Note(s): 1. Management Control Effectiveness (MCEffectiveness) was measured using a scale validated by Demartini and Otley (2020), capturing respondents' overall assessment of how effectively the management control system provides information to: (1) support organizational goal achievement, (2) support operational decision-making, and (3) enable flexibility and adaptability. Responses were measured on a seven-point Likert scale ranging from 1 (“Extremely unsatisfactory”) to 7 (“Extremely satisfactory”). Consistent with its conceptualization, we modeled MCEffectiveness as a reflective construct, with indicators representing different manifestations of a MC system's effectiveness rather than distinct components jointly defining it; accordingly, indicators of convergent validity and internal consistency reliability are reported (Hair et al., 2017, 2021)

2. Responsiveness between Annual Budget and Forecast (Responsiveness) was measured using a four-item scale adapted from Demartini and Otley (2020) that captures four dimensions of adaptive interaction between control mechanisms: strength of influence, directness, consistency, and dependence. Respondents were asked: “How would you rate the degree of interdependence between the “annual budget” and the “forecast” in your company with respect to the following dimensions?” Responses ranged from 1 (“no responsiveness”), 4 (“weak responsiveness”), and to 7 (“strong responsiveness”). In line with Demartini and Otley's (2020) conceptualization of responsiveness as a design feature, this construct was operationalized as a composite index. The four dimensions were equally weighted, reflecting the view that responsiveness emerges from the combined presence of multiple and non-hierarchical characteristics. Forecast operational macro-functions were measured using an adapted version of the scale originally developed by Sivabalan et al. (2009) for budgeting and rolling forecasts, and subsequently used by Bhimani et al. (2018). Consistent with prior literature, we considered three broad macro-functions of forecast use: (1) planning, including formulation of action plans, coordination of resources, management of production capacity, pricing decisions, and encouragement of innovative behavior; (2) control, capturing the use of forecasts to track the execution—cost control and monitoring by top management; and (3) performance evaluation, capturing the use of forecasts to analyze the achievement of targets for individuals and units, namely business-unit and managerial performance evaluation. Respondents were asked: “Currently, to what extent does the company use forecasts or reforecasts for the following functions?” Responses were captured on a seven-point scale ranging from 1 = “does not use” to 7 = “to a great extent.” These constructs were modeled as formative, as each function represents a distinct and non-substitutable role that jointly defines how forecasting is used within the organization. Our understanding is that higher use of one function does not imply higher use of another, and omitting any function would alter the conceptual meaning of forecast use. Therefore, although some indicator weights are relatively low, they were retained due to the statistical significance of their outer loadings, in line with recommendations for formative constructs

3. We included some control variables. First, Firm size (ESize) considering the number of employees: (1) between 50 employees and 249 are medium-sized firms; (2) above 250 are large-sized firms (being the base category). Second, Industry: manufacturing versus non-manufacturing (the base category, which included retail, wholesale, and service firms). Third, we control for Environmental hostility (Hostility) based on Green, Covin, & Slevin (2008), composed of six items with a scale from 1 to 7, with 1 = “Totally disagree” and 7 = “Totally agree”. In this paper, the convergent validity of the scale indicated three items (high bankruptcy rate, low customer loyalty, and low-profit margins)