Monitoring and controlling organizational and team performance

The results demonstrate that change encompasses improving quality control of products and processes, which involves the performance of nonhuman and human actants.

Improving quality control of assembly lines: there were no consistently updated data on the productivity and quality of the lines; the heads of the lines possessed the production data, but used them subjectively and unsystematically and did not employ them to monitor production processes; panels were installed on every line that allow the heads of line to register, every hour, data on the parts produced and the percentage of nonconforming components.

Improving product quality control: by replacing human operators or compensating for their operational uncertainty, new machines improved production and quality control, increased the reliability of the detection of nonconformities, and informed human operators of the operations that must be executed to correct production processes.

Improving control of the human actants' movements in the plant: different colored uniforms were assigned to different functional teams to monitor their movements in the plant, which reduced unnecessary and unjustifiable movements. The heads of the production department confirmed these actions and agreed on their effects on the production process.

The results suggest the existence of a relationship among monitoring, information, and performance, which is confirmed by the literature. Monitoring business performance focuses on the comparison between what is foreseen and accomplished and between the plan and results (Lind, 2015: 7), which requires performance measures to monitor past performance and stimulate future actions (Neely et al., 2005: 1256). The analysis, evaluation, and improvement of process performance are based on information systems (Edosomwan, 1995: 196). Quality monitoring leads to corrective actions (Feigenbaum, 1991: 107) and to preventive actions (Crosby, 1979: 82), and it involves continuous assessments of performance with respect to both effectiveness and efficiency (Goetsch and Davis, 2012: 69). Productivity and quality are connected through the goal-setting process, which determines targets, resources, and measures of performance and defines how quality and productivity are jointly managed (Edosomwan, 1995: 66). Thus, the availability of the necessary production data makes quality monitoring possible, which fosters the improvement of quality and productivity performance.

The results indicate that quality control has become more effective and quality performance has been substantially improved, which accords with the reviewed literature. Quality control consists of the confirmation of a product's ability to meet specified requirements (Wealleans, 2005: 17) or to meet standards (Juran and Gryna, 1980: 3), involving the integration of quality development, quality maintenance, and quality improvement (Feigenbaum, 1991: 6), and to pursue problem solving to ensure continuous improvement (Ross and Perry, 1999: 167). Hence, quality control is critical to the continuous improvement of process performance and product compliance, and thus, it may be the most important technology in the production flow.

The results also emphasize the importance of regulating human actants' activity, which is confirmed by the literature on dress codes. The significance of clothing as a device for identification is related to the control context (Joseph, 1986: 75), and the existence of dress codes submits agents to conduct patterns imposed by an external authority (Lurie, 1981: 18). Hence, the meaning of clothes affects agency. It is also a resource for materializing organizational identity based on the consistency of labor values, and it affects the compliance with organizational standards of behavior (Pratt and Rafaeli, 1997: 865-868; Rafaeli and Pratt, 1993: 40-47). Hence, clothing is a set of signs that conveys meanings that communicate identity and facilitate control.

These theoretical considerations and results allow us to conclude that (1) improving quality and productivity levels depends on improving quality control of the performance of nonhuman actants and products and on improving performance control of human actants and (2) the effectiveness of control processes depends on nonhuman actants' agency, which improves data validity and process traceability, the diversity of signs present in the work environment, and the accuracy and updating of performance measures. These findings allow us to conclude that H4 (increasing the number of nonhuman actants improves both the certainty of a communication process and the conformity of human practices) and H13 (the improvement of technologies implies an increase in hybrid relationships, which improves communication performance) are confirmed.

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