Table 1.

Summary of research hypotheses.

	Description		Supporting Evidence from Existing Literature
Self-Determination	H1	The level of employees' motivation for energy-saving at work, as expressed through their self-determination, will affect the strength of their behavioral intention towards conserving energy (H1a), self-reported energy-saving behavior (H1b), and energy-saving habit at work (H1c).	•Higher levels of self-determination are related to long-term maintenance of PEBs [77]
			•Autonomous motivation is critical in fostering PEBs [78]
			•SDT should be used as a guide to fostering environmental motivation [70]
			•SDT has been used to explain self-reported household energy-saving behaviors [71, 72]
Planned Bahavior	H2	The level of employees' subjective norms (i), attitude (ii), felt personal impact (iii), and felt collective impact (iv), with regards to energy-saving at work, will positively affect the strength of their behavioral intention towards conserving energy (H2a), which will in turn positively affect the level of their self-reported energy-saving behavior (H2b), and energy-saving habit (H2c) at work.	•Planned Behavior (TPB) can explain energy use intentions and behavior [80,81]
			•Energy consumption behavior is positively influenced by consumers' attitude [155]
			•Planned Behavior (TPB) has been employed to explain PEB at work (Cordano & Freeze, 2000; Lülfs & Hahn, 2013, 2014; Martín-Peña et al., 2010)
			•Planned Behavior (TPB), extended with perceived habit, has been used to explain office energy-saving behaviors [19]
Personal Norms	H3	The level of strength of employees' personal norms towards conserving energy at work, will positively affect the strength of their behavioral intention towards conserving energy (H3a), self-reported energy-saving behavior (H3b), and energy-saving habit (H3c) at work.	•Personal PEB norms are connected to actual conservation behaviors [20]
			•Personal norms have been employed to explain workplace PEB [87]
			•Personal norms have been employed to explain energy-saving behavior (Fornara et al., 2016; Ibtissem, 2010; Steg et al., 2005)
			•Employees' personal energy-saving norms can be leveraged to affect their intention, and actual energy-saving behavior [91]
Personal Factors	H4	Employees' level of engagement as expressed by vigor (i), age (ii), female gender (iii), and having children (iv), will positively affect the level of their behavioral intention towards conserving energy (H4a), self-reported energy-saving behavior (H4b), and energy-saving habit (H4c) at work.	•Vigor is characterized by willingness to invest effort, and persistence, at work [93]
			•Vigor has been correlated with increased organizational citizenship behaviors (OCBs) and decreased deviance [94]
			•PEB tends to increase with age, and women tend to have stronger environmental attitudes, concern and behaviors than men across age (Gifford & Nilsson, 2014; Murtagh et al., 2013)
			•Residential users with children exhibit increased motivation to save energy [100]
Behavioral Intervention	H5	Timely & personally relevant feedback distributed by an IoT-enabled, gamified IS solution will lead to: an increase in employees' motivation to conserve energy at work – as expressed through the Self-Determination (H5a), Planned Behavior (H5b) and Personal Norms (H5c) models	•Utilizing feedback has been suggested towards engaging employees in energy saving behavior at work (Lo et al., 2012; Matthies et al., 2011)
			•Real-time consumption feedback can help make “invisible energy visible”, and act as a reference point for the self-evaluation and adjustment of energy behavior (Boomsma et al., 2016; Burgess & Nye, 2008)
			•Employees' personal energy-saving norms can be activated by providing information and training [92]
			•SDT has been suggested towards effectively designing and analyzing gamified experiences (Aparicio et al., 2012; Hamari et al., 2014; Kappen & Nacke, 2014; Sailer et al., 2013)
			•TPB has been used to explain the behavioral effects of gamification in various contexts (Bittner & Shipper, 2014; Hamari & Koivisto, 2013, 2015)
			•VBN has been employed, in order to explain gamifications' effects on PEB and energy conservation behavior (Bittner & Shipper, 2014; Kotsopoulos et al., 2016)
	H6	The increase in employees' motivation to conserve energy will in turn lead to positive actual energy consumption behavior change – as expressed through the change in the participating employees' energy-saving intention (H6a), self-reported behaviour (H6b), and habit (H6c)	•Digital technologies facilitate self-monitoring of undesired behaviours by accurately and timely delivering feedback, thus disrupting existing habits in an effective way [105]
			•Gamification can be used to encourage behavior change, increase and sustain employee participation and compliance in specific goals, motivation, engagement, performance and productivity within an enterprise, break existing habits, update them with new ones, and lead to behavior change (Blohm & Leimeister, 2013; Pickard, 2015; Robson et al., 2015; Seaborn & Fels, 2015; Webb, 2013)
	H7	The increase in employees' energy-saving intention, self-reported behaviour, and habit will lead to actual energy savings (H7), compared to baseline consumption before the intervention	•Up to 15% energy savings [60], and 7.4% on average [13], have been reported from behavioral interventions employing feedback
			•When provided by an IS and combined with incentivization, feedback has been especially effective in promoting energy conservation behavior [29]
			•Gamification has been employed towards increasing motivation, knowledge, behavior and attitude towards energy conservation and promoting real-world energy saving behaviors in the range of 3–6%, with more than 10% achievable (Bourazeri & Pitt, 2013; Brewer et al., 2013; Fijnheer et al., 2016; Fijnheer & Van Oostendorp, 2016; Geelen et al., 2012; Grossberg et al., 2015; Knol & De Vries, 2011; Orland et al., 2014; Reeves et al., 2012)
			•Energy efficiency games, have successfully been deployed in workplace environments (Cool Choices, 2019; Energic, 2019; Grossberg et al., 2015; WeSpire, 2019)