether they would choose high or low quality for given (un)realistic deadline situations. The experiment’s setting, instructions, procedure, and control questions are given in supporting information S2 Appendix. The study was conducted using the z-Tree software package [Zurich Toolbox for Readymade Economic Experiments; cf. 40]. We selected p = 0.4, 0.6, 0.75, and 0.9, which predominantly lay above p_crit = 0.5. While p = 0.4 and 0.6 are chosen to lie evenly and closely around p_crit, p = 0.75 and p = 0.9 are chosen to make coordination to H more attractive if p = 0.6 (which should yield H, as predicted by Austin) is not high enough (based on insights from an early test session).

We sought 12 participants per treatment (i.e., 48 participants in total), of which nine were female and 39 were male. We considered this sample adequate since 44 participants would be sufficient for a power level of 0.8. Since our study relies on the relation between two categorical variables, this consideration is based on a goodness-of-fit test for the Pearson chi-square tests, calculated with the software tool G*Power (alpha = 0.05; degrees of freedom = 3; effect size = 0.5). The effect size is a modest estimate compared to effect sizes in software engineering experiments in general [41] and other software testing experiments in particular [1].

Subjects were randomly assigned to treatment groups by drawing a number from an urn and then randomly matched to groups of two subjects within the treatment for the duration of 24 rounds (i.e., a partner design in a repeated game is set up). Afterwards, new groups were randomly generated, assuring a different workmate for each participant, and another 24 rounds were implemented. The number of 24 rounds surfaced from the pretest and was chosen as a compromise between allowing the low-p treatment group (in which the occurrence probability of an unrealistic deadline is only 40%) enough rounds for coordination and preventing the high-p treatment groups from getting bored, which might have resulted in inattentive decisions. After each round, participants learned whether their other group member provided low or high quality but did not learn whether that group member had a realistic or an unrealistic deadline.

Participants were paid a show-up fee of 2.50 Euro plus the amount earned according to their decisions in the experiment. In order to determine the conversion factor from points to Euro, the expected numbers of points were roughly estimated and a conversion factor of 1/20 Euro/points was chosen in order to yield an average payoff of 10 Euro per hour. Based on the payoffs given in Table 1, consistent play of (L, L) [(H, H)] leads to a player’s total expected points of (314, 286, 267, 251) [(338, 338, 348, 367)] for p = 0.4, 0.6, 0.75, and 0.9. (e.g., if both players in the 0.6 treatment choose L in each round, the expected payoff is calculated as follows: number of rounds*(constant earning—expected penalty) = 48*(8-(p²Q₂+2p(1-p)Q₁)) = 48*(8-(0.6²*3+2*0.6*(1–0.6)*2)) = 286.08). Assuming L is unlikely to be chosen for high p, and further assuming that choosing H should result in higher penalties through imperfect coordination, a constant conversion factor was presumed to yield roughly the same payoff for all treatment groups. The participants’ final payment was between 14.60 Euro and 20.80 Euro with a mean of 18.26 Euro (median 18.20 Euro).

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Table 1. Normal form of Austin’s game.

https://doi.org/10.1371/journal.pone.0245599.t001

Descriptive statistics.

Fig 2 shows the distribution of realistic and unrealistic deadline situations for the four treatments with varying values of p (total number of deadline situations per treatment n = 576). For unrealistic deadline situations, we further differentiate between the decisions made by the subjects concerning high (H) or low (L) software quality. The decreasing number of realistic deadline situations (from left to right) is a direct consequence of the increasing probability p of software developers being assigned an unrealistic deadline (from p = 0.4 to p = 0.9). As can be seen along the increasing probability of being confronted with an unrealistic deadline, the number of decisions for H increases while the one for L decreases. However, there is one exception: the transition from p = 0.6 to p = 0.75 (we discuss this non-linear relationship in the following section).

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Fig 2. Distribution of unrealistic deadline (high-quality and low-quality decisions) and realistic deadline situations per treatment (n = 576).

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Fig 3 illustrates the share of H decisions (number of H decisions divided by the number of unrealistic deadlines) per round for each treatment. A clear trend of an increasing share of H decisions along with p is found. For p = 0.4, the decisions are mostly L. In contrast, we observe predominantly H decisions for p = 0.9. In particular, the observation of both subjects playing H is at about 60% for rounds 1–24 (henceforth denoted as part 1) but increases to almost 100% in rounds 25–48 (henceforth denoted as part 2), that is, after regrouping. Strong fluctuations are observed especially for p = 0.4 and 0.6, resulting from averaging over subjects. Fig 4 shows the respective box plots. A clear trend of increasing H decisions along with p is visible according to the median values and variances. Surprisingly, from p = 0.6 to p = 0.75, the median values concerning the share of high-quality decisions remain nearly constant. Only at p = 0.9, H is the predominant decision. Finally, Fig 5 shows the relation between the probability of unrealistic deadlines (p) and the rate of shortcut taking (r, i.e., number of shortcuts taken divided by the total number of deadline situations). We show the relation for both the total experiment and part 2 (i.e., the participants already being familiar with the game).

