Balancing Security and Regulation With Appreciative Inquiry to Foster Radical Innovation and Meaningful Work

• In Event: Business NOT as Usual: Unexpected Leadership Responses to Challenges and Threats

Short Description

Under the constant threat of cyber security concerns and evolving regulatory requirements, an energy utility company called on a high-tech firm consultant to lead an appreciative inquiry initiative designed to foster innovative responses. The case will be discussed along with the organizational performance outcomes and employee perceptions of work meaningfulness.

Detailed Abstract

Hampered by the shackles of stringent regulation and constant cyber security threat, a large energy (grid management) company struggles to attain optimal organizational performance and employee engagement. The organization engaged with consultants to help facilitate an Appreciative Inquiry (AI) Summit (Ludema, Whitney, Mohr, & Griffin, 2003) within the Information Technology (the largest) division of the company. AI is a strength-based, collaborative approach to build capacity and transform organizations through a shared image of their positive potential, by first discovering the very best in their shared experience (Barrett, Fry, & Wittockx, 2005). Offering AI as an alternative to traditional problem-solving approaches, Cooperrider (1990) invites us to consider the relationship between positive imagery and positive action, suggesting that organizations are affirmative human systems subject to the heliotropic effect. In contrast to the deficit imagery and limited perspective a typical problem-solving mindset evokes, positive images of the future generate an affirmative mindset that allows people to connect more readily to the positive aspects of the past, to recognize the positives of the present, and to see new potential in the future (Cooperrider, 1990). This catalyzes an affirmative emotional environment that includes increased hope, care, joy, optimism, altruism, and passion. As an explicit extension of this list, this also makes the opportunity ripe for participants to cultivate meaningfulness within this context. For, as Cooperrider (1990) states, “appreciation not only draws our eye toward life, but stirs our feelings, excites our curiosity, and provides inspiration to the envisioning mind…the ultimate generative power for the construction of new values and images is the apprehension of that which has value” (p.19).

An additional area of exploration in this study is the impact of AI on cultivating a more meaningful work perspective for employees and the associated implications thereof. I share the assumption with Frankl (2006) and many others (e.g. Pratt & Ashforth, 2003; Wrzesniewski, 2003) that people generally desire meaningfulness in their lives. Given the significant amount of time that work occupies in many people’s lives, it is natural to presume that this desire for meaningfulness carries over into people’s work lives. Meaningfulness in the workplace refers to the level of purpose and significance that work holds for an individual, that may derive from intrinsic qualities of the work itself, the mission and values the work is designed to serve, and/or the relationships and the organizational community in which the work is embedded (Pratt & Ashforth, 2003). In this particular case, an executive within the organization cited concerns with employee engagement. While a separate construct not to be conflated with work meaningfulness, engagement has been demonstrated to show a strong positive correlation to work meaningfulness (May, Gilson, & Harter, 2004). One of the suppositions of this study is that more meaningful work experiences will correlate positively to stronger organizational performance outcomes.

This work is part of a broader mixed-methods comparative case study (Eisenhardt, 1989; Yin, 2014) exploring the impact of appreciative inquiry on work meaningfulness, employee engagement, and organizational performance. Four kinds of qualitative data: (1) responses to qualitative interviews (Rubin & Rubin, 2012), (2) participant observation field notes (Spradley, 1980), (3) outputs from the AI Summit processes, and (4) archival documents such as memos, content from company websites, and information about company performance are utilized. The purpose of collecting this qualitative data is to make sense of (Weick, 1995) and build plausible theory about (Charmaz, 2014; Corley & Gioia, 2011) what might explain the relationships between the AI Summit process and perceptions of work meaningfulness and measures of organizational performance. The outcomes of this particular case will be shared along with the emerging theory.

Participant

• Patrick Farran, SAS Institute