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Fig 3. Share of high-quality decisions (unrealistic deadlines only) depending on the probability of facing an unrealistic deadline.

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Fig 4. High-quality rate plotted for the four treatments.

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Fig 5. Relation between probability of unrealistic deadlines (p) and rate of shortcut taking (r).

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Tests of significance.

For the analysis, we considered the cases in which participants faced unrealistic deadlines, that is, the cases in which the participants needed to decide between high (H) and low quality (L). Since our data includes two categorical variables, we conducted a Pearson chi-square test [42] to test whether the differences between the treatments of varying probabilities of unrealistic deadlines are significant [43]. Since each subject participated in one of the treatments only, we can ensure the independence of residuals. With the lowest expected frequency count amounting to 92.3 (see Table 2), our data fulfills the criterion of expected frequencies.

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Table 2. Contingency table for chi-square test (SPSS output).

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There is a significant association [Fisher’s exact test; 44] between the probability of software developers being confronted with an unrealistic deadline and the rate of shortcut taking (χ² (3) = 202.192, p < 0.000, Cramer’s V = 0.36). Based on the odds ratio (i.e., effect size), the odds of choosing H were 11.34 times higher in case of p = 0.9 compared to p = 0.4. All odds ratios are shown in Table 3. The general trend is: the higher the probability of being confronted with an unrealistic deadline the more likely do subjects decide to deliver high quality (in cases they are actually confronted with an unrealistic deadline).

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Table 3. Odds ratios among the four different treatments.

https://doi.org/10.1371/journal.pone.0245599.t003

To confirm our findings, we further analyzed the data by considering the share of high-quality decisions of her high/low quality decisions (i.e., we interpreted our categorical variable as the percentage of each participant choosing high quality). Based on the data for each group of two participants in parts 1 and 2 of our experiment, we used the Jonckheere-Terpstra test [45, 46] to assess whether a trend in the data is apparent. Since we presume that effects needed some rounds to level off, we used the data from rounds 13–24 (part 1) and 37–48 (part 2) for this assessment. The result shows a trend with significance for part 1 (p < 0.10, n = 24, df = 3) and part 2 (p < 0.01, n = 24, df = 3). (When considering all rounds, the p-values amount to 0.105 for part 1 and 0.023 for part 2.) This indicates that the high-quality decisions as well as the high-quality collaboration in teams increase along with p.

Study 2

Descriptive statistics.

Fig 6 shows the distribution of realistic and unrealistic deadline situations for the four treatments with varying values of p. For unrealistic deadline situations, we further differentiate between the decisions made by the subjects concerning high (H) or low (L) software quality. The decreasing number of realistic deadline situations (from left to right) is a direct consequence of the increasing probability p of software developers being assigned an unrealistic deadline (from p = 0.4 to p = 0.9). Along the increasing probability of being confronted with an unrealistic deadline, the number of decisions for H increases while the one for L does not decrease.

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Fig 6. Distribution of unrealistic deadline (high-quality and low-quality decisions) and realistic deadline situations per treatment (n = 352, *n = 308).

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Fig 7 illustrates the share of H decisions (number of H decisions divided by the number of unrealistic deadlines) per round for each treatment. Fluctuations are observed for all four treatments. Fig 8 shows the respective box plots. A trend of increasing H decisions along with p is visible according to the median values. Surprisingly, from p = 0.5 to p = 0.7, the median values concerning the share of high-quality decisions remain constant. Finally, Fig 9 shows the relation between the probability of unrealistic deadlines (p) and the rate of shortcut taking (r, i.e., number of shortcuts taken divided by the total number of deadline situations).

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Fig 7. Share of high-quality decisions (unrealistic deadlines only) depending on the probability of facing an unrealistic deadline.

https://doi.org/10.1371/journal.pone.0245599.g007

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Fig 8. High-quality rate plotted for the four treatments.

https://doi.org/10.1371/journal.pone.0245599.g008

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Fig 9. Relation between probability of unrealistic deadlines (p) and rate of shortcut taking (r).

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Tests of significance

Again, a Pearson chi-square test [42] was used to test whether the differences between the treatments of varying probabilities of unrealistic deadlines are significant [43]. With the lowest expected frequency count amounting to 68.8 (see Table 4), our data fulfills the criterion of expected frequencies. There is a significant association [Fisher’s exact test; 44] between the probability of software developers being confronted with an unrealistic deadline and the rate of shortcut taking (χ² (3) = 18.788, p < 0.000, Cramer’s V = 0.15). The odds of choosing H were 2.06 times higher in case of p = 0.9 compared to p = 0.4. All odds ratios are shown in Table 5. The general trend is: the higher the probability of being confronted with an unrealistic deadline, the more likely do subjects decide to deliver high quality (in cases they are actually confronted with an unrealistic deadline).

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Table 4. Contingency table for chi-square test (SPSS output).

https://doi.org/10.1371/journal.pone.0245599.t004

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Table 5. Odds ratios among the four different treatments.

https://doi.org/10.1371/journal.pone.0245599.t005

To confirm our findings, we further analyzed the data by considering the number of high-quality decisions proportional to all decisions (i.e., we interpreted our categorical variable as the percentage of each participant choosing high quality). However, the Jonckheere-Terpstra test [45, 46] based on the data for each group of two participants showed no significance (p = 0.173).

In this study, we also included a survey as the last part of our experimental design to further connect our experimental results and the game-theoretical predictions by Austin [10]. Personal traits from the personal style inventory [48, 49] that have been shown to be relevant in software development before [50] did not lead to any insights. Similarly, our logistic regression analysis [i.e., the suitable test for a categorical dependent variable and categorical as well as continuous independent variables; see 43] did not yield a significant predictor as regards the effect of control variables programming experience, HTML experience, sex, or age.

Threats to validity

It is worthy of discussion whether a probability of being assigned an unrealistic deadline and the decision of shortcuts are suitable means to represent the relation between time pressure and software quality in its entirety. For instance, quality is also affected by programming errors rather than solely by taking shortcuts that can indirectly lead to errors. However, since we aim to evaluate the game-theoretical prediction concerning time pressure and software quality as proposed by Austin [10], these limitations can be seen as minor threats to construct validity.

Having professional software developers participate in the experiment instead of students would have increased the external validity, but professional developers’ time restrictions and payment requirements make it unlikely to find a sufficient number of participants. The framing design of the experiment ensures that external validity is not threatened by differences in specific programming skills between students and professionals (see Section “Experimental Studies, General Setup” for a detailed discussion.)

Internal validity is threatened due to the framing design of our experiment. While subjects in the first study were not confronted with a real programming task, the programming task in the second study were minor ones and limited to a markup language. However, the actual decision of software developers as well as our experimental subjects (whether to report a delay or take a shortcut in case of an unrealistic deadline) is made based on the given conditions (p, Q₁, Q₂, C, and the assigned deadline situation). Therefore, this decision mostly takes place before the actual implementation of the assigned task is carried out. We are thus confident to have constructed a sufficient representation of a real-live setting. Furthermore, we believe that framing was the only viable option in our setting. Our first study uses the clearest representation of the developers’ strategic situation. Our second study gives up some experimental control over the agents’ costs (e.g., for the effort of conducting the task vs. taking a shortcut) in order to introduce a real programming task and thus to gain internal validity.

Theoretical implications

Our study continues research on the impact of time pressure on performance since it was motivated by the inconclusiveness concerning the dyadic relationship between time pressure and performance in general and set out to find evidence to support or refute the game-theoretical prediction by Austin [10] concerning the impact of deadlines on software quality in particular. Austin’s model comes along with the hypothesis that the rate of shortcuts taken in software development projects can be reduced by setting deadlines aggressively (i.e., the rate of unrealistic deadlines p should be larger than a critical value p_crit). The experiment parameters were set to imply a p_crit of 0.5. In our analysis, we found that taking shortcuts is the prevailing decision for p = 0.4 in both experiments. In contrast, shortcuts were almost completely eliminated for p = 0.9 in the first experiment (see Figs 3 and 5) and were shown to decrease in the second experiment (i.e., the rates of shortcut taking for p > p_crit are lower than for p < = p_crit; see Fig 9). For p between 0.5 and 1, Austin also stipulates a shortcut rate of zero. In the experiments, however, we found no clear pattern. There were subjects that were mostly indecisive whether to take shortcuts. Our results are thereby in line with studies suggesting that different levels of time pressure have different kinds of impact, that is, a non-linear relationship between time pressure and performance [6]. According to Austin [10], there should be a linear increase of shortcuts taken for probabilities lower than p_crit and no shortcut taking for probabilities equal to or greater than p_crit. In general, we find a trend that shows a decreasing shortcut-taking rate along the increase of p (i.e., the rates of shortcut taking for p > p_crit are below the rates of shortcut taking for p < = p_crit). Shortcut taking frequently takes place for p = 0.4 and can be significantly decreased for p = 0.9. The general trend is that higher probabilities of unrealistic deadlines are linked to higher software quality.

Limitations and directions for future research

We see several design decisions that are worth a discussion concerning our experimental setting. First, the game-theoretical model and our experimental setting presume p to be equal and independent for both developers. Thus, the skills of the developers are likely to be decisive for whether a deadline can be met. Whereas our experiment is designed as a framing one, a real-life evaluation in the field within a software development company might lead to different insights. Software developers might use shortcuts in cases of realistic deadlines as well or increase capacity (e.g., night shifts) to deliver high quality when facing unrealistic deadlines.

Software developers might be involved in effort estimation [51] and thus have a notion of the actual deadlines. This knowledge is likely to mitigate the effect of time pressure. Moreover, close collaboration and intensive communication among team members [52, 53] is relevant, particularly in case of agile development approaches [54–56], which are designed for intensive interaction, thus enabling an extensive social exchange. Accordingly, we suggest that the scenario described by Austin [10] is less likely to apply to such more recent development approaches. Austin’s theoretical results and our empirical evaluation are more likely to apply to more traditional approaches of software development. That said, the model and our findings are not irrelevant for industrial practice–many companies mix and match development approaches and tailor methods to fit their particular needs [57]. Thus, for example, you see stage-gate models (i.e., using phased delivery deadlines for releases) blended with agile approaches in industrial practice [58].

The findings show that continuously having aggressive deadlines in software development projects might indeed be effective for improving the quality of the products. Whereas reporting delays in software projects becomes destigmatized, shortcut taking only completely vanishes at p = 0.9 in our first study. Such a high rate of unrealistic deadlines might be unworkable. For lower values of p_crit to be effective, quality and career penalties could be altered. This could be done for example by communicating a corporate identity to the software developers which particularly values the quality of the software products [15] and makes employees profit from high quality rather than from meeting deadlines.

Previous research has shown that time pressure decreases decision quality [3, 13, 59], thus contradicting Austin’s analytical results. However, studies likewise show that decision quality increases if pressure is generated by work-pace dependent incentives (i.e., higher payoff if faster) [3]. In this context, it is important to consider how time pressure is created. Our experiment was designed in a way that software developers face an unrealistic deadline. The approach of allocating insufficient time [27] in general is problematic because different individuals work at a different pace. Since we used a framing for our experiment, this problem did not arise in our setting.

While we were not able to determine the concrete value of p_crit due to our study design, we falsified the theoretical value for p_crit, which according to Austin’s model and our parameter choices should have been 0.5. We call upon further research to determine the actual critical value of p and analyze reasons for the deviation between theory and empirical evidence. In addition, researchers should address settings with more than two software developers because team structure can have an impact on perceived time pressure [15]. Additionally, future research should consider additional options for designing the experiment such as varying incentive mechanisms and different punishment levels for not meeting the deadline.

Managerial implications

Our study provides implications that are relevant to the management of software projects. First, traditional ways of deadline setting should be reconsidered. It is likely that shortcuts taken by software developers can be reduced if unrealistic deadlines are seen as an ever-present condition in the organization. While other recommendations suggest that time pressure should not exceed a specific level to avoid negative consequences like distraction, our results are in favor of a constantly high level of time pressure. However, our findings should not be seen as normative. We have analyzed how given levels of time pressure affect shortcut taking in software development projects. The actual scarcity level of the available time in relation to the time required to accomplish a task is subject to further investigations.

Second, our study suggests that the value of p_crit that leads to the desired effect of software developers choosing high quality over shortcuts is rather high. Consequently, the consonance of such a high level of time pressure with organizations’ ethical principles and work culture needs to be examined. Once developers become aware that deadlines are most likely unrealistic, the role of time pressure as a motivator [e.g., 24] might vanish. Thus, the actual level of time pressure applied in organizations needs careful consideration in the given context. In this regard, the experience of the developers needs to be considered as well.

Finally, organizations need to carefully consider whether they follow the deadline-setting policy advocated by the game-theoretical analysis of Austin (2001) or choose to seek a more transparent approach, particularly in agile development environments. Software development is a social activity and managers are unlikely to prevent their developers from discussing deadlines and the respective policies. In particular, agile development approaches benefit from the interaction of all project members (e.g., in daily scrums).